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Journal articles on the topic 'ECG diagnostic systems'
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Vijendra, V., and Meghana Kulkarni. "Fuzzy Controlled ID Interpretation Based ECG Diagnostic Systems." Advanced Science Letters 23, no. 3 (March 1, 2017): 1734–40. http://dx.doi.org/10.1166/asl.2017.8553.
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Satija, Udit, Barathram Ramkumar, and M. Sabarimalai Manikandan. "An automated ECG signal quality assessment method for unsupervised diagnostic systems." Biocybernetics and Biomedical Engineering 38, no. 1 (2018): 54–70. http://dx.doi.org/10.1016/j.bbe.2017.10.002.
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Zywietz, Chr, J. H. van Bemmel, and R. Degani. "Evaluation of ECG Interpretation Systems: Signal Analysis." Methods of Information in Medicine 29, no. 04 (1990): 298–307. http://dx.doi.org/10.1055/s-0038-1634795.
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AbstractPerformance analysis of biosignal processing systems which provide diagnostic statements requires particular care. Besides general accuracy requirements, psychological and legal implications for patient and physician have to be considered on both the development and the user sites. Cybernetics and control engineering have provided the basic methodology for performance analysis of systems: in technical systems often mathematically defined functions and signals can be fed into the system to be tested and its response and output provide the necessary performance characteristics after adequate mathematical analysis. For systems which process biosignals, as for example ECG analysis systems, instead of analytically given signals learning and test sets of data derived from patients have to be applied. The performance analysis is done on a statistical basis. In this paper construction and composition of learning and test data sets as well as methods for performance evaluation of the signal pocessing part of ECG programs are described. Specific reference is made to the European project Common Standards for Quantitative Electrocardiography (CSE) where ten ECG- and nine VCG-programs have been tested. The results of these tests provide reference data and standards for further program development as well as for independent system performance evaluation.
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Vimal, C., and B. Sathish. "Random Forest Classifier Based ECG Arrhythmia Classification." International Journal of Healthcare Information Systems and Informatics 5, no. 2 (April 2010): 1–10. http://dx.doi.org/10.4018/jhisi.2010040101.
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Heart Rate Variability (HRV) analysis is a non-invasive tool for assessing the autonomic nervous system and for arrhythmia detection and classification. This paper presents a Random Forest classifier based diagnostic system for detecting cardiac arrhythmias using ECG data. The authors use features extracted from ECG signals using HRV analysis and DWT for classification. The experimental results indicate that a prediction accuracy of more than 98% can be obtained using the proposed method. This system can be further improved and fine-tuned for practical applications.
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Nikolsky, A. V., V. M. Levanov, D. V. Drozdov, and A. A. Kozlov. "Patients’ selfoperated telemedical solutions for ecg screening." Medical alphabet 2, no. 12 (November 26, 2019): 25–28. http://dx.doi.org/10.33667/2078-5631-2019-2-12(387)-25-28.
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Currently, a number of manufacturers offer devices for self-registration of ECG and other parameters of the cardiovascular system (CVS) by patients and signal analysis using telemedicine technologies. This makes it possible to create medical services based on mobile remote monitoring. The purpose of the article: a review of existing telemedicine solutions for individual ECG recording and related mobile applications and server-side data analysis software for assessing applicability in functional diagnostics and cardiology services. The article highlights the history of the development of methods for telemedicine analysis of ECG, provides a comparative review of modern solutions for medical cardioregistration. Findings. 1. Individual ECG telemonitoring is a promising technology that is comparable in terms of diagnostic capabilities to assess cardiac rhythm disturbances with Holter ECG monitoring and multifunctional monitoring implanted with ECG loopback recorders. The main vector of development of individual ECG telemonitoring systems is related to the automation of ECG analysis both on the server side and in the patient’s mobile application, for this the application of artificial intelligence and big data (bigdata) is promising. 2. Telecardiogram of an electrocardiogram promotes closer contact of the patient and medical service at the minimum expenses of time for such interaction.
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Epstein, Richard H., Yuel-Kai Jean, Roman Dudaryk, Robert E. Freundlich, Jeremy P. Walco, Dorothee A. Mueller, and Shawn E. Banks. "Natural Language Mapping of Electrocardiogram Interpretations to a Standardized Ontology." Methods of Information in Medicine 60, no. 03/04 (September 2021): 104–9. http://dx.doi.org/10.1055/s-0041-1736312.
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Abstract Background Interpretations of the electrocardiogram (ECG) are often prepared using software outside the electronic health record (EHR) and imported via an interface as a narrative note. Thus, natural language processing is required to create a computable representation of the findings. Challenges include misspellings, nonstandard abbreviations, jargon, and equivocation in diagnostic interpretations. Objectives Our objective was to develop an algorithm to reliably and efficiently extract such information and map it to the standardized ECG ontology developed jointly by the American Heart Association, the American College of Cardiology Foundation, and the Heart Rhythm Society. The algorithm was to be designed to be easily modifiable for use with EHRs and ECG reporting systems other than the ones studied. Methods An algorithm using natural language processing techniques was developed in structured query language to extract and map quantitative and diagnostic information from ECG narrative reports to the cardiology societies' standardized ECG ontology. The algorithm was developed using a training dataset of 43,861 ECG reports and applied to a test dataset of 46,873 reports. Results Accuracy, precision, recall, and the F1-measure were all 100% in the test dataset for the extraction of quantitative data (e.g., PR and QTc interval, atrial and ventricular heart rate). Performances for matches in each diagnostic category in the standardized ECG ontology were all above 99% in the test dataset. The processing speed was approximately 20,000 reports per minute. We externally validated the algorithm from another institution that used a different ECG reporting system and found similar performance. Conclusion The developed algorithm had high performance for creating a computable representation of ECG interpretations. Software and lookup tables are provided that can easily be modified for local customization and for use with other EHR and ECG reporting systems. This algorithm has utility for research and in clinical decision-support where incorporation of ECG findings is desired.
