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Saikumar K, Rajesh V, Hasane Ahammad S K, Sai Krishna M, Sai Pranitha G i Ajay Kumar Reddy R. "Cab for Heart Diagnosis with RFO Artificial Intelligence Algorithm". International Journal of Research in Pharmaceutical Sciences 11, nr 1 (8.02.2020): 1199–205. http://dx.doi.org/10.26452/ijrps.v11i1.1958.
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CAB coronary artery Blockage is a main difficulty; this causes the heart problems. Different models are utilized to diagnosis the CAB as well as a category of heart problems. This work involves the heart surgery operations & very fast diagnosis. This research just requires the heart images i.e., CT-angiography images. Speed and real diagnosis are possible with technical Image processing (TIP) with the use of ML (Machine Learning) algorithm. With the help of RFO-DT (random forest optimization decision Trees) based, TIP and ML are used to detect the ROH (region of a Heart problem). Entire work consists of 2 stages; at first pre-processing is performed and the second stage DT is extracted, probability values are calculated performed the RFO-DT-ML model. Coronary artery is the main tissue in the heart, so it needs more concentration; normal scanning procedures are not sufficient, so CTA is necessary. In this, data sets are collated from the IEEE data house website. Conventional methods like GA, DE, and GWO are not efficient for heart functionality assessment for coronary artery disorders findings. If a patient with heart diseases have a problem for fast disease findings. So Fast and accurate disease finding models are required; therefore, this model i.e., RFO with AI, gives the best diagnosis results with accuracy. Finally, the design has been done and progressed by 4.766% OV, OF by using 6.5%, OT by 2.5%. These are efficient results.
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Chen, Kun-chih (Jimmy), Po-Chen Chien, Zi-Jie Gao i Chi-Hsun Wu. "A Fast ECG Diagnosis by Using Non-Uniform Spectral Analysis and the Artificial Neural Network". ACM Transactions on Computing for Healthcare 2, nr 3 (lipiec 2021): 1–21. http://dx.doi.org/10.1145/3453174.
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The electrocardiogram (ECG) has been proven as an efficient diagnostic tool to monitor the electrical activity of the heart and has become a widely used clinical approach to diagnose heart diseases. In a practical way, the ECG signal can be decomposed into P, Q, R, S, and T waves. Based on the information of the features in these waves, such as the amplitude and the interval between each wave, many types of heart diseases can be detected by using the neural network (NN)-based ECG analysis approach. However, because of a large amount of computing to preprocess the raw ECG signal, it is time consuming to analyze the ECG signal in the time domain. In addition, the non-linear ECG signal analysis worsens the difficulty to diagnose the ECG signal. To solve the problem, we propose a fast ECG diagnosis approach based on spectral analysis and the artificial neural network. Compared with the conventional time-domain approaches, the proposed approach analyzes the ECG signal only in the frequency domain. However, because most of the noises in the raw ECG signal belong to high-frequency signals, it is necessary to acquire more features in the low-frequency spectrum and fewer features in the high-frequency spectrum. Hence, a non-uniform feature extraction approach is proposed in this article. According to less data preprocessing in the frequency domain than the one in the time domain, the proposed approach not only reduces the total diagnosis latency but also reduces the computing power consumption of the ECG diagnosis. To verify the proposed approach, the well-known MIT-BIH arrhythmia database is involved in this work. The experimental results show that the proposed approach can reduce ECG diagnosis latency by 47% to 52% compared with conventional ECG analysis methods under similar diagnostic accuracy of heart diseases. In addition, because of less data preprocessing, the proposed approach can achieve lower area overhead by 22% to 29% and lower computing power consumption by 29% to 34% compared with the related works, which is proper for applying this approach to portable medical devices.
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Saikumar K i Rajesh V. "Coronary blockage of artery for Heart diagnosis with DT Artificial Intelligence Algorithm". International Journal of Research in Pharmaceutical Sciences 11, nr 1 (10.01.2020): 471–79. http://dx.doi.org/10.26452/ijrps.v11i1.1844.
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Coronary blockage of an artery (CBA) is a fundamental problem cause of heart attacks. There are different techniques used to diagnosis this CBA as like other category of heart diseases. In this research, open heart surgery operation and quick diagnosis have been analyzed. This CBA diagnosis & operation requires clear images of heart i.e., CTA pictures. Fast and reliable detections are possible with professional image processing techniques (IMT) with the help of Artificial intelligence algorithms (AIA). By the help of Decision Tree (DT) based IMT and “AIA” is used to find the region of heart image CBA diagnosis with a concentration of determination. Total work contains two stages; 1st is pre-processing means image processing training 2nd is decision step, in this extraction, and statistical calculations are performed using the DT-AIA model. Implementation has been achieved and progressed by using 4.766% OV, OF by using 6.5%, OT by means of 2.5%, AI with the aid of 0.21% these are very good results.
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Rao, Naseem, i Safdar Tanweer. "Fast Pattern Discovery in Healthcare Data Using Graphics Processors". Journal of Drug Delivery and Therapeutics 9, nr 1-s (21.02.2019): 358–60. http://dx.doi.org/10.22270/jddt.v9i1-s.2446.
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The mobile medical diagnosis and health monitoring system helps in managing the various chronic diseases like asthma, blood pressure and heart diseases etc. in consultation with the remotely available physicians by initiating the emergency call automatically on the physician’s mobile phone and providing the on-line vital medical parameters captured by the body area sensor network of the patient. We observed that a GPU based solution can outperform a CPU based solution by more than 30% in terms of speed up, while giving same accuracy of results, divided among healthy, normal and unhealthy patients. Finally, key parameter to model our health care data likestandard deviations of {1, 0.5, 0.5}, means of {(1, 1), (0, 0), (-1,-1)} are used to study healthy persons and unhealthy patients.
Keywords: Healthcare ; GPU; EEG; PCG; datastructure
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Abdou, Abdoul-Dalibou, Ndeye Fatou Ngom i Oumar Niang. "Arrhythmias Prediction Using an Hybrid Model Based on Convolutional Neural Network and Nonlinear Regression". International Journal of Computational Intelligence and Applications 19, nr 03 (wrzesień 2020): 2050024. http://dx.doi.org/10.1142/s1469026820500248.
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In biomedical signal processing, artificial intelligence techniques are used for identifying and extracting relevant information. However, it lacks effective solutions based on machine learning for the prediction of cardiac arrhythmias. The heart diseases diagnosis rests essentially on the analysis of various properties of ECG signal. The arrhythmia is one of the most common heart diseases. A cardiac arrhythmia is a disturbance of the heart rhythm. It occurs when the heart beats too slowly, too fast or anarchically, with no apparent cause. The diagnosis of cardiac arrhythmias is based on the analysis of the ECG properties, especially, the durations (P, QRS, T), the amplitudes (P, Q, R, S, T), the intervals (PQ, QT, RR), the cardiac frequency and the rhythm. In this paper we propose a system of arrhythmias diagnosis assistance based on the analysis of the temporal and frequential properties of the ECG signal. After the features extraction step, the ECG properties are then used as input for a convolutional neural network to detect and classify the arrhythmias. Finally, the classification results are used to perform a prediction of arrhythmias with nonlinear regression model. The method is illustrated using the MIT-BIH database.
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Aliyevа, A. M., I. I. Almazova, T. V. Pinchuk, E. V. Resnick, Yu N. Fedulaev i I. G. Nikitin. "The value of copeptin in the diagnosis and prognosis of cardiovascular diseases". Clinical Medicine (Russian Journal) 98, nr 3 (16.07.2020): 203–9. http://dx.doi.org/10.30629/0023-2149-2020-98-3-203-209.
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Vasopressin and its receptors play a key role in maintaining homeostasis in physiological and pathophysiological conditions. As a result, the vasopressin system has become an important target for both diagnostic and therapeutic use in a number of diseases. Kopeptin, C-terminal part of vasopressin prohormone. Copeptin has come to be seen as an important marker for identifying high-risk patients and predicting outcomes for various diseases. This improves the clinical value of commonly used biomarkers and risk stratification tools. The area that could benefit most from the introduction of the copeptin measurement in practice is cardiovascular disease. Determination of the level of copeptin becomes a fast and reliable method of differential diagnosis, especially in acute coronary syndromes. A special role in the diagnosis of acute myocardial infarction (AMI) is given to the combination of copeptin and troponin. According to available sources, such a combination eliminates AMI with very high sensitivity and negative predictive value. Moreover, elevated levels of copeptin correlate with poorer prognosis, and a higher risk of side effects after AMI, especially in patients with heart failure.
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Sureja, Nitesh, Bharat Chawda i Avani Vasant. "A novel salp swarm clustering algorithm for prediction of the heart diseases". Indonesian Journal of Electrical Engineering and Computer Science 25, nr 1 (1.01.2022): 265. http://dx.doi.org/10.11591/ijeecs.v25.i1.pp265-272.
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Heart <span>diseases have a severe impact on human life and health. Cardiovascular deaths and diseases have increased at a fast rate worldwide. The early prediction of these diseases is necessary to prevent deaths. Now a day; a considerable amount of medical information is available and collected as databases. An efficient technique is required to analyse this data and predict the disease. Clustering can help medical practitioners in diagnosis by classifying the patient’s data collected for a disease. Clustering techniques can analyse such data based on each patient-generated and predict disease. A new prediction model based on salp swarm algorithm and support vector machine is proposed in this research for predicting heart diseases. Salp swarm algorithm is used to select the useful features from the database. Support vector machine classifier is used to predict heart diseases. Results obtained are compared with the other algorithms available in the literature. It is observed that the proposed approach produces better results with accuracy 98.75% and 98.46% with the dataset 1 and 2, respectively. In addition to this, the algorithm converges in significantly less time in comparison to other algorithms. This algorithm might become a perfect supporting tool for medical </span>practitioners.
