Relevant bibliographies by topics / Fast Diagnosis of Heart Diseases

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Fast Diagnosis of Heart Diseases'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "Fast Diagnosis of Heart Diseases"

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Saikumar K, Rajesh V, Hasane Ahammad S K, Sai Krishna M, Sai Pranitha G, and Ajay Kumar Reddy R. "Cab for Heart Diagnosis with RFO Artificial Intelligence Algorithm." International Journal of Research in Pharmaceutical Sciences 11, no. 1 (February 8, 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, and 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, no. 3 (July 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 and Rajesh V. "Coronary blockage of artery for Heart diagnosis with DT Artificial Intelligence Algorithm." International Journal of Research in Pharmaceutical Sciences 11, no. 1 (January 10, 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, and Safdar Tanweer. "Fast Pattern Discovery in Healthcare Data Using Graphics Processors." Journal of Drug Delivery and Therapeutics 9, no. 1-s (February 21, 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, and Oumar Niang. "Arrhythmias Prediction Using an Hybrid Model Based on Convolutional Neural Network and Nonlinear Regression." International Journal of Computational Intelligence and Applications 19, no. 03 (September 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, and I. G. Nikitin. "The value of copeptin in the diagnosis and prognosis of cardiovascular diseases." Clinical Medicine (Russian Journal) 98, no. 3 (July 16, 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, and Avani Vasant. "A novel salp swarm clustering algorithm for prediction of the heart diseases." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 1 (January 1, 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, and Hasan Hadi Khaleel. "Comparison of various data mining methods for early diagnosis of human cardiology." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 3 (September 1, 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, and K. R. Shobha. "Application of Artificial Neural Networks for Heart Disease Prediction." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 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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Dissertations / Theses on the topic "Fast Diagnosis of Heart Diseases"

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Verhoek, Michael. "Fast segmentation of the LV myocardium in real-time 3D echocardiography." Thesis, University of Oxford, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.566050.

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Heart disease is a major cause of death in western countries. In order to diagnose and monitor heart disease, 3D echocardiography is an important tool, as it provides a fast, relatively low-cost, portable and harmless way of imaging the moving heart. Segmentation of cardiac walls is an indispensable method of obtaining quantitative measures of heart function. However segmentation of ultrasound images has its challenges: image quality is often relatively low and current segmentation methods are often not fast. It is desirable to make the segmentation technique as fast as possible, making quantitative heart function measures available at the time of recording. In this thesis, we test two state-of-the-art fast segmentation techniques to address this issue; furthermore, we develop a novel technique for finding the best segmentation propagation strategy between points of time in a cardiac image sequence. The first fast method is Graph Cuts (GC), an energy minimisation technique that represents the image as a graph. We test this method on static 3D echocardiography to segment the myocardium, varying the importance of the regulariser function. We look at edge measures, position constraints and tissue characterisation and find that GC is relatively fast and accurate. The second fast method is Random Forests (RFos), a discriminative classifier using binary decision trees, used in machine learning. To our knowledge, we are the first to test this method for myocardial segmentation on 2D and 3D static echocardiography. We investigate the number of trees, image features used, some internal parameters, and compare with intensity thresholding. We conclude that RFos are very fast and more accurate than GC segmentation. The static RFo method is subsequently applied to all time frames. We describe a novel optical flow based propagation technique that improves the static results by propagating the results from well-performing time frames to less-performing frames. We describe a learning algorithm that learns for each frame which propagation strategy is best. Furthermore, we look at the influence of the number of images and of the training set available per tree, and we compare against other methods that use motion information. Finally, we perform the same propagation learning method on the static GC results, concluding that the propagation method improves the static results in this case as well. We compare the dynamic GC results with the dynamic RFo results and find that RFos are more accurate and faster than GC.
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Alsalamah, Mashail. "Heart diseases diagnosis using artificial neural networks." Thesis, Coventry University, 2017. http://curve.coventry.ac.uk/open/items/a9564d2b-df62-4573-8888-cabdbbdcd4e0/1.