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Khalid Ibrahim, Mohammed, Ahmed A. Hamad, Murad Obaid Abed, and Riyadh Abdulhamza Mohammed. "Review: Recent Directions in ECG-FPGA Researches." Journal of University of Babylon for Engineering Sciences 27, no. 2 (June 10, 2019): 242–51. http://dx.doi.org/10.29196/jubes.v27i2.2344.
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The last few years witnessed an increased interest in utilizing field programmable gate array (FPGA) for a variety of applications. This utilizing derived mostly by the advances in the FPGA flexible resource configuration, increased speed, relatively low cost and low energy consumption. The introduction of FPGA in medicine and health care field aim generally to replace costly and usually bigger medical monitoring and diagnostic equipment with much smaller and possibly portable systems based on FPGA that make use of the design flexibility of FPGA. Many recent researches focus on FPGA systems to deal with the well-known yet very important electrocardiogram (ECG) signal aspects to provide acceleration and improvement in the performance as well as finding and proposing new ideas for such implementations. The recent directions in ECG-FPGA are introduced in this paper.
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Übeyli, Elif Derya. "Usage of eigenvector methods in implementation of automated diagnostic systems for ECG beats." Digital Signal Processing 18, no. 1 (January 2008): 33–48. http://dx.doi.org/10.1016/j.dsp.2007.05.005.
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Wilson, Fiona, Cliodhna McHugh, Caroline MacManus, Aaron Baggish, Christopher Tanayan, Satyajit Reddy, Meagan M. Wasfy, and Richard B. Reilly. "Diagnostic Accuracy of a Portable ECG Device in Rowing Athletes." Diagnostics 12, no. 10 (September 20, 2022): 2271. http://dx.doi.org/10.3390/diagnostics12102271.
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Background: Athletes can experience exercise-induced transient arrythmias during high-intensity exercise or competition, which are difficult to capture on traditional Holter monitors or replicate in clinical exercise testing. The aim of this study was to investigate the reliability of a portable single channel ECG sensor and data recorder (PluxECG) and to evaluate the confidence and reliability in interpretation of ECGs recorded using the PluxECG during remote rowing. Methods: This was a two-phase study on rowing athletes. Phase I assessed the accuracy and precision of heart rate (HR) using the PluxECG system compared to a reference 12-lead ECG system. Phase II evaluated the confidence and reliability in interpretation of ECGs during ergometer (ERG) and on-water (OW) rowing at moderate and high intensities. ECGs were reviewed by two expert readers for HR, rhythm, artifact and confidence in interpretation. Results: Findings from Phase I found that 91.9% of samples were within the 95% confidence interval for the instantaneous value of the changing exercising HR. The mean correlation coefficient across participants and tests was 0.9886 (σ = 0.0002, SD = 0.017) and between the two systems at elevated HR was 0.9676 (σ = 0.002, SD = 0.05). Findings from Phase II found significant differences for the presence of artifacts and confidence in interpretation in ECGs between readers’ for both intensities and testing conditions. Interpretation of ECGs for OW rowing had a lower level of reader agreement than ERG rowing for HR, rhythm, and artifact. Using consensus data between readers’ significant differences were apparent between OW and ERG rowing at high-intensity rowing for HR (p = 0.05) and artifact (p = 0.01). ECGs were deemed of moderate-low quality based on confidence in interpretation and the presence of artifacts. Conclusions: The PluxECG device records accurate and reliable HR but not ECG data during exercise in rowers. The quality of ECG tracing derived from the PluxECG device is moderate-low, therefore the confidence in ECG interpretation using the PluxECG device when recorded on open water is inadequate at this time.
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Cervigón, Raquel, Brian McGinley, Darren Craven, Martin Glavin, and Edward Jones. "The Effects of Compression on the Detection of Atrial Fibrillation in ECG Signals." Applied Sciences 11, no. 13 (June 25, 2021): 5908. http://dx.doi.org/10.3390/app11135908.
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Although Atrial Fibrillation (AF) is the most frequent cause of cardioembolic stroke, the arrhythmia remains underdiagnosed, as it is often asymptomatic or intermittent. Automated detection of AF in ECG signals is important for patients with implantable cardiac devices, pacemakers or Holter systems. Such resource-constrained systems often operate by transmitting signals to a central server where diagnostic decisions are made. In this context, ECG signal compression is being increasingly investigated and employed to increase battery life, and hence the storage and transmission efficiency of these devices. At the same time, the diagnostic accuracy of AF detection must be preserved. This paper investigates the effects of ECG signal compression on an entropy-based AF detection algorithm that monitors R-R interval regularity. The compression and AF detection algorithms were applied to signals from the MIT-BIH AF database. The accuracy of AF detection on reconstructed signals is evaluated under varying degrees of compression using the state-of-the-art Set Partitioning In Hierarchical Trees (SPIHT) compression algorithm. Results demonstrate that compression ratios (CR) of up to 90 can be obtained while maintaining a detection accuracy, expressed in terms of the area under the receiver operating characteristic curve, of at least 0.9. This highlights the potential for significant energy savings on devices that transmit/store ECG signals for AF detection applications, while preserving the diagnostic integrity of the signals, and hence the detection performance.
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van Herpen, G., J. H. van Bemmel, and J. A. Kors. "Methodology of the Modular ECG Analysis System MEANS." Methods of Information in Medicine 29, no. 04 (1990): 346–53. http://dx.doi.org/10.1055/s-0038-1634805.