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Mohammed Shanshool, Abeer, Enas Mohammed Hussien Saeed i Hasan Hadi Khaleel. "Comparison of various data mining methods for early diagnosis of human cardiology". IAES International Journal of Artificial Intelligence (IJ-AI) 12, nr 3 (1.09.2023): 1343. http://dx.doi.org/10.11591/ijai.v12.i3.pp1343-1351.
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Recent healthcare reports indicate clearly an increasing mortality rates worldwide which puts a significant burden on the healthcare sector due to different diseases. Coronary artery diseases (CAD) is one of the main reasons of these uprising death rates since it affects the heart directly. For early diagnosis and treatment of CADs, a swiftly growing technology called data mining (DM) has been used to collect and categorize necessary data from patients; age, blood sugar and pressure, a type of thorax pain, cholesterol, and so on. Therefore, this paper adopted four DM methods; Decision tree (DT), logistic regression (LR), random forest (RF), and Naïve Bayes (NB) to achieve the goal. The paper utilized the Cleveland dataset along with Python programming language to compare among the four DM methods in terms of precision, accuracy, recall, and area under the curve. The results illustrated that NB method has the best accuracy of 89.47% compared with previous studies which will help with accurate, fast and inexpensive diagnosis of CADs.
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Upadhyay, Ravi Kant. "Emerging Risk Biomarkers in Cardiovascular Diseases and Disorders". Journal of Lipids 2015 (2015): 1–50. http://dx.doi.org/10.1155/2015/971453.
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Present review article highlights various cardiovascular risk prediction biomarkers by incorporating both traditional risk factors to be used as diagnostic markers and recent technologically generated diagnostic and therapeutic markers. This paper explains traditional biomarkers such as lipid profile, glucose, and hormone level and physiological biomarkers based on measurement of levels of important biomolecules such as serum ferritin, triglyceride to HDLp (high density lipoproteins) ratio, lipophorin-cholesterol ratio, lipid-lipophorin ratio, LDL cholesterol level, HDLp and apolipoprotein levels, lipophorins and LTPs ratio, sphingolipids, Omega-3 Index, and ST2 level. In addition, immunohistochemical, oxidative stress, inflammatory, anatomical, imaging, genetic, and therapeutic biomarkers have been explained in detail with their investigational specifications. Many of these biomarkers, alone or in combination, can play important role in prediction of risks, its types, and status of morbidity. As emerging risks are found to be affiliated with minor and microlevel factors and its diagnosis at an earlier stage could find CVD, hence, there is an urgent need of new more authentic, appropriate, and reliable diagnostic and therapeutic markers to confirm disease well in time to start the clinical aid to the patients. Present review aims to discuss new emerging biomarkers that could facilitate more authentic and fast diagnosis of CVDs, HF (heart failures), and various lipid abnormalities and disorders in the future.
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Suyash, Kumar, i K. R. Shobha. "Application of Artificial Neural Networks for Heart Disease Prediction". Journal of Computational and Theoretical Nanoscience 17, nr 9 (1.07.2020): 4190–96. http://dx.doi.org/10.1166/jctn.2020.9043.
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Heart related diseases are on a rise throughout the world. While the WHO estimates 31% of all deaths worldwide are caused by heart related diseases, some estimates even attribute 18 million deaths throughout the world due to such diseases. Although, the monumental strides in the field of machine learning, especially neural networks have enabled us to solve complex recognition problems, we still at large have been unable to utilize their power to the maximum in the data rich medical science field. These networks can in fact be used to construct intelligent systems which can help predict the presence of heart diseases in their early stages. Such intelligent systems shall result in significant life savings due to the readily available timely medical care and the following treatments. Encompassing the techniques of classification, a supervised learning approach of machine learning, in these intelligent systems can be aimed at pinpointing the accurate diagnosis. This paper thus, proposes a diagnostic system for predicting the presence of heart diseases using neural networks with back propagation.
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SEVLİ, Onur. "A Comparative Study of Heart Disease Diagnosis using Various Classifiers and Resampling Techniques". Journal of Intelligent Systems: Theory and Applications 5, nr 2 (1.09.2022): 92–105. http://dx.doi.org/10.38016/jista.1069541.
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Heart diseases are common worldwide and cause one-third of global deaths. The difficulty in distinguishing the symptoms of heart disease and the fact that most heart patients are not aware of the symptoms until the moment of crisis make the diagnosis of the disease difficult. Machine learning, an artificial intelligence discipline, provides experts with successful decision support solutions in diagnosing new cases based on known data. In this study, classifications were made using various machine learning techniques for the early diagnosis of heart diseases. The study was carried out on the UCI heart disease dataset, which is widely used in the literature. In order to increase the classification success, resampling techniques were used to ensure the class balance of the dataset. For each of 8 different machine learning techniques, namely Naive Bayes, Decision Trees, Support Vector Machine, K Nearest Neighbor, Logistic Regression, Random Forest, AdaBoost, and CatBoost, in addition to no-sampling classification, 8 different methods from oversampling and undersampling techniques were used to make a total of 72 classification processes were carried out. The result of each classification process is reported with 5 different parameters: accuracy, precision, recall, F1 score, and AUC. The highest accuracy value was obtained as 98.46% in the classification using Random Forest and InstanceHardnessThreshold undersampling technique. It was observed that the measurements obtained were higher than the results obtained in similar studies conducted in the literature in recent years.
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Dmitriev, A. "Clinical observations of serological diagnosis of gonorrhea". Kazan medical journal 25, nr 11 (29.10.2021): 1238. http://dx.doi.org/10.17816/kazmj80571.
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Carl Funk (Derm. Ztschr. Bd. 55, H. 2, 1929) performed a complement rejection reaction in 600 gonorroics and in 100 people with various other diseases, and in acute gonorrhea A. received 60% of positive results, in chronic - 80%, with epididymitis 90%, with prostatitis and spermatocystitis 97%, with arthritis, bursitis and tendovaginitis 97%. The brightness of the reaction is observed on the 14th day after the onset of the disease. According to the author's observations, the reaction is characterized by great specificity, only in isolated cases (polyart. Reumatica) there is a nonspecific delay in hemolysis. The reaction is of great service in differentiating doubtful cases of inflammation of the appendages, eyes, joints, heart disease and gonococcal sepsis and a number of other diseases of the genitourinary sphere. Stable positive, the reaction of deviation of complement in cases of establishing the fact that gonorrhea has been cured indicates the presence of a hidden focus with gonococci.
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Santalova, G. V., P. A. Lebedev, A. A. Garanin i M. E. Kuzin. "Problems of chronic rheumatic heart disease diagnosis at the present stage". Clinical Medicine (Russian Journal) 99, nr 4 (20.09.2021): 259–65. http://dx.doi.org/10.30629/0023-2149-2021-99-4-259-265.
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The review refl ects modern data on the epidemiology of acute rheumatic fever and chronic rheumatic heart disease in Russia and the world at present, as well as the dynamics of the prevalence of these diseases over the past decades. Much attention is paid to the issues of modern diagnostics of these conditions by physical, laboratory and instrumental methods. The focus is on the Jones criteria in the diagnosis of acute rheumatic fever in accordance with their revision by the American Heart Association experts in 2015. Taking into account the fact that damage to the valvular apparatus of the heart in acute rheumatic fever is the main disabling outcome of carditis at the present stage, a special place in the article is devoted to the discussion of echocardiographic criteria for valvulitis. The recommendations of the International Expert Council of the World Heart Federation aimed at detecting chronic rheumatic heart disease in patients without a history of acute rheumatic fever diagnosed by ultrasound imaging are also given. Criteria for pathological aortic and mitral regurgitation are presented. The authors believe that extrapolation of modern principles of ultrasound diagnostics of chronic rheumatic heart disease in Russia and their use as screening programs in young people and adolescents will contribute to its early detection and timely selection of patients for secondary prevention of benzathine with benzylpenicillin.
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Hasbullah, Sumayyah, Mohd Soperi Mohd Zahid i Satria Mandala. "Detection of Myocardial Infarction Using Hybrid Models of Convolutional Neural Network and Recurrent Neural Network". BioMedInformatics 3, nr 2 (15.06.2023): 478–92. http://dx.doi.org/10.3390/biomedinformatics3020033.
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Myocardial Infarction (MI) is the death of the heart muscle caused by lack of oxygenated blood flow to the heart muscle. It has been the main cause of death worldwide. The fastest way to detect MI is by using an electrocardiogram (ECG) device, which generates graphs of heartbeats morphology over a certain period of time. Patients with MI need fast intervention as delay will lead to worsening heart conditions or failure. To improve MI diagnosis, much research has been carried out to come up with a fast and reliable system to aid automatic MI detection and prediction from ECG readings. Recurrent Neural Network (RNN) with memory has produced more accurate results in predicting time series problems. Convolutional neural networks have also shown good results in terms of solving prediction problems. However, CNN models do not have the capability of remembering temporal information. This research proposes hybrid models of CNN and RNN techniques to predict MI. Specifically, CNN-LSTM and CNN-BILSTM models have been developed. The PTB XL dataset is used to train the models. The models predict ECG input as representing MI symptoms, healthy heart conditions or other cardiovascular diseases. Deep learning models offer automatic feature extraction, and our models take advantage of automatic feature extraction. The other superior models used their own feature extraction algorithm. This research proposed a straightforward architecture that depends mostly on the capability of the deep learning model to learn the data. Performance evaluation of the models shows overall accuracy of 89% for CNN LSTM and 91% for the CNN BILSTM model.
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Ziryawulawo, Ali, Angel Charles Ogare, Famina Ayebare i Ramadhani Sinde. "Application of IoT and Machine Learning Techniques for Heart Disease Prediction and Diagnosis: A Comprehensive Review". International Journal of Advances in Scientific Research and Engineering 08, nr 07 (2022): 76–85. http://dx.doi.org/10.31695/ijasre.2022.8.7.7.