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Information technology has virtually altered every aspect of human life in the present era. The application of informatics in the health sector is rapidly gaining prominence and the benefits of this innovative paradigm are being realized across the globe. This evolution produced large number of patients’ data that can be employed by computer technologies and machine learning techniques, and turned into useful information and knowledge. This data can be used to develop expert systems to help in diagnosing some life-threating diseases such as heart diseases, with less cost, processing time and improved diagnosis accuracy. Even though, modern medicine is generating huge amount of data every day, little has been done to use this available data to solve challenges faced in the successful diagnosis of heart diseases. Highlighting the need for more research into the usage of robust data mining techniques to help health care professionals in the diagnosis of heart diseases and other debilitating disease conditions. Based on the foregoing, this thesis aims to develop a health informatics system for the classification of heart diseases using data mining techniques focusing on Radial Basis functions and emerging Neural Networks approach. The presented research involves three development stages; firstly, the development of a preliminary classification system for Coronary Artery Disease (CAD) using Radial Basis Function (RBF) neural networks. The research then deploys the deep learning approach to detect three different types of heart diseases i.e. Sleep Apnea, Arrhythmias and CAD by designing two novel classification systems; the first adopt a novel deep neural network method (with Rectified Linear unit activation) design as the second approach in this thesis and the other implements a novel multilayer kernel machine to mimic the behaviour of deep learning as the third approach. Additionally, this thesis uses a dataset obtained from patients, and employs normalization and feature extraction means to explore it in a unique way that facilitates its usage for training and validating different classification methods. This unique dataset is useful to researchers and practitioners working in heart disease treatment and diagnosis. The findings from the study reveal that the proposed models have high classification performance that is comparable, or perhaps exceed in some cases, the existing automated and manual methods of heart disease diagnosis. Besides, the proposed deep-learning models provide better performance when applied on large data sets (e.g., in the case of Sleep Apnea), with reasonable performance with smaller data sets. The proposed system for clinical diagnoses of heart diseases, contributes to the accurate detection of such disease, and could serve as an important tool in the area of clinic support system. The outcome of this study in form of implementation tool can be used by cardiologists to help them make more consistent diagnosis of heart diseases.
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梁平 and Ping Maurice Leung. "The role of cross-sectional and pulsed Doppler echocardiography in themanagement of patients with congenital heart disease: a changing practice." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1991. http://hub.hku.hk/bib/B30408908.

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Cho, Jinsoo. "Velocity-based cardiac segmentation and motion-tracking." Diss., Available online, Georgia Institute of Technology, 2004:, 2003. http://etd.gatech.edu/theses/available/etd-04082004-180106/unrestricted/cho%5Fjinsoo%5F200312%5Fphd.pdf.

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Tan, Zhen. "Low noise heart sound acquisition in wearable system for individual-centered CVD diagnosis." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691773.

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Hansson, Kerstin. "Diagnostic imaging of cardiopulmonary structures in normal dogs and dogs with mitral regurgitation /." Uppsala : Dept. of Biomedicine and Veterinary Public Health, Division of Diagnostic Imaging and Clinical Pathology, Swedish Univ. of Agricultural Sciences, 2004. http://epsilon.slu.se/v167.pdf.

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Sitt, Wing-hung Edward, and 薛穎雄. "Is the validity of non-invasive computerized tomography coronary angiography equivalent to invasive coronary angiography for theevaluation of coronary artery disease." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39724578.

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Hui, Ling, and 許凌. "Dobutamine stress echocardiography for children with acquired and congenital cardiac diseases." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B29914954.

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Wang, Yan, and 王焱. "Atherosclerotic disease of the carotid, coronary and renal arteries: diagnosis, angioplasty and the effect ofstent surface on early thrombosis and restenosis." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B31246060.