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AbstractThe methodology, used in the Modular ECG Analysis System (MEANS) is described. MEANS consists of modules for signal analysis and diagnostic classification. The basic structure of the modular interpretation system remained intact over a period of 20 years, while all modules underwent many changes as a function of experience and insight, and the continuously changing information technology. The article describes the advantages of a modular approach to decision-support systems, the most important ones being easier maintenance of the software package and separate optimization and testing of each module. The overall evaluation of MEANS was done in the CSE study. Evaluation results for modules and for the entire system are presented.
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Минина, E. Minina, Файнзильберг, and L. Faynzilberg. "Phase Portrait of Single-Channel Ecg in Asseessment of Functional Reserves of Cardiovascular System." Journal of New Medical Technologies 21, no. 3 (September 5, 2014): 22–27. http://dx.doi.org/10.12737/5891.
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To study and simulate of dynamic behavior of complex biomedical systems the methods of chaos theory and synergetics are used. These methods provide an opportunity to adequately disclose and analyze the mechanisms the functioning of a living com-plex system, considering the phase trajectory in the state space. When various approaches to the study of the behavior of biological systems in phase space, can "produce" separate diagnostic characteristics that didn’t duplicated by other methods of analysis, and complement each other.
Found that the original features of the phase portrait of single-channel ECG device that can be computed by appliance "FAZAGRAF®" with finger electrodes have an additional diagnostic value in quantifying the level of functional reserves of the cardiovascular system, and also have practical significance in the differential diagnosis of functional status and reserves cardiovascular system in different populations in screening studies in clinical practice and sports medicine.
It was found that the original characteristics of the phase portrait single-channel ECG: βT, STR, αQRS and σQRS, and significantly different in the groups with different levels of functional reserves of the cardiovascular system and have additional diagnostic value. Dynamics of changes in the characteristics of the phase portrait single channel ECG with stepwise increasing load quantitatively reflects the differences in the level of functional reserves and orientation of compensatory and adaptive processes.
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Güler, İnan, and Elif Derya Übeyli. "Feature saliency using signal-to-noise ratios in automated diagnostic systems developed for ECG beats." Expert Systems with Applications 28, no. 2 (February 2005): 295–304. http://dx.doi.org/10.1016/j.eswa.2004.10.008.
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Georgieva-Tsaneva, G. "Application of Mathematical Methods for Analysis of Digital ECG Data." Information Technologies and Control 14, no. 2 (June 1, 2016): 35–44. http://dx.doi.org/10.1515/itc-2017-0005.
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AbstractThis paper presents several mathematical methods for analysis of electrocardiogram digital data. The measurement of beat to beat fluctuations known as Heart Rate Variability becomes a non-invasive diagnostic technique to study the cardiac autonomic regulation. The analysis was done by software developed by the author. The article presents the results of linear methods, nonlinear methods and wavelet analysis of Heart Rate Variability data in healthy and diseased subjects. The obtained results and the performed comparative analysis demonstrate the possibility for effective application of the considered methods in new cardiovascular information systems.
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Yin, Ming, Ru Tang, Miao Liu, Ke Han, Xiao Lv, Maolin Huang, Ping Xu, Yongdeng Hu, Baobao Ma, and Yanrong Gai. "Influence of Optimization Design Based on Artificial Intelligence and Internet of Things on the Electrocardiogram Monitoring System." Journal of Healthcare Engineering 2020 (October 26, 2020): 1–8. http://dx.doi.org/10.1155/2020/8840910.
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With the increasing emphasis on remote electrocardiogram (ECG) monitoring, a variety of wearable remote ECG monitoring systems have been developed. However, most of these systems need improvement in terms of efficiency, stability, and accuracy. In this study, the performance of an ECG monitoring system is optimized by improving various aspects of the system. These aspects include the following: the judgment, marking, and annotation of ECG reports using artificial intelligence (AI) technology; the use of Internet of Things (IoT) to connect all the devices of the system and transmit data and information; and the use of a cloud platform for the uploading, storage, calculation, and analysis of patient data. The use of AI improves the accuracy and efficiency of ECG reports and solves the problem of the shortage and uneven distribution of high-quality medical resources. IoT technology ensures the good performance of remote ECG monitoring systems in terms of instantaneity and rapidity and, thus, guarantees the maximum utilization efficiency of high-quality medical resources. Through the optimization of remote ECG monitoring systems with AI and IoT technology, the operating efficiency, accuracy of signal detection, and system stability have been greatly improved, thereby establishing an excellent health monitoring and auxiliary diagnostic platform for medical workers and patients.
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Monedero, Iñigo. "A novel ECG diagnostic system for the detection of 13 different diseases." Engineering Applications of Artificial Intelligence 107 (January 2022): 104536. http://dx.doi.org/10.1016/j.engappai.2021.104536.
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Song, Weibo. "A New Method for Refined Recognition for Heart Disease Diagnosis Based on Deep Learning." Information 11, no. 12 (November 28, 2020): 556. http://dx.doi.org/10.3390/info11120556.
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The proper evaluation of heart health requires professional medical experience. Therefore, in clinical diagnosis practice, the development direction is to reduce the high dependence of the diagnosis process on medical experience and to more effectively improve the diagnosis efficiency and accuracy. Deep learning has made remarkable achievements in intelligent image analysis technology involved in the medical process. From the aspect of cardiac diagnosis, image analysis can extract more profound and abundant information than sequential electrocardiogram (ECG) signals. Therefore, a new region recognition and diagnosis method model of a two-dimensional ECG (2D-ECG) signal based on an image format is proposed. This method can identify and diagnose each refined waveform in the cardiac conduction cycle reflected in the image format ECG signal, so as to realize the rapid and accurate positioning and visualization of the target recognition area and finally get the analysis results of specific diseases. The test results show that compared with the results obtained by a one-dimensional sequential ECG signal, the proposed model has higher average diagnostic accuracy (98.94%) and can assist doctors in disease diagnosis with better visualization effect.