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Heart diseases and related disorders have emerged as the most dangerous and leading cause of global deaths affecting mostly the elderly people who suffer from these diseases without realizing or knowing about it. Due to the fact that it’s very hard to notice the signs of the sickness at an early stage, mostly the signs will be shown once the heart problem has reached the peak level. This paper therefore presents a detailed review of how to monitor and predict heart disease using machine learning and the IoT. With the help of IoT, patients and doctors will monitor cardiovascular diseases early enough. A comparative analysis of different IoT technologies, most of which employed machine learning approaches for predicting and diagnosing cardiovascular disease, is conducted, different methodologies are compared and the results analysis is conducted and the performance tabulated. The Internet of Things (IoT) is transforming embedded systems into networked smart gadgets with sensors. The primary drawback of employing smart devices was their limited storage and processing capability, which cloud computing addressed by providing high-level processing and storage capacities. The Internet of Things (IoT) is transforming embedded systems into networked smart gadgets with sensors. The most significant disadvantage of using smart devices was their low cost. The problem of limited storage capacity and processing power was solved by cloud computing.
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Mel'nichenko, G. A., i I. I. Larina. "Syndrome of thyrotoxicosis. Differential diagnosis and treatment". Terapevticheskii arkhiv 90, nr 10 (15.10.2018): 4–13. http://dx.doi.org/10.26442/terarkh201890104-13.
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Since the middle of the twentieth century, there has been a significant change in methods of the diagnosis and treatment of thyroid diseases with thyrotoxicosis syndrome. Previously doctors did not have trouble just with diagnosing diseases that occur with a typical clinical presentation (the Merzeburg triad, a multinodal goiter with fibrillation) because of no possible to determine thyroid hormones. Then in the early 70s years the appearance of immunological methods for estimating hormones in the blood has led to significant changes in our understanding of the variants of thyroid pathology with thyrotoxicosis (TT). Today, the diagnosis of the fact of thyrotoxicosis as a whole is not difficult (except for the confusion of preanalytical errors), but differential diagnosis within the declared syndrome remains extremely relevant to this day. Unfortunately, in the minds of many doctors, these diseases are sometimes perceived as a whole, and in the conditions of the "century of speeds", a modern doctor, extremely limited in time, often unjustifiably prescribes thyreostatic therapy, treatment with radioactive iodine or even surgical intervention after detecting thyrotoxicosis. The old truth "remember that a patient with thyrotoxicosis is a person with a sick heart..." has not lost relevance today. It is very important for the practicing physician be able to navigate in the spectrum of pathologies manifested by the thyrotoxicosis pattern because of the influence of excess thyroid hormones on the cardiovascular system and the hemostasis system. Hereinafter we tried to show diagnostic aspects focusing on differences in pathologies with TT syndrome in a lot of thyroid diseases and even nonthyroid diseases.
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Shahmohammadi, Mehrdad, Hongxing Luo, Philip Westphal, Richard N. Cornelussen, Frits W. Prinzen i Tammo Delhaas. "Hemodynamics-driven mathematical model of first and second heart sound generation". PLOS Computational Biology 17, nr 9 (22.09.2021): e1009361. http://dx.doi.org/10.1371/journal.pcbi.1009361.
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We propose a novel, two-degree of freedom mathematical model of mechanical vibrations of the heart that generates heart sounds in CircAdapt, a complete real-time model of the cardiovascular system. Heart sounds during rest, exercise, biventricular (BiVHF), left ventricular (LVHF) and right ventricular heart failure (RVHF) were simulated to examine model functionality in various conditions. Simulated and experimental heart sound components showed both qualitative and quantitative agreements in terms of heart sound morphology, frequency, and timing. Rate of left ventricular pressure (LV dp/dtmax) and first heart sound (S1) amplitude were proportional with exercise level. The relation of the second heart sound (S2) amplitude with exercise level was less significant. BiVHF resulted in amplitude reduction of S1. LVHF resulted in reverse splitting of S2 and an amplitude reduction of only the left-sided heart sound components, whereas RVHF resulted in a prolonged splitting of S2 and only a mild amplitude reduction of the right-sided heart sound components. In conclusion, our hemodynamics-driven mathematical model provides fast and realistic simulations of heart sounds under various conditions and may be helpful to find new indicators for diagnosis and prognosis of cardiac diseases. New & noteworthy To the best of our knowledge, this is the first hemodynamic-based heart sound generation model embedded in a complete real-time computational model of the cardiovascular system. Simulated heart sounds are similar to experimental and clinical measurements, both quantitatively and qualitatively. Our model can be used to investigate the relationships between heart sound acoustic features and hemodynamic factors/anatomical parameters.
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Renò, Vito, Mauro Sciancalepore, Giovanni Dimauro, Rosalia Maglietta, Michele Cassano i Matteo Gelardi. "A Novel Approach for the Automatic Estimation of the Ciliated Cell Beating Frequency". Electronics 9, nr 6 (15.06.2020): 1002. http://dx.doi.org/10.3390/electronics9061002.
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The qualitative and quantitative evaluation of nasal epithelial cells is interesting in chronic infectious and inflammatory pathologies of the nose and sinuses. Among the cells of the population of the nasal mucosa, ciliated cells are particularly important. In fact, the observation of these cells is essential to investigate primary ciliary dyskinesia, a rare and severe disease associated with other serious diseases such as respiratory diseases, situs inversus, heart disease, and male infertility. Biopsy or brushing of the ciliary mucosa and assessment of ciliary function through measurements of the Ciliary Beating Frequency (CBF) are usually required to facilitate diagnosis. Therefore, low-cost and easy-to-use technologies devoted to measuring the ciliary beating frequency are desirable. We have considered related works in this field and noticed that up to date an actually usable system is not available to measure and monitor CBF. Moreover, performing this operation manually is practically unfeasible or demanding. For this reason, we designed BeatCilia, a low cost and easy-to-use system, based on image processing techniques, with the aim of automatically measuring CBF. This system performs cell Region of Interest (RoI) detection basing on dense optical flow computation of cell body masking, focusing on the cilia movement and taking advantage of the structural characteristics of the ciliated cell and CBF estimation by applying a fast Fourier transform to extract the frequency with the peak amplitude. The experimental results show that it offers a reliable and fast CBF estimation method and can efficiently run on a consumer-grade smartphone. It can support rhinocytologists during cell observation, significantly reducing their efforts.
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Rankovic, Nevena, Dragica Rankovic, Igor Lukic, Nikola Savic i Verica Jovanovic. "Unveiling the Comorbidities of Chronic Diseases in Serbia Using ML Algorithms and Kohonen Self-Organizing Maps for Personalized Healthcare Frameworks". Journal of Personalized Medicine 13, nr 7 (22.06.2023): 1032. http://dx.doi.org/10.3390/jpm13071032.
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In previous years, significant attempts have been made to enhance computer-aided diagnosis and prediction applications. This paper presents the results obtained using different machine learning (ML) algorithms and a special type of a neural network map to uncover previously unknown comorbidities associated with chronic diseases, allowing for fast, accurate, and precise predictions. Furthermore, we are presenting a comparative study on different artificial intelligence (AI) tools like the Kohonen self-organizing map (SOM) neural network, random forest, and decision tree for predicting 17 different chronic non-communicable diseases such as asthma, chronic lung diseases, myocardial infarction, coronary heart disease, hypertension, stroke, arthrosis, lower back diseases, cervical spine diseases, diabetes mellitus, allergies, liver cirrhosis, urinary tract diseases, kidney diseases, depression, high cholesterol, and cancer. The research was developed as an observational cross-sectional study through the support of the European Union project, with the data collected from the largest Institute of Public Health “Dr. Milan Jovanovic Batut” in Serbia. The study found that hypertension is the most prevalent disease in Sumadija and western Serbia region, affecting 9.8% of the population, and it is particularly prominent in the age group of 65 to 74 years, with a prevalence rate of 33.2%. The use of Random Forest algorithms can also aid in identifying comorbidities associated with hypertension, with the highest number of comorbidities established as 11. These findings highlight the potential for ML algorithms to provide accurate and personalized diagnoses, identify risk factors and interventions, and ultimately improve patient outcomes while reducing healthcare costs. Moreover, they will be utilized to develop targeted public health interventions and policies for future healthcare frameworks to reduce the burden of chronic diseases in Serbia.
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Varadharajan, Senthil Kumaran, i Viswanathan Nallasamy. "Implementation of Field Programmable Gate Array (FPGA) Based Distributed Arithmetic Gated Current Unit to Achieve High ECG Diagnosis Rate". Journal of Nanoelectronics and Optoelectronics 17, nr 1 (1.01.2022): 82–89. http://dx.doi.org/10.1166/jno.2022.3201.
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Nowadays, the diagnosis of different dreadful diseases are more important to save human livings by using fast growing Machine Learning and Deep Learning frameworks. Many algorithms such as Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), and Recurrent Neural networks (RNN) are playing the pivotal role in the diseases diagnosis particularly in detecting heart arrhythmias. But integrating these networks in the real time hardware remains to be daunting challenge among the researchers since it requires more hardware components and in-efficient methods of solving the larger datasets. To solve the aforementioned problem, this paper proposes the novel Distributed Arithmetic (DA) based Gated Recurrent Units (GRU) for achieving the better hardware efficiency and high diagnosis rate. GRU is considered to be simplified structure of the LSTM with reduced computational overhead. The paper also implements the Distributed Arithmetic (DA) operation for implementing the GRU’s hardware components to consume the less energy and delay. The extensive experimentation is carried out using ZYNQ-7000 SoC using hardware and software codesign methodology and tested with ECG datasets from UCI respiratory and performance boundaries such as accuracy, precision, recall, specificity, F1-score, power, delay and area are calculated and evaluated. Finally the validation and comparative analysis is done for the proposed framework. The outcomes proves that, the proposed DA based GRU framework provides promising solutions for the disease diagnosis and significantly utilized the resources during the experimentation.