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Pontre, Beau. "Measurement, modelling and potential clinical applications of spatial variations in magnetic resonance proton transverse relaxation rates in iron-loaded liver and heart tissue." University of Western Australia. School of Physics, 2006. http://theses.library.uwa.edu.au/adt-WU2006.0062.

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[Truncated abstract. Formulae and special characters in this field can only be approximated. See PDF version for accurate reproduction.] Magnetic resonance imaging (MRI) has been developed over the past two and a half decades to enable non-invasive assessment of soft tissues in the human body. MRI provides images of the tissues in the body with intensities weighted by nuclear magnetic relaxation properties of the tissue. Recent advances have utilised MRI as a quantitative tool with the nuclear magnetic relaxation rates in tissues being accurately quantified. One clinical application of quantitative MRI has been in the quantification of body iron stores in the management of iron overload diseases. MR images also contain information about the spatial variations of relaxation rates, which could be clinically useful. In the quantification of liver iron concentrations, proton transverse relaxation rate (R2) maps have been used not only to quantify iron concentrations but also to visualise the spatial variations. The work in this thesis addresses the use of spatial information from proton transverse relaxation rate maps in clinical practice. The quantitative spatial information contained in these maps is analysed in two clinically important settings, namely the non-invasive assessment of liver fibrosis and the assessment of magnetic susceptibility artefacts in cardiac proton transverse relaxometry. Spatial distributions of liver R2 maps were quantified using texture measures based on grey-tone spatial dependence (GTSD) matrices. Some of these measures gave a statistically significant distinction between patients with minimal or no fibrosis and those with fibrosis or cirrhosis. Distinction of fibrosis using this technique was enhanced in subjects with iron overload diseases, suggesting that iron is required as a contrast agent for sufficient sensitivity of image texture to fibrosis. In subjects with low tissue iron concentrations, tissue hydration was observed to also have an influence on R2. In patients with end stage liver disease, a model combining tissue iron concentration and tissue hydration gave a better prediction of R2 than iron concentration alone. A model combining several of the texture measures was developed using logistic regression and was found to improve distinction of high-grade fibrosis from low-grade fibrosis. For the distinction of F0 and F1 fibrosis stages (as assessed by the METAVIR system) from F2 and above the area under the receiver-operating characteristic (ROC) curve was 0.75. As this model was developed using a cohort of subjects with varying pathologies, the performance of the model is expected to improve if only iron-loaded subjects are considered.
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Books on the topic "Fast Diagnosis of Heart Diseases"

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Diagnosis of heart disease. New York: Springer-Verlag, 1991.

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László, Mihóczy, ed. Non-invasive cardiac diagnosis. Budapest: Akadémiai Kiadó, 1988.

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T, Basson Craig, and Lerman Bruce B, eds. Structural heart disease. New York: Demos Medical Pub., 2010.

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Chung, Edward K. Pocket guide to ECG diagnosis. Cambridge, Mass., USA: Blackwell Science, 1996.

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Abrams, Jonathan. Essentials of cardiac physical diagnosis. Philadelphia: Lea & Febiger, 1987.

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Essentials of cardiac physical diagnosis. Philadelphia: Lea & Febiger, 1987.

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Vecht, Romeo. ECG Diagnosis Made Easy. London: Taylor & Francis Group Plc, 2004.

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Council on Clinical Cardiology (American Heart Association), ed. Examination of the heart. Dallas: American Heart Association, 1990.

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Topics in structural heart disease. New York: Demos Medical Pub., 2010.

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T, Basson Craig, and Lerman Bruce B, eds. Topics in structural heart disease. New York: Demos Medical Pub., 2010.

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Book chapters on the topic "Fast Diagnosis of Heart Diseases"

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Ullah, Mohammad Aman, Saifa Shahid, and Zinat Sultana. "Machine Learning Based Diagnosis System for Easy and Fast Heart Disease Prediction." In Interdisciplinary Research in Technology and Management, 426–31. London: CRC Press, 2021. http://dx.doi.org/10.1201/9781003202240-67.