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Le, Trung Q., Vibhuthi Chandra, Kahkashan Afrin, Sanjay Srivatsa, and Satish Bukkapatnam. "A Dynamic Systems Approach for Detecting and Localizing of Infarct-Related Artery in Acute Myocardial Infarction Using Compressed Paper-Based Electrocardiogram (ECG)." Sensors 20, no. 14 (July 17, 2020): 3975. http://dx.doi.org/10.3390/s20143975.
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Timely evaluation and reperfusion have improved the myocardial salvage and the subsequent recovery rate of the patients hospitalized with acute myocardial infarction (MI). Long waiting time and time-consuming procedures of in-hospital diagnostic testing severely affect the timeliness. We present a Poincare pattern ensemble-based method with the consideration of multi-correlated non-stationary stochastic system dynamics to localize the infarct-related artery (IRA) in acute MI by fully harnessing information from paper-based Electrocardiogram (ECG). The vectorcardiogram (VCG) diagnostic features extracted from only 2.5-s long paper ECG recordings were used to hierarchically localize the IRA—not mere localization of the infarcted cardiac tissues—in acute MI. Paper ECG records and angiograms of 106 acute MI patients collected at the Heart Artery and Vein Center at Fresno California and the 12-lead ECG signals from the Physionet PTB online database were employed to validate the proposed approach. We reported the overall accuracies of 97.41% for healthy control (HC) vs. MI, 89.41 ± 9.89 for left and right culprit arteries vs. others, 88.2 ± 11.6 for left main arteries vs. right-coronary-ascending (RCA) and 93.67 ± 4.89 for left-anterior-descending (LAD) vs. left-circumflex (LCX). The IRA localization from paper ECG can be used to timely triage the patients with acute coronary syndromes to the percutaneous coronary intervention facilities.
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Jortveit, Jarle, Andreas Früh, and Hans Henrik Odland. "Paroxysmal Tachycardia Diagnosed by ECG247 Smart Heart Sensor in a Previously Healthy Child." Case Reports in Pediatrics 2022 (March 27, 2022): 1–4. http://dx.doi.org/10.1155/2022/9027255.
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Supraventricular tachycardia (SVT) is the most common symptomatic heart rhythm disorder in children and adolescents. ECG recordings of the heart rhythm during episodes is necessary for the diagnosis and for the selection of treatment. However, conventional long-term ECG recording systems may miss the diagnosis due to the disease’s intermittent nature. Novel adhesive patch ECG monitors, like ECG247 Smart Heart Sensor, may represent new important diagnostic tools in children and adolescents with symptoms of heart rhythm disorders. We report a case of tachyarrhythmia in a previously healthy 12-year-old child.
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Gautham, A., and V. Karthik Raj. "DESIGNING OF A SINGLE ARM SINGLE LEAD ECG SYSTEM FOR WET AND DRY ELECTRODE: A COMPARISON WITH TRADITIONAL SYSTEM." Biomedical Engineering: Applications, Basis and Communications 28, no. 03 (June 2016): 1650021. http://dx.doi.org/10.4015/s1016237216500216.
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Electrocardiography is a non-invasive medical diagnostic procedure used to record the electrical activity of the heart as a waveform. An electrocardiogram (ECG) can be utilized to evaluate the electrical activity of the heart, also the rate and regularity of the heart beat and other related diagnoses. ECG systems have evolved along since its invention and researches are going on continuously to decrease the complexity of ECG systems. This paper discusses the designing of a single arm single lead ECG system to acquire ECG signals from areas of left arm alone. The proposed system uses pre-gelled disposable surface electrodes and dry copper electrodes. The single arm approach in ECG acquisition reduces the complexity of the system to a greater extent and also improves the ease of use and patient comfort. The paper discusses the various designing aspects and the working of the single arm single lead ECG system. A hardware only approach has been used here in the design of the ECG system. ECGs were obtained from 10 healthy subjects using the proposed system, which were compared along with ECG acquired from a commercially used system. The obtained ECG had morphological features similar to a normal ECG waveform. The results of the comparison were very promising and all the values recorded were in the normal range of values for the respective parameters in comparison.
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Kjeldsen, Sofie Troest, Sarah Dalgas Nissen, Rikke Buhl, and Charlotte Hopster-Iversen. "Paroxysmal Atrial Fibrillation in Horses: Pathophysiology, Diagnostics and Clinical Aspects." Animals 12, no. 6 (March 10, 2022): 698. http://dx.doi.org/10.3390/ani12060698.
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Atrial fibrillation (AF) is the most common arrhythmia in horses causing poor performance. As in humans, the condition can be intermittent in nature, known as paroxysmal atrial fibrillation (pAF). This review covers the literature relating to pAF in horses and includes references to the human literature to compare pathophysiology, clinical presentation, diagnostic tools and treatment. The arrhythmia is diagnosed by auscultation and electrocardiography (ECG), and clinical signs can vary from sudden loss of racing performance to reduced fitness or no signs at all. If left untreated, pAF may promote electrical, functional and structural remodeling of the myocardium, thus creating a substrate that is able to maintain the arrhythmia, which over time may progress into permanent AF. Long-term ECG monitoring is essential for diagnosing the condition and fully understanding the duration and frequency of pAF episodes. The potential to adapt human cardiac monitoring systems and computational ECG analysis is therefore of interest and may benefit future diagnostic tools in equine medicine.