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Huang, Rong, i Yingchun Zhou. "Disease Classification and Biomarker Discovery Using ECG Data". BioMed Research International 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/680381.
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In the recent decade, disease classification and biomarker discovery have become increasingly important in modern biological and medical research. ECGs are comparatively low-cost and noninvasive in screening and diagnosing heart diseases. With the development of personal ECG monitors, large amounts of ECGs are recorded and stored; therefore, fast and efficient algorithms are called for to analyze the data and make diagnosis. In this paper, an efficient and easy-to-interpret procedure of cardiac disease classification is developed through novel feature extraction methods and comparison of classifiers. Motivated by the observation that the distributions of various measures on ECGs of the diseased group are often skewed, heavy-tailed, or multimodal, we characterize the distributions by sample quantiles which outperform sample means. Three classifiers are compared in application both to all features and to dimension-reduced features by PCA: stepwise discriminant analysis (SDA), SVM, and LASSO logistic regression. It is found that SDA applied to dimension-reduced features by PCA is the most stable and effective procedure, with sensitivity, specificity, and accuracy being 89.68%, 84.62%, and 88.52%, respectively.
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Al-Absi, Hamada R. H., Mohammad Tariqul Islam, Mahmoud Ahmed Refaee, Muhammad E. H. Chowdhury i Tanvir Alam. "Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning". Sensors 22, nr 12 (7.06.2022): 4310. http://dx.doi.org/10.3390/s22124310.
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Cardiovascular diseases (CVD) are the leading cause of death worldwide. People affected by CVDs may go undiagnosed until the occurrence of a serious heart failure event such as stroke, heart attack, and myocardial infraction. In Qatar, there is a lack of studies focusing on CVD diagnosis based on non-invasive methods such as retinal image or dual-energy X-ray absorptiometry (DXA). In this study, we aimed at diagnosing CVD using a novel approach integrating information from retinal images and DXA data. We considered an adult Qatari cohort of 500 participants from Qatar Biobank (QBB) with an equal number of participants from the CVD and the control groups. We designed a case-control study with a novel multi-modal (combining data from multiple modalities—DXA and retinal images)—to propose a deep learning (DL)-based technique to distinguish the CVD group from the control group. Uni-modal models based on retinal images and DXA data achieved 75.6% and 77.4% accuracy, respectively. The multi-modal model showed an improved accuracy of 78.3% in classifying CVD group and the control group. We used gradient class activation map (GradCAM) to highlight the areas of interest in the retinal images that influenced the decisions of the proposed DL model most. It was observed that the model focused mostly on the centre of the retinal images where signs of CVD such as hemorrhages were present. This indicates that our model can identify and make use of certain prognosis markers for hypertension and ischemic heart disease. From DXA data, we found higher values for bone mineral density, fat content, muscle mass and bone area across majority of the body parts in CVD group compared to the control group indicating better bone health in the Qatari CVD cohort. This seminal method based on DXA scans and retinal images demonstrate major potentials for the early detection of CVD in a fast and relatively non-invasive manner.
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Alrabghi, Lujain, Raghad Alnemari, Rawan Aloteebi, Hamad Alshammari, Mustafa Ayyad, Mohammed Al Ibrahim, Mohsen Alotayfi, Turki Bugshan, Abdullah Alfaifi i Hussain Aljuwayd. "Stroke types and management". International Journal Of Community Medicine And Public Health 5, nr 9 (24.08.2018): 3715. http://dx.doi.org/10.18203/2394-6040.ijcmph20183439.
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Strokes are a leading cause of morbidity and mortality across the world, in fact the third leading cause after heart diseases and cancer. Additionally, among the survivors of stroke, one-third suffers from permanent disabilities. Strokes can be classified broadly as ischemic and hemorrhagic, which account for 80% and 20% of total respectively. The prognosis of cerebrovascular accidents depends on quick diagnosis of the type, followed by appropriate and fast management. We conducted this review using a comprehensive search of MEDLINE, PubMed and EMBASE, from January 1982 to March 2017. The following search terms were used: stroke, cerebrovascular accidents, ischemic stroke, hemorrhagic stroke, stroke types, management of stroke, rehabilitation, CVA prevention. The most critical part about approaching a stroke patient is to identify the type of stroke, whether hemorrhagic or ischemic, as each type requires a different guideline of management. Also, time is the key in preserving neuronal function and preventing further damage. At the same time, the general population must be educated about methods of preventing stroke by making positive lifestyle changes.
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MORIDANI, MOHAMMAD KARIMI, i MAJID POULADIAN. "A NOVEL METHOD TO ISCHEMIC HEART DISEASE DETECTION BASED ON NON-INVASIVE ECG IMAGING". Journal of Mechanics in Medicine and Biology 19, nr 03 (maj 2019): 1950002. http://dx.doi.org/10.1142/s0219519419500027.
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Electrocardiogram (ECG) signals containing very important information about the cardiac are one of the most common tools for physicians in the diagnosis of various types of cardiac diseases. Low accuracy in positioning, limitation of time accuracy, the similarity of signals between some diseases and normal signals and probability of missing some aspect of data are the defect aspects of this method. Importance of cardiac signals and defects of current methods in diagnosis show the need of substituting a new method to show the activity of cardiac. One of the most dangerous defections is ischemia, which corrects and on time diagnose could avoid the latter effect of it. Each of common methods for diagnosis of this illness has their own advantages and disadvantages. In this paper, we consider describing a non-invasive method for ischemic episode detection based on mapping of ECG signals. With this method, we can present the signals with virtual colors and facilitate the diagnosis of ischemic disease. So, a new method of 12-lead cardiac presentation is described that in fact present the 12-lead signals in two images. The result of this paper will present the indicators of sensitivity, specificity and accuracy in the context of disease diagnosis. This paper proposed a novel ECG imaging algorithm for classifying the normal and ischemic signals and 95.35% specificity, 96.79% sensitivity and 95.76% accuracy were achieved which are very much promising compared to the other methods and doctor’s accuracy.
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Chaulin, Aleksey M., i Dmitry V. Duplyakov. "Environmental factors and cardiovascular diseases". Hygiene and sanitation 100, nr 3 (16.04.2021): 223–28. http://dx.doi.org/10.47470/0016-9900-2021-100-3-223-228.
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Introduction. New advances in the diagnosis and treatment of cardiovascular diseases (CVD), as practice shows, are not able to significantly improve the statistical indicators of morbidity and mortality of CVD. This fact indicates that there are additional factors and mechanisms that are important to consider, both for prevention and for the most optimal management of patients. Recently, the relationship between environmental and lifestyle factors with CVD has been actively studied. However, despite understanding the relationship between environmental factors and various diseases, including CVD, the mechanisms by which specific factors increase or decrease the risk of developing CVD are not yet fully understood, and a number of studies are contradictory. The aim of our work was to generalize existing data on the impact of such critical environmental factors as air pollution and solar insolation on the cardiovascular system, as well as to comprehensively discuss the mechanisms by which these environmental factors can participate in the development and progression of CVD. To achieve our work’s goal, we analyzed modern foreign literature using the PubMed database. Conclusion. According to numerous experimental and clinical studies, air pollution and solar insolation deficiency play an essential role in developing CVD and the aggravation of patients with various CVD (atherosclerosis, hypertension, coronary heart disease, heart failure, myocardial infarction, and stroke). Thus, air pollution and lack of solar insolation can be considered as critical risk factors for CVD. Future research should focus on the study and establishment of specific pathogenetic mechanisms by which environmental factors affect the cardiovascular system’s health to develop effective treatment and prevention measures.
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Zhang, Shuo, Ruiqing Zhang, Shijie Chang, Chengyu Liu i Xianzheng Sha. "A Low-Noise-Level Heart Sound System Based on Novel Thorax-Integration Head Design and Wavelet Denoising Algorithm". Micromachines 10, nr 12 (17.12.2019): 885. http://dx.doi.org/10.3390/mi10120885.
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Along with the great performance in diagnosing cardiovascular diseases, current stethoscopes perform unsatisfactorily in controlling undesired noise caused by the surrounding environment and detector operation. In this case, a low-noise-level heart sound system was designed to inhibit noise by a novel thorax-integration head with a flexible electric film. A hardware filter bank and wavelet-based algorithm were employed to enhance the recorded heart sounds from the system. In the experiments, we used the new system and the 3M™ Littmann® Model 3200 Electronic Stethoscope separately to record heart sounds in different noisy environments. The results illustrated that the average estimated noise ratio represented 21.26% and the lowest represented only 12.47% compared to the 3M stethoscope, demonstrating the better performance in denoising ability of this system than state-of-the-art equipment. Furthermore, based on the heart sounds recorded with this system, some diagnosis results were achieved from an expert and compared to echocardiography reports. The diagnoses were correct except for two uncertain items, which greatly confirmed the fact that this system could reserve complete pathological information in the end.
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Yang, Hsin-Jung, Ilkay Oksuz, Damini Dey, Jane Sykes, Michael Klein, John Butler, Michael S. Kovacs i in. "Accurate needle-free assessment of myocardial oxygenation for ischemic heart disease in canines using magnetic resonance imaging". Science Translational Medicine 11, nr 494 (29.05.2019): eaat4407. http://dx.doi.org/10.1126/scitranslmed.aat4407.