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Zuluaga, Maria A., Marcela Hernández Hoyos, Julio C. Dávila, Luis F. Uriza, and Maciej Orkisz. "A Fast Lesion Registration to Assist Coronary Heart Disease Diagnosis in CTA Images." In Computer Vision and Graphics, 710–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33564-8_85.

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Fowler, Noble O. "Pericardial Diseases." In Diagnosis of Heart Disease, 292–313. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-3068-7_23.

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Fowler, Noble O. "Aortic Diseases." In Diagnosis of Heart Disease, 375–88. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-3068-7_27.

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Taqatqa, Anas Saleh Lutfi, Umang Gupta, Ra-id Abdulla, and Ziyad M. Hijazi. "Cardiac Catheterization in Children: Diagnosis and Therapy." In Heart Diseases in Children, 67–88. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-7994-0_5.

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Fan, Guoguang. "Differential Diagnosis of Heart and Aorta Diseases." In Atlas of Differential Diagnosis, 285–95. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9763-0_15.

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Hamilton, Robert M. "The Diagnosis of Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC) in Children." In Congenital Diseases in the Right Heart, 131–37. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84800-378-1_17.

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Ahmad, Shoaib, Ahmed Dheyaa Al-Obaidi, Abeer Mundher Ali, and Sara Shihab Ahmad. "Diagnosis of CHD During Perinatal Life." In Clinical and Surgical Aspects of Congenital Heart Diseases, 15–19. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-23062-2_3.

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Rosado-de-Christenson, Melissa L., and Jeffrey S. Klein. "A Systematic Approach to Chest Radiographic Diagnosis." In Diseases of the Chest and Heart 2015–2018, 94–100. Milano: Springer Milan, 2015. http://dx.doi.org/10.1007/978-88-470-5752-4_12.

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Kim, Tae Seon, and Hyun-Dong Kim. "Context-Aware Computing Based Adaptable Heart Diseases Diagnosis Algorithm." In Lecture Notes in Computer Science, 284–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11552451_38.

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Conference papers on the topic "Fast Diagnosis of Heart Diseases"

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Lai, James C. K., Marco P. Schoen, Arya Ebrahimpour, Alok Bhushan, Christopher K. Daniels, and Solomon W. Leung. "Fast-Response Smart Self-Assembling Biosensors for Biomarker Detection." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-42100.

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The development of biosensors has been astronomical with the advent of the rapid growth of nanomaterials and nanotechnology. Nanobiosensors are becoming ubiquitous in numerous biomedical applications. Thus, there is a great impetus to exploit smart nanoparticles and other nanomaterials for designing and fabricating smart nanobiosensors that are ultrasensitive and biocompatible. We are developing smart self-assembling biosensors that can detect specific biomolecules (e.g., enzymes, cofactors, metabolites, drugs, hormones, etc.) from micro- to nanomolar levels. Applications of the biosensors include detection of organ dysfunction and/or failure (e.g., liver malfunction, heart failure, etc.), early detection of malignant cancers, toxicant identification, and other biomarkers of diseases. Although nanobiosensors that possess high sensitivity and specificity have been designed and marketed, one fundamental issue remains to be resolved. This important issue is one concerning biocompatibility. Thus, in our development of smart biosensors using nanomaterials, we have adopted a dual purpose approach. (i) On the one hand, it is necessary to systematically and comprehensively evaluate the material properties, characterize and model the signal sensing ability, and determine the biocompatibility of materials to be employed for the design of nanobiosensors. (ii) On the other hand, it is imperative to identify the ideal criteria for the designs of fast-response smart self-assembling nanobiosensors for biomarker detection. Based on a critical review of the literature and consideration of the biocompatibility, functional characterization, and other related issues discussed above, we have identify a set of criteria for the design of fast-response smart self-assembling nanobiosensors for detection of multiple biomarkers. We have also identified many biomedical areas where such nanobiosensors can be applied to detect biomarkers for various diseases. Our dual purpose approach will ultimately lead to the design of much more biocompatible and highly sensitive nanobiosensors and diagnostic equipment (nanobiosensor arrays).
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Patel, Ankeeta R., and Maulin M. Joshi. "Heart diseases diagnosis using neural network." In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT). IEEE, 2013. http://dx.doi.org/10.1109/icccnt.2013.6726740.