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Shyam Kumar, Prashanth, Mouli Ramasamy, Kamala Ramya Kallur, Pratyush Rai, and Vijay K. Varadan. "Personalized LSTM Models for ECG Lead Transformations Led to Fewer Diagnostic Errors Than Generalized Models: Deriving 12-Lead ECG from Lead II, V2, and V6." Sensors 23, no. 3 (January 26, 2023): 1389. http://dx.doi.org/10.3390/s23031389.
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Background and Objective: The prevalence of chronic cardiovascular diseases (CVDs) has risen globally, nearly doubling from 1990 to 2019. ECG is a simple, non-invasive measurement that can help identify CVDs at an early and treatable stage. A multi-lead ECG, up to 15 leads in a wearable form factor, is desirable. We seek to derive multiple ECG leads from a select subset of leads so that the number of electrodes can be reduced in line with a patient-friendly wearable device. We further compare personalized derivations to generalized derivations. Methods: Long-Short Term Memory (LSTM) networks using Lead II, V2, and V6 as input are trained to obtain generalized models using Bayesian Optimization for hyperparameter tuning for all patients and personalized models for each patient by applying transfer learning to the generalized models. We compare quantitatively using error metrics Root Mean Square Error (RMSE), R2, and Pearson correlation (ρ). We compare qualitatively by matching ECG interpretations of board-certified cardiologists. Results: ECG interpretations from personalized models, when corrected for an intra-observer variance, were identical to the original ECGs, whereas generalized models led to errors. Mean performance values for generalized and personalized models were (RMSE-74.31 µV, R2-72.05, ρ-0.88) and (RMSE-26.27 µV, R2-96.38, ρ-0.98), respectively. Conclusions: Diagnostic accuracy based on derived ECG is the most critical validation of ECG derivation methods. Personalized transformation should be sought to derive ECGs. Performing a personalized calibration step to wearable ECG systems and LSTM networks could yield ambulatory 15-lead ECGs with accuracy comparable to clinical ECGs.
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Choi, Sunho, Hyung Joon Joo, Yoojoong Kim, Jong-Ho Kim, and Junhee Seok. "Conversion of Automated 12-Lead Electrocardiogram Interpretations to OMOP CDM Vocabulary." Applied Clinical Informatics 13, no. 04 (August 2022): 880–90. http://dx.doi.org/10.1055/s-0042-1756427.
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Abstract Background A computerized 12-lead electrocardiogram (ECG) can automatically generate diagnostic statements, which are helpful for clinical purposes. Standardization is required for big data analysis when using ECG data generated by different interpretation algorithms. The common data model (CDM) is a standard schema designed to overcome heterogeneity between medical data. Diagnostic statements usually contain multiple CDM concepts and also include non-essential noise information, which should be removed during CDM conversion. Existing CDM conversion tools have several limitations, such as the requirement for manual validation, inability to extract multiple CDM concepts, and inadequate noise removal. Objectives We aim to develop a fully automated text data conversion algorithm that overcomes limitations of existing tools and manual conversion. Methods We used interpretations printed by 12-lead resting ECG tests from three different vendors: GE Medical Systems, Philips Medical Systems, and Nihon Kohden. For automatic mapping, we first constructed an ontology-lexicon of ECG interpretations. After clinical coding, an optimized tool for converting ECG interpretation to CDM terminology is developed using term-based text processing. Results Using the ontology-lexicon, the cosine similarity-based algorithm and rule-based hierarchical algorithm showed comparable conversion accuracy (97.8 and 99.6%, respectively), while an integrated algorithm based on a heuristic approach, ECG2CDM, demonstrated superior performance (99.9%) for datasets from three major vendors. Conclusion We developed a user-friendly software that runs the ECG2CDM algorithm that is easy to use even if the user is not familiar with CDM or medical terminology. We propose that automated algorithms can be helpful for further big data analysis with an integrated and standardized ECG dataset.
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Zhu, Wenliang, Lishen Qiu, Wenqiang Cai, Jie Yu, Deyin Li, Wanyue Li, Jun Zhong, Yan Wang, and Lirong Wang. "A novel method to reduce false alarms in ECG diagnostic systems: capture and quantification of noisy signals." Physiological Measurement 42, no. 7 (July 1, 2021): 075001. http://dx.doi.org/10.1088/1361-6579/abf9f4.
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Hussein, Ahmed F., Warda R. Mohammed, Mustafa Musa Jaber, and Osamah Ibrahim Khalaf. "An Adaptive ECG Noise Removal Process Based on Empirical Mode Decomposition (EMD)." Contrast Media & Molecular Imaging 2022 (August 17, 2022): 1–9. http://dx.doi.org/10.1155/2022/3346055.
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The electrocardiogram (ECG) is a generally used instrument for examining cardiac disorders. For proper interpretation of cardiac illnesses, a noise-free ECG is often preferred. ECG signals, on the other hand, are suffering from numerous noises throughout gathering and programme. This article suggests an empirical mode decomposition-based adaptive ECG noise removal technique (EMD). The benefits of the proposed methods are used to dip noise in ECG signals with the least amount of distortion. For decreasing high-frequency noises, traditional EMD-based approaches either cast off the preliminary fundamental functions or use a window-based methodology. The signal quality is then improved via an adaptive process. The simulation study uses ECG data from the universal MIT-BIH database as well as the Brno University of Technology ECG Quality Database (BUT QDB). The proposed method’s efficiency is measured using three typical evaluation metrics: mean square error, output SNR change, and ratio root mean square alteration at various SNR levels (signal to noise ratio). The suggested noise removal approach is compatible with other commonly used ECG noise removal techniques. A detailed examination reveals that the proposed method could be served as an effective means of noise removal ECG signals, resulting in enhanced diagnostic functions in automated medical systems.