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Myocardial oxygenation—the ability of blood vessels to supply the heart muscle (myocardium) with oxygen—is a critical determinant of cardiac function. Impairment of myocardial oxygenation is a defining feature of ischemic heart disease (IHD), which is caused by pathological conditions that affect the blood vessels supplying oxygen to the heart muscle. Detecting altered myocardial oxygenation can help guide interventions and prevent acute life-threatening events such as heart attacks (myocardial infarction); however, current diagnosis of IHD relies on surrogate metrics and exogenous contrast agents for which many patients are contraindicated. An oxygenation-sensitive cardiac magnetic resonance imaging (CMR) approach used previously to demonstrate that CMR signals can be sensitized to changes in myocardial oxygenation showed limited ability to detect small changes in signals in the heart because of physiologic and imaging noise during data acquisition. Here, we demonstrate a CMR-based approach termed cfMRI [cardiac functional magnetic resonance imaging (MRI)] that detects myocardial oxygenation. cfMRI uses carbon dioxide for repeat interrogation of the functional capacity of the heart’s blood vessels via a fast MRI approach suitable for clinical adoption without limitations of key confounders (cardiac/respiratory motion and heart rate changes). This method integrates multiple whole-heart images within a computational framework to reduce noise, producing confidence maps of alterations in myocardial oxygenation. cfMRI permits noninvasive monitoring of myocardial oxygenation without requiring ionizing radiation, contrast agents, or needles. This has the potential to broaden our ability to noninvasively identify IHD and a diverse spectrum of heart diseases related to myocardial ischemia.
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Flachskampf, Frank A., i Tomasz Baron. "The Role of Novel Cardiac Imaging for Contemporary Management of Heart Failure". Journal of Clinical Medicine 11, nr 20 (20.10.2022): 6201. http://dx.doi.org/10.3390/jcm11206201.
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Heart failure is becoming the central problem in cardiology. Its recognition, differential diagnosis, and the monitoring of therapy are intimately coupled with cardiac imaging. Cardiac imaging has witnessed an explosive growth and differentiation, with echocardiography continuing as the first diagnostic step; the echocardiographic exam itself has become considerably more complex than in the last century, with the assessment of diastolic left ventricular function and strain imaging contributing important information, especially in heart failure. Very often, however, echocardiography can only describe the fact of functional impairment and morphologic remodeling, whereas further clarification of the underlying disease, such as cardiomyopathy, myocarditis, storage diseases, sarcoidosis, and others, remains elusive. Here, cardiovascular magnetic resonance and perfusion imaging should be used judiciously to arrive as often as possible at a clear diagnosis which ideally enables specific therapy.
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Radha, Remya, Syeda Kiran Shahzadi i Mohammad Hussein Al-Sayah. "Fluorescent Immunoassays for Detection and Quantification of Cardiac Troponin I: A Short Review". Molecules 26, nr 16 (9.08.2021): 4812. http://dx.doi.org/10.3390/molecules26164812.
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Cardiovascular diseases are considered one of the major causes of human death globally. Myocardial infarction (MI), characterized by a diminished flow of blood to the heart, presents the highest rate of morbidity and mortality among all other cardiovascular diseases. These fatal effects have triggered the need for early diagnosis of appropriate biomarkers so that countermeasures can be taken. Cardiac troponin, the central key element of muscle regulation and contraction, is the most specific biomarker for cardiac injury and is considered the “gold standard”. Due to its high specificity, the measurement of cardiac troponin levels has become the predominant indicator of MI. Various forms of diagnostic methods have been developed so far, including chemiluminescence, fluorescence immunoassay, enzyme-linked immunosorbent assay, surface plasmon resonance, electrical detection, and colorimetric protein assays. However, fluorescence-based immunoassays are considered fast, accurate and most sensitive of all in the determination of cardiac troponins post-MI. This review represents the strategies, methods and levels of detection involved in the reported fluorescence-based immunoassays for the detection of cardiac troponin I.
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MOHSIN, AHMAD, i OLIVER FAUST. "AUTOMATED CHARACTERIZATION OF CARDIOVASCULAR DISEASES USING WAVELET TRANSFORM FEATURES EXTRACTED FROM ECG SIGNALS". Journal of Mechanics in Medicine and Biology 19, nr 01 (luty 2019): 1940009. http://dx.doi.org/10.1142/s0219519419400098.
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Cardiovascular disease has been the leading cause of death worldwide. Electrocardiogram (ECG)-based heart disease diagnosis is simple, fast, cost effective and non-invasive. However, interpreting ECG waveforms can be taxing for a clinician who has to deal with hundreds of patients during a day. We propose computing machinery to reduce the workload of clinicians and to streamline the clinical work processes. Replacing human labor with machine work can lead to cost savings. Furthermore, it is possible to improve the diagnosis quality by reducing inter- and intra-observer variability. To support that claim, we created a computer program that recognizes normal, Dilated Cardiomyopathy (DCM), Hypertrophic Cardiomyopathy (HCM) or Myocardial Infarction (MI) ECG signals. The computer program combined Discrete Wavelet Transform (DWT) based feature extraction and K-Nearest Neighbor (K-NN) classification for discriminating the signal classes. The system was verified with tenfold cross validation based on labeled data from the PTB diagnostic ECG database. During the validation, we adjusted the number of neighbors [Formula: see text] for the machine learning algorithm. For [Formula: see text], training set has an accuracy and cross validation of 98.33% and 95%, respectively. However, when [Formula: see text], it showed constant for training set but dropped drastically to 80% for cross-validation. Hence, training set [Formula: see text] prevails. Furthermore, a confusion matrix proved that normal data was identified with 96.7% accuracy, 99.6% sensitivity and 99.4% specificity. This means an error of 3.3% will occur. For every 30 normal signals, the classifier will mislabel only 1 of the them as HCM. With these results, we are confident that the proposed system can improve the speed and accuracy with which normal and diseased subjects are identified. Diseased subjects can be treated earlier which improves their probability of survival.
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Choi, Yoon-A., Sejin Park, Jong-Arm Jun, Chee Meng Benjamin Ho, Cheol-Sig Pyo, Hansung Lee i Jaehak Yu. "Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals". Applied Sciences 11, nr 4 (17.02.2021): 1761. http://dx.doi.org/10.3390/app11041761.
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Stroke is the third highest cause of death worldwide after cancer and heart disease, and the number of stroke diseases due to aging is set to at least triple by 2030. As the top three causes of death worldwide are all related to chronic disease, the importance of healthcare is increasing even more. Models that can predict real-time health conditions and diseases using various healthcare services are attracting increasing attention. Most diagnosis and prediction methods of stroke for the elderly involve imaging techniques such as magnetic resonance imaging (MRI). It is difficult to rapidly and accurately diagnose and predict stroke diseases due to the long testing times and high costs associated with MRI. Thus, in this paper, we design and implement a health monitoring system that can predict the precursors of stroke diseases in the elderly in real time during daily walking. First, raw electroencephalography (EEG) data from six channels were preprocessed via Fast Fourier Transform (FFT). The raw EEG power values were then extracted from the raw spectra: alpha (α), beta (β), gamma (γ), delta (δ), and theta (θ) as well as the low β, high β, and θ to β ratio, respectively. The experiments in this paper confirm that the important features of EEG biometric signals alone during walking can accurately determine stroke precursors and occurrence in the elderly with more than 90% accuracy. Further, the Random Forest algorithm with quartiles and Z-score normalization validates the clinical significance and performance of the system proposed in this paper with a 92.51% stroke prediction accuracy. The proposed system can be implemented at a low cost, and it can be applied for early disease detection and prediction using the precursor symptoms of real-time stroke. Furthermore, it is expected that it will be able to detect other diseases such as cancer and heart disease in the future.
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Krestin, G. P., P. Theissen, G. Friedmann, H. Schicha i A. Linden. "Nierenarterienstenose: Möglichkeiten der Kernspintomographie". Nuklearmedizin 28, nr 06 (1989): 226–33. http://dx.doi.org/10.1055/s-0038-1629495.
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Non-invasive detection of stenotic lesions of the renal arteries remains an important clinical problem. Recent advances in magnetic resonance angiography represent a significant progress towards achieving non-invasive diagnosis of vascular diseases. The purpose of this study was to evaluate the possibilities of assessment of renal artery stenosis with commonly available hard-and software equipment. Imaging of renal arteries was performed with a ECG-gated fast multiphase gradient echo sequence which allows production of a series of images in different heart phases. Examinations were performed in 15 healthy volunteers and in 12 patients with angiographically verified renal artery stenosis. In 10 patients additional dynamic studies with fast imaging during short breath-holding periods after administration of gadolinium-DTPA served for the assessment of renal perfusion. A superconducting system operating at 1.5 T was used to produce gradient echo sequences with small flip angles and dephasing gradients of constant amplitude. To find the optimal imaging method for depiction of the renal arteries the following parameters were systematically varied: respiratory gating, resolution, number of excitations, slice thickness, phase encoding direction, rephasing gradients, Flip angle and echo time. A good visualization of the vessels was always possible and using the best parameter combination even the narrowed lumen could be assessed in some cases. With this technique or with gadolinium-enhanced dynamic studies the perfusion of the kidneys can be demonstrated. However, quantitation of the stenosis or quantitation of renal perfusion was not possible; even the depiction of the stenotic lesion was successful only in half of the cases. Thus commonly used MR equipment is not yet able to replace more invasive methods in the diagnosis of renal artery stenosis. In order to make MR angiography a successful technique for the assessment of vascular diseases more sophisticated methods that allow a quantitation of flow or velocity across the vessel, will have to be developed.
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Obrezan, Andrey, i Nataliya Shcherbakova. "CANCER TREATMENT AND CARDIOVASCULAR TOXITY: PATHOGENESIS AND ACTUAL APPROACHES TO THE DIAGNOSIS OF HEART FAILURE IN CANCER PATIENTS". Problems in oncology 65, nr 2 (1.02.2019): 172–80. http://dx.doi.org/10.37469/0507-3758-2019-65-2-172-180.