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Shamsuddin, Noraishah, and Mohd Nasir Taib. "Diagnosis of heart diseases using nonlinear ARX model." In its Applications (CSPA). IEEE, 2011. http://dx.doi.org/10.1109/cspa.2011.5759908.

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Santos, Hicaro S., Odilon O. Dutra, Edmilson M. Moreira, and Luis H. C. Ferreira. "Android framework for automatic diagnosis of heart diseases." In 2017 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE, 2017. http://dx.doi.org/10.1109/memea.2017.7985878.

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Khan, Rooh Ullah, Tassadaq Hussain, Hanan Quddus, Amna Haider, Adnan Adnan, and Zahid Mehmood. "An Intelligent Real-time Heart Diseases Diagnosis Algorithm." In 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). IEEE, 2019. http://dx.doi.org/10.1109/icomet.2019.8673506.

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Makram, Manal, Nisreen Ali, and Ammar Mohammed. "Machine Learning Approach for Diagnosis of Heart Diseases." In 2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC). IEEE, 2022. http://dx.doi.org/10.1109/miucc55081.2022.9781735.

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Krželj, Vjekoslav, and Ivana Čulo Čagalj. "INHERITED METABOLIC DISORDERS AND HEART DISEASES." In Symposium with International Participation HEART AND … Akademija nauka i umjetnosti Bosne i Hercegovine, 2019. http://dx.doi.org/10.5644/pi2019.181.02.

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Inherited metabolic disorders can cause heart diseases, cardiomyopathy in particular, as well as cardiac arrhythmias, valvular and coronary diseases. More than 40 different inherited metabolic disorders can provoke cardiomyopathy, including lysosomal storage disorders, fatty acid oxidation defects, organic acidemias, amino acidopathies, glycogen storage diseases, congenital disorders of glycosylation as well as peroxisomal and mitochondrial disorders. If identified and diagnosed on time, some of congenital metabolic diseases could be successfully treated. It is important to assume them in cases when heart diseases are etiologically undefined. Rapid technological development has made it easier to establish the diagnosis of these diseases. This article will focus on common inherited metabolic disorders that cause heart diseases, as well as on diseases that might be possible to treat.
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EL-Bouridy, Mohamed E., and Amira Salah EL-Batouty. "An Intelligent High Accuracy & hybrid Identification for Heart diseases Diagnosis." In 2021 International Telecommunications Conference (ITC-Egypt). IEEE, 2021. http://dx.doi.org/10.1109/itc-egypt52936.2021.9513892.

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Alslatie, Mohammad, Hiam Alquran, Wan Azani Mustafa, Isam Abu-Qasmieh, Ali Mohammad Alqudah, and Ahmed Alkhayyat. "Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Images." In 2022 5th International Conference on Engineering Technology and its Applications (IICETA). IEEE, 2022. http://dx.doi.org/10.1109/iiceta54559.2022.9888399.

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Guyette, Jacques, Zewei Tao, Angelica DeMartino, Melissa Kuhn, Marsha Rolle, George Pins, and Glenn R. Gaudette. "Delivering Stem Cells to the Heart on Biological Sutures: Effects on Regional Mechanical Function." In ASME 2011 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2011. http://dx.doi.org/10.1115/sbc2011-53680.