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Ribeiro, Pedro, João Alexandre Lobo Marques, and Pedro Miguel Rodrigues. "COVID-19 Detection by Means of ECG, Voice, and X-ray Computerized Systems: A Review." Bioengineering 10, no. 2 (February 3, 2023): 198. http://dx.doi.org/10.3390/bioengineering10020198.
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Since the beginning of 2020, Coronavirus Disease 19 (COVID-19) has attracted the attention of the World Health Organization (WHO). This paper looks into the infection mechanism, patient symptoms, and laboratory diagnosis, followed by an extensive assessment of different technologies and computerized models (based on Electrocardiographic signals (ECG), Voice, and X-ray techniques) proposed as a diagnostic tool for the accurate detection of COVID-19. The found papers showed high accuracy rate results, ranging between 85.70% and 100%, and F1-Scores from 89.52% to 100%. With this state-of-the-art, we concluded that the models proposed for the detection of COVID-19 already have significant results, but the area still has room for improvement, given the vast symptomatology and the better comprehension of individuals’ evolution of the disease.
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Shi, Jiguang, Zhoutong Li, Wenhan Liu, Huaicheng Zhang, Qianxi Guo, Sheng Chang, Hao Wang, Jin He, and Qijun Huang. "Optimized Solutions of Electrocardiogram Lead and Segment Selection for Cardiovascular Disease Diagnostics." Bioengineering 10, no. 5 (May 18, 2023): 607. http://dx.doi.org/10.3390/bioengineering10050607.
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Most of the existing multi-lead electrocardiogram (ECG) detection methods are based on all 12 leads, which undoubtedly results in a large amount of calculation and is not suitable for the application in portable ECG detection systems. Moreover, the influence of different lead and heartbeat segment lengths on the detection is not clear. In this paper, a novel Genetic Algorithm-based ECG Leads and Segment Length Optimization (GA-LSLO) framework is proposed, aiming to automatically select the appropriate leads and input ECG length to achieve optimized cardiovascular disease detection. GA-LSLO extracts the features of each lead under different heartbeat segment lengths through the convolutional neural network and uses the genetic algorithm to automatically select the optimal combination of ECG leads and segment length. In addition, the lead attention module (LAM) is proposed to weight the features of the selected leads, which improves the accuracy of cardiac disease detection. The algorithm is validated on the ECG data from the Huangpu Branch of Shanghai Ninth People’s Hospital (defined as the SH database) and the open-source Physikalisch-Technische Bundesanstalt diagnostic ECG database (PTB database). The accuracy for detection of arrhythmia and myocardial infarction under the inter-patient paradigm is 99.65% (95% confidence interval: 99.20–99.76%) and 97.62% (95% confidence interval: 96.80–98.16%), respectively. In addition, ECG detection devices are designed using Raspberry Pi, which verifies the convenience of hardware implementation of the algorithm. In conclusion, the proposed method achieves good cardiovascular disease detection performance. It selects the ECG leads and heartbeat segment length with the lowest algorithm complexity while ensuring classification accuracy, which is suitable for portable ECG detection devices.
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Wasimuddin, Muhammad, Khaled Elleithy, Abdelshakour Abuzneid, Miad Faezipour, and Omar Abuzaghleh. "Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling." Electronics 10, no. 2 (January 14, 2021): 170. http://dx.doi.org/10.3390/electronics10020170.
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Cardiovascular diseases have been reported to be the leading cause of mortality across the globe. Among such diseases, Myocardial Infarction (MI), also known as “heart attack”, is of main interest among researchers, as its early diagnosis can prevent life threatening cardiac conditions and potentially save human lives. Analyzing the Electrocardiogram (ECG) can provide valuable diagnostic information to detect different types of cardiac arrhythmia. Real-time ECG monitoring systems with advanced machine learning methods provide information about the health status in real-time and have improved user’s experience. However, advanced machine learning methods have put a burden on portable and wearable devices due to their high computing requirements. We present an improved, less complex Convolutional Neural Network (CNN)-based classifier model that identifies multiple arrhythmia types using the two-dimensional image of the ECG wave in real-time. The proposed model is presented as a three-layer ECG signal analysis model that can potentially be adopted in real-time portable and wearable monitoring devices. We have designed, implemented, and simulated the proposed CNN network using Matlab. We also present the hardware implementation of the proposed method to validate its adaptability in real-time wearable systems. The European ST-T database recorded with single lead L3 is used to validate the CNN classifier and achieved an accuracy of 99.23%, outperforming most existing solutions.
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Floriano, Pierre N., Nicolaos Christodoulides, Craig S. Miller, Jeffrey L. Ebersole, John Spertus, Beate G. Rose, Denis F. Kinane, et al. "Use of Saliva-Based Nano-Biochip Tests for Acute Myocardial Infarction at the Point of Care: A Feasibility Study." Clinical Chemistry 55, no. 8 (August 1, 2009): 1530–38. http://dx.doi.org/10.1373/clinchem.2008.117713.