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The field of cardio-oncology has received increasing attention in recent years. This is due to the fact that the results of a large number of clinical studies on antitumor therapy, covering issues treatments side effects, including associated cardiovascular pathology, are published. Advances in treatment have led to improved survival of patients with cancer, but have also increased clinical significance of treatment side effects. Myocardium, having high metabolic activity, responds to substrate and energy imbalance under the action of increasing malignancy and toxic effects of radio- or chemotherapy. Finding of baseline risk factors, timely identification of cardiovascular diseases, ability to predict the long-term consequences of cancer treatment-associated cardiovascular side effects lead to improving of the prognosis and quality of life, avoiding of over-diagnosis cardiovascular diseases and inappropriating violation of life-saving treatment of a malignant tumor.
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Caenen, Annette, Mathieu Pernot, Kathryn R. Nightingale, Jens-Uwe Voigt, Hendrik J. Vos, Patrick Segers i Jan D’hooge. "Assessing cardiac stiffness using ultrasound shear wave elastography". Physics in Medicine & Biology 67, nr 2 (17.01.2022): 02TR01. http://dx.doi.org/10.1088/1361-6560/ac404d.
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Abstract Shear wave elastography offers a new dimension to echocardiography: it measures myocardial stiffness. Therefore, it could provide additional insights into the pathophysiology of cardiac diseases affecting myocardial stiffness and potentially improve diagnosis or guide patient treatment. The technique detects fast mechanical waves on the heart wall with high frame rate echography, and converts their propagation speed into a stiffness value. A proper interpretation of shear wave data is required as the shear wave interacts with the intrinsic, yet dynamically changing geometrical and material characteristics of the heart under pressure. This dramatically alters the wave physics of the propagating wave, demanding adapted processing methods compared to other shear wave elastography applications as breast tumor and liver stiffness staging. Furthermore, several advanced analysis methods have been proposed to extract supplementary material features such as viscosity and anisotropy, potentially offering additional diagnostic value. This review explains the general mechanical concepts underlying cardiac shear wave elastography and provides an overview of the preclinical and clinical studies within the field. We also identify the mechanical and technical challenges ahead to make shear wave elastography a valuable tool for clinical practice.
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Bhanujyothi H C, Dr.Chetana Tukkoji, Vidya J, Swastika T. Jain, Shyamala Boosi,. "Prognosis of Diabetes Mellitus using Machine Learning Techniques". Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, nr 5 (11.04.2021): 836–41. http://dx.doi.org/10.17762/turcomat.v12i5.1491.
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Diabetes mellitus is a condition caused due to increase in blood glucose level. More than 90% of people are diagnosed with Type 2 diabetes disease,T2D is a fast-growing, chronic disease caused by the imbalance in insulin function. Diabetes is a now the leading cause of heart disease, stroke, blindness, non-traumatic limb amputations and end-stage renal failure. Early detection may take a step towards keeping diabetes patients healthy and it also reduces the risk of such serious complications. Nowadays, the application of Machine learning in the medical field is gradually increasing. This can aid in improving the classification system used for disease diagnosis, that assist medical experts in detecting the fatal diseases at an early stage. This paper presents a performance comparison of the machine learning algorithms in diabetes detection. Techniques like SVM, Random forest, Gradient Boosting, Navie Bayes, Logistic regressionand KNN are used in this work.
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Naidu, Nikita A., Kamlesh Wadher i Milind Umekar. "An Overview on Biomaterials: Pharmaceutical and Biomedical Applications". Journal of Drug Delivery and Therapeutics 11, nr 1-s (15.02.2021): 154–61. http://dx.doi.org/10.22270/jddt.v11i1-s.4723.
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The development of biomaterials have existed from around half a century and manifest its use in different fields. Biomaterials are used in living creature body, looking on its biocompatibility nature. In recent years, advances of biomaterials are showing a marked presence in the fast growing fields of pharmaceuticals and medicines. According to their availability, different types of biomaterials like metal, ceramic, polymer and their composites are used for several purpose in the body. In this review article, types of biomaterials have been discussed with their advantages, disadvantages and recent applications in the pharmaceutical field such as implants used to mimic the structure and function of tissues, dental implants, wound healing, cell regeneration, regenerative medicines, delivery of drugs and different organ regeneration. Organ regeneration leading to replacement of organs such as heart, trachea and lungs etc. by use of specific biomaterials have been reported with the diagnosis of diseases and its treatment.
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Khalirakhmanov, A. F., A. Z. Sharafeev, G. D. Gatiyatullina, S. V. Zinchenko, R. F. Gaifullina i A. A. Rizvanov. "Heart failure in cancer patients". Siberian journal of oncology 20, nr 6 (12.01.2022): 114–19. http://dx.doi.org/10.21294/1814-4861-2021-20-6-114-119.
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The purpose of the study was to conduct a systematic review of data on the role of heart failure (HF) in the development of cancer, as well as to discuss problems dealing with diagnosis and treatment of heart failure in cancer patients. Material and methods. A literature search was conducted using the Cochrane library, elibrary, medline, and embase databases over the past 7 years. The general mechanisms of heart failure and cancer, cardiotoxicity risk factors, and some aspects of the diagnosis and treatment of HF in cancer patients were analyzed. Results. The literature analysis indicates that cardiovascular disease and cancer have common risk factors. Several common pathophysiological mechanisms that associate HF with cancer have been identified. They include inflammation, oxidative stress, and neurohomonal activation. HF is known to be a common complication of aggressive cardiotoxic cancer therapy that can aggravate or trigger existing HF. Recent epidemiological studies have shown that the development of cancer is more common among patients with pre-existing HF. Although the reason for this relationship has not yet been identified, it is assumed that HF may be a pro-oncogenic condition. There are several strategies to prevent and treat toxicity of various chemotherapeutic drugs. They are all based on accurate patient selection, short- and longterm follow-up, and therapies that can prevent and delay cardiac dysfunction. Conclusion. The main goal of cardio-oncology is to prevent and treat of cardiotoxic effects of chemotherapy drugs. In this context, elucidation of the underlying mechanisms plays an important role in the development of strategies for the prevention of chemotherapy-associated cardiomyopathy. It is necessary to pay attention to the fact that there is more and more evidence that patients with HF have high risks of developing cancer, thereby requiring more attention. In general, understanding the direct and indirect mechanisms of the relationship between HF and cancer can help in the prevention and early diagnosis of these diseases.
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Konop, Marek, Mateusz Rybka, Emilia Waraksa, Anna Laskowska, Artur Nowiński, Tomasz Grzywacz, Wojciech Karwowski, Adrian Drapała i Ewa Kłodzińska. "Electrophoretic Determination of Trimethylamine (TMA) in Biological Samples as a Novel Potential Biomarker of Cardiovascular Diseases Methodological Approach". International Journal of Environmental Research and Public Health 18, nr 23 (23.11.2021): 12318. http://dx.doi.org/10.3390/ijerph182312318.
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In competitive athletes, the differential diagnosis between nonpathological changes in cardiac morphology associated with training (commonly referred to as “athlete’s heart”) and certain cardiac diseases with the potential for sudden death is an important and not uncommon clinical problem. The use of noninvasive, fast, and cheap analytical techniques can help in making diagnostic differentiation and planning subsequent clinical strategies. Recent studies have demonstrated the role of gut microbiota and their metabolites in the onset and the development of cardiovascular diseases. Trimethylamine (TMA), a gut bacteria metabolite consisting of carnitine and choline, has recently emerged as a potentially toxic molecule to the circulatory system. The present work aims to develop a simple and cost-effective capillary electrophoresis-based method for the determination of TMA in biological samples. Analytical characteristics of the proposed method were evaluated through the study of its linearity (R2 > 0.9950) and the limit of detection and quantification (LOD = 1.2 µg/mL; LOQ = 3.6 µg/mL). The method shows great potential in high-throughput screening applications for TMA analysis in biological samples as a novel potential biomarker of cardiovascular diseases. The proposed electrophoretic method for the determination of TMA in biological samples from patients with cardiac disease is now in progress.
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Tarlovskaya, E. I., E. V. Solovyova, N. A. Popova, T. V. Vlasova, M. L. Gorbunova, N. V. Idabaeva i Yu A. Pochukalina. "A clinical case of arrhythmogenic right ventricular dysplasia (ADP)". South Russian Journal of Therapeutic Practice 3, nr 2 (29.06.2022): 100–106. http://dx.doi.org/10.21886/2712-8156-2022-3-2-100-106.
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The difficulties of diagnosis and management of patients with alcohol-induced heart lesions, features of the clinic of alcoholic cardiomyopathy are highlighted. Excessive and prolonged alcohol consumption increases the risk of developing acute and chronic heart failure, cardiac arrhythmias and aggravates existing cardiovascular diseases. At the same time, due to insufficient assessment of the origin of cardiac manifestations (patients often hide or downplay the fact of alcohol abuse), patients do not always receive specific treatment. The management of such patients presents significant difficulties, taking into account, among other things, the defeat of the gastrointestinal tract, central and peripheral nervous system and should be carried out jointly with doctors of other specialties.
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Nasrabadi, Abbas, i Javad Haddadnia. "Predicting Heart Attacks in Patients Using Artificial Intelligence Methods". Modern Applied Science 10, nr 3 (13.01.2016): 66. http://dx.doi.org/10.5539/mas.v10n3p66.