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Heart failure (HF) affects over 5 million people in the US alone,1 costs society over $10B per year in medical care costs,2 and is the single most common hospital discharge diagnosis.3 Despite our advances in many areas of cardiovascular disease, we have made little progress in treating HF,4 largely due to the fact that few treatments actually aim to treat myocardial infarction (MI), which is the underlying cause of most cases of HF. Once a patient exhibits HF, their long-term survival is in jeopardy, exhibiting less than 50% probability of survival 5 years after diagnosis.4
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Reports on the topic "Fast Diagnosis of Heart Diseases"

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FEDOTKINA, S. A., O. V. MUZALEVA, and E. V. KHUGAEVA. RETROSPECTIVE ANALYSIS OF THE USE OF TELEMEDICINE TECHNOLOGIES FOR THE PREVENTION, DIAGNOSIS AND TREATMENT OF HYPERTENSION. Science and Innovation Center Publishing House, 2021. http://dx.doi.org/10.12731/978-0-615-67320-2-4-22.

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Introduction. The economic losses associated with disability due to diseases of the circulatory system, as well as the costs of providing medical care to patients suffering from heart and vascular diseases, are increasing annually. The state preventive measures currently being carried out are of a delayed nature. The results of the medical examination of the population of the Russian Federation in recent years (2015-2019) indicate that the incidence of cardiovascular diseases, including hypertension, is at a fairly high level. In the middle of the last century, the Concept of risk factors for the development of chronic non-communicable diseases were formulated, in the structure of which cardiovascular diseases, including arterial hypertension, occupies one of the primary positions. The concept is based on the results of promising epidemiological studies, and, at present, is a methodological basis for planning and organizing primary prevention of cardiovascular diseases. The purpose of the study. Based on the analysis of literary sources (including foreign ones) containing experience in the use of telemedicine technologies, to assess their significance for the prevention, diagnosis and treatment of hypertension, as well as forecasting improvements in the quality of medical care when adapting to the use of clinical recommendations. Materials and methods. The article provides an analytical review of the use of modern telemedicine technologies in the prevention of hypertension. The results of the study and their discussion. The analysis of literary sources has shown that in the context of the progress of information and telecommunication technologies in the healthcare system, a fundamentally new direction has appeared in the organization and provision of medical care to the population - telemedicine, which will ensure the modern level of prevention, detection and treatment of chronic non-communicable diseases, and also determines positive medical, social and economic performance indicators. To date, updates in the legislative framework of the Russian Federation are aimed at ensuring that medical care with the use of telemedicine technologies is more widespread, taking into account the standards of medical care and clinical recommendations. Conclusion. Based on a review of literature sources, it has been established that the modern solution to the problem of improving the quality of medical care for patients, including those with hypertension, diseases is medical care using telemedicine technologies that prove their medical, social and economic effectiveness.
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Weng, JIeqiong, Jingfang Zhang, Ke Xu, Mengfei Yuan, Tingting Yao, Xinyu Wang, and Xiaoxu Shen. Efficacy of Shexiang Baoxin Pills Combined with Statins on Blood Lipid Profile in Patients with Coronary Heart Disease: A systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, September 2022. http://dx.doi.org/10.37766/inplasy2022.9.0100.

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Review question / Objective: P（Population) ：Patients with coronary heart disease; I（Intervention) : Statins treatment in combination with Shexiang Baoxin pill; C（Comparison): Statins alone; O（Outcome）: Improvement of symptoms and blood lipids; S（Study design）：Clinical randomized trials. Eligibility criteria: To be included, trials were required to meet the following criteria: (1) patients were included in the studies according to diagnostic criteria of coronary heart disease established by the WHO, InternationalSociety of Cardiology and Association (ISCA), Internal Medicine, 7th edition ( IM-7th), Practice of InternalMedicine, 14th edition ( PIM-14th), Guidelines for the Diagnosis of Cardiovascular Diseases in InternalMedicine, 3rd edition (GIM-3rd) or conventional diagnostic criteria (CDC) including assessment of anginapectoris and electrocardiogram (ECG) results; (2) the study was conducted as a randomized controlled trial.
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