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Abstract Background: For adults with chest pain, the electrocardiogram (ECG) and measures of serum biomarkers are used to screen and diagnose myocardial necrosis. These measurements require time that can delay therapy and affect prognosis. Our objective was to investigate the feasibility and utility of saliva as an alternative diagnostic fluid for identifying biomarkers of acute myocardial infarction (AMI). Methods: We used Luminex and lab-on-a-chip methods to assay 21 proteins in serum and unstimulated whole saliva procured from 41 AMI patients within 48 h of chest pain onset and from 43 apparently healthy controls. Data were analyzed by use of logistic regression and area under curve (AUC) for ROC analysis to evaluate the diagnostic utility of each biomarker, or combinations of biomarkers, in screening for AMI. Results: Both established and novel cardiac biomarkers demonstrated significant differences in concentrations between patients with AMI and controls without AMI. The saliva-based biomarker panel of C-reactive protein, myoglobin, and myeloperoxidase exhibited significant diagnostic capability (AUC = 0.85, P < 0.0001) and in conjunction with ECG yielded strong screening capacity for AMI (AUC = 0.96) comparable to that of the panel (brain natriuretic peptide, troponin-I, creatine kinase-MB, myoglobin; AUC = 0.98) and far exceeded the screening capacity of ECG alone (AUC approximately 0.6). En route to translating these findings to clinical practice, we adapted these unstimulated whole saliva tests to a novel lab-on-a-chip platform for proof-of-principle screens for AMI. Conclusions: Complementary to ECG, saliva-based tests within lab-on-a-chip systems may provide a convenient and rapid screening method for cardiac events in prehospital stages for AMI patients.
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Timofeev, E. V. "INTERNET ELECTROCARDIOGRAPHY IN PEDIATRICS." Juvenis Scientia 7, no. 6 (2021): 17–27. http://dx.doi.org/10.32415/jscientia_2021_7_6_17-27.
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<b>Introduction.</b> Internet electrocardiography (internet-ECG) is becoming one of the most deman-ded areas of telemedicine; it becomes especially important in conditions of remoteness from medical and diagnostic institutions and impossibility of real consulting assistance of specialists. The general principle of operation of the internet-ECG devices is transferring the record to a server with subsequent processing and obtaining an automatic conclusion on the rhythm and morphology of the atrial-ventricular complex. At present, internet-ECG is widely used in adult network, while its possibilities in pediatrics are poorly covered. <br><b>Materials and methods.</b> The archive of children's ECGs recorded using the Cardiometer-MT system in the period from 2013 to 2021 has been analyzed. 3 groups of children were identified. 1<sup>st</sup> - screening of practically healthy children of primary school age (2153 children), 2<sup>nd</sup> group - school-age children examined in children's city polyclinics, who underwent stress and vegetative tests (2500 children), 3<sup>rd</sup> group - 200 healthy full-term newborn children who had a standard resting ECG on 1-2 days of life. <br><b>Results.</b> The advantages of using such systems in the mass examination of children of various ages in the framework of screening programs are shown. The results of an ECG examination of 2153 healthy children of primary school age are presented. The results of the automatic and medical conclusion are compared, sensitivity and specificity in the detection of cardiac arrhythmias and conduction are determined. The advantages of internet-ECG in carrying out vegetative and stress tests in the examination of adolescent children are substantiated. Performance of functional (including vegetative) and stress tests using internet-ECG systems allows to estimate reliably the functional state of the cardiovascular system, to determine adaptive capabilities of the vegetative nervous system, to reveal hypertension at early stages. <br><b>Conclusion.</b> The internet-ECG makes it possible to significantly simplify and streamline the ECG examination of children, forming an electronic archive of records, which is relevant for field exa-minations in children's groups and in conditions of a shortage of qualified specialists in functional diagnostics.
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Kirakosyan, E. V., and M. M. Lokhmatov. "Therapeutic and diagnostic enteroscopy in children: 15 years of experience." Experimental and Clinical Gastroenterology, no. 1 (May 2, 2020): 102–10. http://dx.doi.org/10.31146/1682-8658-ecg-173-1-102-110.
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Each new stage in the development of endoscopy was characterized by the creation of more sophisticated equipment and the expansion of diagnostic capabilities. The 21st century was marked by the transition to robotic remotely controlled endoscopic systems, the use of digital or electronic endoscopic technology in children. The modern level of endoscopy in pediatrics includes a high definition of the image obtained, a morphological study of biopsy specimens and a full range of endosurgical procedures. The paper presents the experience of introducing, teaching, preparing, conducting, analyzing and evaluating the results, features and nuances of use in children of therapeutic and diagnostic enteroscopy of a new generation: video capsule endoscopy and double-balloon enteroscopy. The modern approach to intraluminal diagnosis and endoscopic treatment of children with digestive system pathologies is substantiated.
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Neycheva, Tatyana, Dobromir Dobrev, and Vessela Krasteva. "Common-Mode Driven Synchronous Filtering of the Powerline Interference in ECG." Applied Sciences 12, no. 22 (November 8, 2022): 11328. http://dx.doi.org/10.3390/app122211328.
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Powerline interference (PLI) is a major disturbing factor in ground-free biopotential acquisition systems. PLI produces both common-mode and differential input voltages. The first is suppressed by a high common-mode rejection ratio of bioamplifiers. However, the differential PLI component evoked by the imbalance of electrode impedances is amplified together with the diagnostic differential biosignal. Therefore, PLI filtering is always demanded and commonly managed by analog or digital band-rejection filters. In electrocardiography (ECG), PLI filters are not ideal, inducing QRS and ST distortions as a transient reaction to steep slopes, or PLI remains when its amplitude varies and PLI frequency deviates from the notch. This study aims to minimize the filter errors in wide deviation ranges of PLI amplitudes and frequencies, introducing a novel biopotential readout circuit with a software PLI demodulator–remodulator concept for synchronous processing of both differential-mode and common-mode signals. A closed-loop digital synchronous filtering (SF) algorithm is designed to subtract a PLI estimation from the differential-mode input in real time. The PLI estimation branch connected to the SF output includes four stages: (i) prefilter and QRS limiter; (ii) quadrature demodulator of the output PLI using a common-mode driven reference; (iii) two servo loops for low-pass filtering and the integration of in-phase and quadrature errors; (iv) quadrature remodulator for synthesis of the estimated PLI using the common-mode signal as a carrier frequency. A simulation study of artificially generated PLI sinusoids with frequency deviations (48–52 Hz, slew rate 0.01–0.1 Hz/s) and amplitude deviations (root mean square (r.m.s.) 50–1000 μV, slew rate 10–200 μV/s) is conducted for the optimization of SF servo loop settings with artificial signals from the CTS-ECG calibration database (10 s, 1 lead) as well as for the SF algorithm test with 40 low-noise recordings from the Physionet PTB Diagnostic ECG database (10 s, 12 leads) and CTS-ECG analytical database (10 s, 8 leads). The statistical study for the PLI frequencies (48–52 Hz, slew rate ≤ 0.1 Hz/s) and amplitudes (≤1000 μV r.m.s., slew rate ≤ 40 μV/s) show that maximal SF errors do not exceed 15 μV for any record and any lead, which satisfies the standard requirements for a peak ringing noise of < 25 μV. The signal-to-noise ratio improvement reaches 57–60 dB. SF is shown to be robust against phase shifts between differential- and common-mode PLI. Although validated for ECG signals, the presented SF algorithm is generalizable to different biopotential acquisition settings via surface electrodes (electroencephalogram, electromyogram, electrooculogram, etc.) and can benefit many diagnostic and therapeutic medical devices.