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<p class="zhengwen">Today the heart disease is one of the most important causes of death in the world. So its early prediction and diagnosis is important in medical field, which could help in on time treatment, decreasing health costs and decreasing death caused by it. In fact the main goal of using data mining algorithms in medicine by using patients’ data is better utilizing the database and discovering tacit knowledge to help doctors in better decision making.</p><p class="zhengwen">Therefore using data mining and discovering knowledge in cardiovascular centers could create a valuable knowledge, which improves the quality of service provided by managers, and could be used by doctors to predict the future behavior of heart diseases using past records. Also some of the most important applications of data mining and knowledge discovery in heart patients system includes: diagnosing heart attack from various signs and properties, evaluating the risk factors which increases the heart attack.</p>In this article the effort focused on evaluating the previous works on discovering knowledge using data mining in heart diseases field, and also explain the used algorithms in every one of the previous works, to help the future researchers to gain maximum benefits from these abilities. Because of this, in the next sections, first we will explain various works in data mining field using heart patients’ data, and will show the ability of data mining in various applications of heart disease field, and based on a table will show the history of data mining and it’s applications in heart diseases field. Finally we will provide the best methods and algorithms used in various applications of heart diseases using a comparison and will show the results in a table. It is obvious in the diagrams that the suggested method has the best performance and best quality in prediction.
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Xue, Jia, Yani Zhang, Zhe Guang, Ting Miao, Zohaib Ali, Dun Qiao, Yiming Yao i in. "Ultra-High Sensitivity Terahertz Microstructured Fiber Biosensor for Diabetes Mellitus and Coronary Heart Disease Marker Detection". Sensors 23, nr 4 (10.02.2023): 2020. http://dx.doi.org/10.3390/s23042020.
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Diabetes Mellitus (DM) and Coronary Heart Disease (CHD) are among top causes of patient health issues and fatalities in many countries. At present, terahertz biosensors have been widely used to detect chronic diseases because of their accurate detection, fast operation, flexible design and easy fabrication. In this paper, a Zeonex-based microstructured fiber (MSF) biosensor is proposed for detecting DM and CHD markers by adopting a terahertz time-domain spectroscopy system. A suspended hollow-core structure with a square core and a hexagonal cladding is used, which enhances the interaction of terahertz waves with targeted markers and reduces the loss. This work focuses on simulating the transmission performance of the proposed MSF sensor by using a finite element method and incorporating a perfectly matched layer as the absorption boundary. The simulation results show that this MSF biosensor exhibits an ultra-high relative sensitivity, especially up to 100.35% at 2.2THz, when detecting DM and CHD markers. Furthermore, for different concentrations of disease markers, the MSF exhibits significant differences in effective material loss, which can effectively improve clinical diagnostic accuracy and clearly distinguish the extent of the disease. This MSF biosensor is simple to fabricate by 3D printing and extrusion technologies, and is expected to provide a convenient and capable tool for rapid biomedical diagnosis.
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Sigurdardottir, Elin Edda, Ingemar Turesson, Sigrun Helga Lund, Ebba K. Lindqvist, Neha Korde, Sham Mailankody, Magnus Björkholm, Ola Landgren i Sigurdur Y. Kristinsson. "Multiple Myeloma Patients With Prior Knowledge Of MGUS Have a Better Survival Compared To Multiple Myeloma Patients Without Prior Knowledge Of MGUS". Blood 122, nr 21 (15.11.2013): 1984. http://dx.doi.org/10.1182/blood.v122.21.1984.1984.
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Abstract Background A recent prospective cancer screening trial including over 77,000 individuals followed-up for over 10 years shows that multiple myeloma (MM) is consistently preceded by a precursor state, monoclonal gammopathy of undetermined significance (MGUS). Clinically, most newly diagnosed MM patients are unaware of their prior MGUS state. Importantly, among individuals diagnosed with MGUS, only a small proportion will develop MM or a related malignancy during their lifespan. Current clinical guidelines suggest lifelong, annual monitoring of individuals diagnosed with MGUSto detect progression to MM or related disorders. At this time, the impact of annual monitoring on the outcome of patients who eventually develop MM is unknown. In addition, as MGUS is usually diagnosed during work-up for another disorder, the impact of comorbidity in MM patients with prior knowledge of MGUS is unknown. We assessed the impact of prior knowledge of MGUS in relation to MM survival. Patients and Methods The study cohort consisted of 14,798 individuals diagnosed with MM in Sweden 1976-2005, with follow-up until 2007. A total of 394 patients had previously been diagnosed with MGUS. Patients diagnosed with MM were identified through the Swedish Cancer Register. MM patients with prior knowledge of MGUS were identified from a nationwide MGUS cohort which was established from in and out-patient units from major hospital-based hematology/oncology centers in Sweden. Details of sex, date of birth, date of diagnosis, type and concentration of M-protein at MGUS diagnosis, and comorbidities (autoimmune diseases, infections, other malignant diseases, ischemic heart diseases, heart failure, cerebrovascular diseases, chronic lung diseases and renal diseases) were gathered for all patients. Survival rate from time of MM diagnosis comparing patients with and without prior knowledge of MGUS was calculated with a Kaplan Meier method. Risk factors for death were analysed in a Cox proportional hazards model where relative risks (RR) and 95% confidence intervals (CIs) were calculated. A Chi-square test was used to evaluate whether there was a significant difference in comorbidities. Results MM patients with prior knowledge of MGUS had significantly (RR = 0.86; CI = 0.77-0.96) better survival (median = 2.8 years; 95% CI = 2.6-3.3) than MM patients without prior knowledge of MGUS (median = 2.1 years; 95% CI = 2.1-2.2; Figure). Older age at diagnosis (RR = 1.04; 95% CI = 1.04-1.05) was associated with an inferior survival, whereas female sex and recent year at diagnosis were related to improved survival: RR = 0.86 (CI = 0.83-0.89) and 0.95 (CI = 0.95-0.95), respectively. There was no difference in survival comparing MGUS patients with high (>1.5 g/dL) versus low (<1.5 g/dL) M-protein concentration (RR = 1.01; 95% CI 0.72-1.41). Unexpectedly, M-protein concentration ≤0.5 g/dL at MGUS diagnosis was associated with poored survival than M-protein concentration greater than 0.5 g/dL (RR = 1.86; 95% CI = 1.13-3.04, p=0.014). Autoimmune diseases (p<0.001), infections (p<0.001), other malignant diseases (p<0.001), ischemic heart diseases (p<0.001), heart failure (p<0.001), cerebrovascular diseases (p<0.001), and renal diseases (p<0.001) at diagnosis of MM were significantly more common in MM patients with prior knowledge of MGUS than in MM patients without. Conclusions Based almost 15,000 MM patients diagnosed in Sweden between 1976 and 2005, for the first time, we found that prior knowledge of MGUS is associated with better survival (median overall survival 2.8 versus 2.1 years). Given patterns of co-morbidities, the observed survival difference is likely a reflection of the fact that MGUS patients are evaluated more often for signs of MM progression and may be diagnosed/treated at an earlier stage. The finding that low-risk MGUS patients that develop MM had a shorter survival rate is interesting and may be due to less intense monitoring and needs to be studied further. Our observations raise the question whether screening for MGUS, e.g., above the age of 50, could translate into earlier detection/treatment of MM (“early myeloma“) and lead to better survival. Future prospective studies are needed to explore these findings further. Disclosures: Turesson: Celgene Corp: Honoraria.
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Palstrøm, Nicolai Bjødstrup, Rune Matthiesen, Lars Melholt Rasmussen i Hans Christian Beck. "Recent Developments in Clinical Plasma Proteomics—Applied to Cardiovascular Research". Biomedicines 10, nr 1 (12.01.2022): 162. http://dx.doi.org/10.3390/biomedicines10010162.
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The human plasma proteome mirrors the physiological state of the cardiovascular system, a fact that has been used to analyze plasma biomarkers in routine analysis for the diagnosis and monitoring of cardiovascular diseases for decades. These biomarkers address, however, only a very limited subset of cardiovascular diseases, such as acute myocardial infarct or acute deep vein thrombosis, and clinical plasma biomarkers for the diagnosis and stratification cardiovascular diseases that are growing in incidence, such as heart failure and abdominal aortic aneurysm, do not exist and are urgently needed. The discovery of novel biomarkers in plasma has been hindered by the complexity of the human plasma proteome that again transforms into an extreme analytical complexity when it comes to the discovery of novel plasma biomarkers. This complexity is, however, addressed by recent achievements in technologies for analyzing the human plasma proteome, thereby facilitating the possibility for novel biomarker discoveries. The aims of this article is to provide an overview of the recent achievements in technologies for proteomic analysis of the human plasma proteome and their applications in cardiovascular medicine.
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Vakhnenko, Yu V., E. A. Bagdasaryan i D. A. Savchenko. "Primary diagnosis of dilated cardiomyopathy in combination with myocardial non-compaction in an elderly patient: a case report". Russian Journal of Cardiology 28, nr 5 (29.03.2023): 5344. http://dx.doi.org/10.15829/1560-4071-2023-5344.
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The classifications of the World Health Organization, the European Society of Car-diology and the American Heart Association indicate the existence of several phenotypes of myocardial non-compaction (MnC) with specific structural and functional abnormalities. The MnC+dilated cardiomyopathy (DCM) phenotype is considered one of the most severe variants. Disputes continue about whether to regard MnC as an independent disease or as a consequence of DCM and heart failure. In other words, MnC remains one of the most mysterious heart diseases. As an illustration of MnC+DCM phenotype, the authors offer a case of a patient with cardiovascular disease from her youth, but maintained a satisfactory state of health and performance until her old age. Symptoms of arrhythmia and heart failure with massive pericardial effusion were first described in her at the age of 66, which is uncharacteristic for this MnC phenotype. Attention is drawn to the difficulties of differential diagnosis of MnC due to the non-specificity of clinical performance, the role of echocardiography in the recognition of the disease and predictors of its unfavorable outcome. The fact that the patient, even when typical signs of MnC were detected during echocardiography, initially had coronary artery disease as the main diagnosis, indicates the relevance of publishing another case report on this rare pathology in order to improve the awareness of cardiologists and general practitioners.
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Umunakwe, Bruno Onyinye, i Delian Chimaroke Anyanwu. "Repositioning the knowledge of mental health disorders among the Igbo tribe of Nigeria". Advanced Research in Medical and Health Sciences 1, nr 1 (19.02.2023): 1–12. http://dx.doi.org/10.57040/armhs.v1i1.359.
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The Igbo tribe of eastern Nigeria take the lead in expressing that mental health disorders are products of metaphysical machination or unwholesome practice which originated principally from various spiritual/metaphysical causations such as: - punishment from God or gods, evil eyes of the enemies, violation of certain customs, spirit/demonic possession, evil manipulations, sorcery, disturbances in social relations, dependence on drug substances and natural causes. In fact, the Igbo teaching on mental health diseases separated the causations and treatments from clinical protocols. By this, certain mental health diseases are incurable. Today, clinical facts have separated mental health disorders from the realm of spiritual causations with medical proofs that mental diseases comprise elements of genetic, psychological, environmental and sociological dispositions such as heart disease, drug abuse, anxiety, fever and other related medical issues. This happens that disease etiology, medical symptoms and physical examination define the type of mental disorder, diagnosis and treatment. This study aims at changing the traditional knowledge Igbo people have about mental disorders through creating wider clinical discussions on mental diseases. The study recommends that where there are cases of mental disorders, medical treatment, diagnosis and counselling (psychotherapy) should be considered appropriate.
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Emídio, Lima. "Cardiac Tamponade as the Cause of Pulmonary Edema: Case Report". Journal of Pulmonology and Respiratory Research 7, nr 2 (17.08.2023): 021–23. http://dx.doi.org/10.29328/journal.jprr.1001046.
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Introduction: Cardiac tamponade is an emergency syndrome that requires fast diagnosis and treatment; otherwise patient follows obstructive shock and cardiac arrest. Case report: A 70-year-old female was brought to the emergency department with hypoxemia. She had a history of progressive dyspnea over the past three weeks. Past medical history includes smoking. On physical examination: tachypnea, hypoxemia (SaO2 89%), jugular venous distention, arterial pressure 220/100 mmHg, heart rate rhythmic of 82 bpm. On pulmonary auscultation: diffuse and bilateral crackles. Lung ultrasound showed a bilateral B line and the echocardiogram demonstrated a pericardial effusion with signs of tamponade. A pericardiocentesis evacuated 620 ml of hemorrhagic fluid and the patient was transferred to the intensive care unit, hemodynamically stable, with SaO2 95%. At the ICU the echocardiogram, showed resolution of the cardiac tamponade and a tumor adhered to the lateral wall of the left ventricle. Chest CT demonstrated: a left lung tumor, infiltrating the pericardial sac. A pericardium biopsy demonstrated undifferentiated carcinoma. Discussion: Cardiac tamponade diagnosis requires a high level of suspicion. Respiratory failure, chest pain, and shock, observed in cardiac tamponade, are also present in different diseases. The most common finding of cardiac tamponade is dyspnea (78% of cases). Our patient had dyspnea due to pulmonary edema, secondary to left ventricle diastolic dysfunction caused by the tamponade. A bedside echocardiogram made the diagnosis of cardiac tamponade and guided the effective pericardiocentesis. Conclusion: Cardiac tamponade must be suspected in all cases of acute dyspnea. Echocardiogram is the method of choice for the diagnosis and for guiding the pericardiocentesis.
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Bagno, Andrea, Federico Anzil, Vincenzo Tarzia, Vittorio Pengo, Alfredo Ruggeri i Gino Gerosa. "Application of Wavelet Analysis to the Phonocardiographic Signal of Mechanical Heart Valve Closing Sounds". International Journal of Artificial Organs 32, nr 3 (marzec 2009): 166–72. http://dx.doi.org/10.1177/039139880903200307.
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Heart valve disorders, caused by congenital defects, rheumatic fever, calcification, myocardial infarction and other cardiovascular diseases, often require native valves to be replaced by bio-prosthetic devices or mechanical heart valves (MHVs). Among MHVs, bileaflet valves are usually preferred for their hemodynamic features, similar to physiological ones, and their durability, but they are prone to complications due to thromboembolic events. Due to the asynchronous closure of the leaflets, bileaflet MHVs are also known to produce closing sounds typically characterized by the presence of two peaks in the time domain. The detection of this “double click” in the signal may be useful for the early diagnosis of bileaflet MHV malfunction. The closing sound is actually a non-stationary signal that can be properly explored by means of time-frequency analysis. This paper describes a preliminary approach to the investigation of bileaflet MHV closing sounds performed by Continuous Wavelet Transform (CWT) analysis. Signals were collected from 3 patients immediately after surgery by means of the Myotis 3C, which is a traditional phonocardiographic apparatus. Signals were analyzed by two algorithms: one embedded in the Myotis 3C, based on the Fast Fourier Transform (FFT); and one specifically created for the purposes of the present study, based on CWT. The performance of these algorithms was compared and the results showed that the proposed CWT technique correctly classifies as “double” a large number of clicks that are recognized as “single” by the Myotis 3C.
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Tripathy, Subhranshu Sekhar, Agbotiname Lucky Imoize, Mamata Rath, Niva Tripathy, Sujit Bebortta, Cheng-Chi Lee, Te-Yu Chen, Stephen Ojo, Joseph Isabona i Subhendu Kumar Pani. "A Novel Edge-Computing-Based Framework for an Intelligent Smart Healthcare System in Smart Cities". Sustainability 15, nr 1 (31.12.2022): 735. http://dx.doi.org/10.3390/su15010735.
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The wide use of internet-enabled devices has not left the healthcare sector untouched. The health status of each individual is being monitored irrespective of his/her medical conditions. The advent of such medical devices is beneficial not only for patients but also for physicians, hospitals, and insurance companies. It makes healthcare fast, reliable, and hassle-free. People can keep an eye on their blood pressure, pulse rate, etc., and thus take preventive measures on their own. In hospitals, too, the Internet of Things (IoT) is being deployed for various tasks such as monitoring oxygen and blood sugar levels, electrocardiograms (ECGs), etc. The IoT in healthcare also reduces the cost of various ailments through fast and rigorous data analysis. The prediction of diseases through machine-learning techniques based on symptoms has become a promising concept. There may also be a situation where real-time analysis is required. In such a latency-sensitive situation, fog computing plays a vital role. Establishing communication every time with the cloud is not required with the introduction of fog and thus the latency is reduced. Healthcare is a latency-sensitive application area. So, the deployment of fog computing in this area is of vital importance. Our work focuses on improving the efficiency of the system for the precise diagnosis of and recommendations for heart disease. It evaluates the system using a machine-learning module.
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Zhuravleva, Nadezhda V., Vadim E. Babokin, Elena V. Barsukova, Luiza M. Karzakova, Rosa V. Fomina, Nadezhda A. Komelyagina, Tatyana L. Smirnova i in. "THE EFFECT OF COVID-19 ON MYOCARDIAL DAMAGE: A CLINICAL CASE". Acta medica Eurasica, nr 2 (30.06.2022): 31–39. http://dx.doi.org/10.47026/2413-4864-2022-2-31-39.
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Despite the fact that the general clinical manifestations of COVID-19 are well known, there remain problems associated with the impact of COVID-19 on human health, in addition to its effects on the respiratory system. Patients with COVID-19 and concomitant cardiovascular diseases are more likely to be hospitalized and to pass treatment in the intensive care units and to have worse prognoses. The article discusses the problems arising from the effect of type 2 coronavirus acute respiratory syndrome (SARS-CoV-2) on the cardiovascular system, starting with the mechanisms associated with angiotensin converting enzyme 2 (ACE2) receptors, as well as discusses cases of major pathological changes in the heart and blood vessels that are detected in these patients. In addition to the known risk factors for severe COVID-19: cardiovascular diseases, diabetes mellitus, chronic lung diseases and old age, even young patients without a history of risk factors may develop myocardial damage. We present a description of a clinical case of acute myocardial infarction against the background of a new coronavirus infection COVID-19 in a patient aged 28 years with a diagnosis of coronary heart disease.
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Lopukhov, S. V., i E. V. Filippov. "PREMATURE OVARIAN FAILURE, ITS CONSEQUENCES, MORTALITY AND IMPACT ON THE CARDIOVASCULAR SYSTEM". NAUKA MOLODYKH (Eruditio Juvenium) 9, nr 1 (31.03.2021): 147–56. http://dx.doi.org/10.23888/hmj202191147-156.
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This review focuses on the topic of premature ovarian failure (POF) as highly relevant in modern medicine (up to 2% of women in the population suffer from this disease). However, patients with premature ovarian failure not only are still not receiving any treatment, but even making this diagnosis is very difficult. Even after a correct diagnosis is made, these patients are not followed up, despite the fact they have already developed a hormonal imbalance. These women develop two groups of complications: short-term complications associated with a rapid estrogen deficiency in the body, and much more dangerous long-term complications affecting multiple organs and even systems. But in the meanwhile, women with premature ovarian failure are under increased risk of death from all causes, in particular from coronary heart disease (CHD), respiratory diseases, genitourinary diseases and from external causes. And this is despite the fact that cardio-vascular diseases (CVD) are already the leading cause of death among women worldwide. It is women with POF that are at the highest risk of development of cardiovascular diseases, compared to women with normal menopause. These patients, therefore, constitute one of the most important groups to be targeted by screening and prevention strategies primarily for cardiovascular diseases. These strategies should include the use of risk stratification tools to identify women that need lifestyle modifying and pharmacological therapy to prevent development of such diseases in them. This is the only way to maintain a high quality of life in these women over the long term.