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Vasudeva, Shrivathsa Thokur, Shrikantha Sasihithlu Rao, Navin Karanth Panambur, Arun Kumar Shettigar, Chakrapani Mahabala, Padmanabh Kamath, Manjunath Patel Gowdru Chandrashekarappa, and Emanoil Linul. "Development of a Convolutional Neural Network Model to Predict Coronary Artery Disease Based on Single-Lead and Twelve-Lead ECG Signals." Applied Sciences 12, no. 15 (July 31, 2022): 7711. http://dx.doi.org/10.3390/app12157711.
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Coronary artery disease (CAD) is one of the most common causes of heart ailments; many patients with CAD do not exhibit initial symptoms. An electrocardiogram (ECG) is a diagnostic tool widely used to capture the abnormal activity of the heart and help with diagnoses. Assessing ECG signals may be challenging and time-consuming. Identifying abnormal ECG morphologies, especially in low amplitude curves, may be prone to error. Hence, a system that can automatically detect and assess the ECG and treadmill test ECG (TMT-ECG) signals will be helpful to the medical industry in detecting CAD. In the present work, we developed an intelligent system that can predict CAD, based on ECG and TMT signals more accurately than any other system developed thus far. The distinct convolutional neural network (CNN) architecture deals with single-lead and multi-lead (12-lead) ECG and TMT-ECG data effectively. While most artificial intelligence-based systems rely on the universal dataset, the current work used clinical lab data collected from a renowned hospital in the neighborhood. ECG and TMT-ECG graphs of normal and CAD patients were collected in the form of scanned reports. One-dimensional ECG data with all possible features were extracted from the scanned report with the help of a modified image processing method. This feature extraction procedure was integrated with the optimized architecture of the CNN model leading to a novel prediction system for CAD. The automated computer-assisted system helps in the detection and medication of CAD with a high prediction accuracy of 99%.
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Krasteva, Vessela, Ivaylo Christov, Stefan Naydenov, Todor Stoyanov, and Irena Jekova. "Application of Dense Neural Networks for Detection of Atrial Fibrillation and Ranking of Augmented ECG Feature Set." Sensors 21, no. 20 (October 15, 2021): 6848. http://dx.doi.org/10.3390/s21206848.
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Considering the significant burden to patients and healthcare systems globally related to atrial fibrillation (AF) complications, the early AF diagnosis is of crucial importance. In the view of prominent perspectives for fast and accurate point-of-care arrhythmia detection, our study optimizes an artificial neural network (NN) classifier and ranks the importance of enhanced 137 diagnostic ECG features computed from time and frequency ECG signal representations of short single-lead strips available in 2017 Physionet/CinC Challenge database. Based on hyperparameters’ grid search of densely connected NN layers, we derive the optimal topology with three layers and 128, 32, 4 neurons per layer (DenseNet-3@128-32-4), which presents maximal F1-scores for classification of Normal rhythms (0.883, 5076 strips), AF (0.825, 758 strips), Other rhythms (0.705, 2415 strips), Noise (0.618, 279 strips) and total F1 relevant to the CinC Challenge of 0.804, derived by five-fold cross-validation. DenseNet-3@128-32-4 performs equally well with 137 to 32 features and presents tolerable reduction by about 0.03 to 0.06 points for limited input sets, including 8 and 16 features, respectively. The feature reduction is linked to effective application of a comprehensive method for computation of the feature map importance based on the weights of the activated neurons through the total path from input to specific output in DenseNet. The detailed analysis of 20 top-ranked ECG features with greatest importance to the detection of each rhythm and overall of all rhythms reveals DenseNet decision-making process, noticeably corresponding to the cardiologists’ diagnostic point of view.
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Sandberg, Edvard Liljedahl, Bjørnar Leangen Grenne, Trygve Berge, Jostein Grimsmo, Dan Atar, Sigrun Halvorsen, Rune Fensli, and Jarle Jortveit. "Diagnostic Accuracy and Usability of the ECG247 Smart Heart Sensor Compared to Conventional Holter Technology." Journal of Healthcare Engineering 2021 (November 2, 2021): 1–8. http://dx.doi.org/10.1155/2021/5230947.
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Background. Heart rhythm disorders, especially atrial fibrillation (AF), are increasing global health challenges. Conventional diagnostic tools for assessment of rhythm disorders suffer from limited availability, limited test duration time, and usability challenges. There is also a need for out-of-hospital investigation of arrhythmias. Therefore, the Norwegian ECG247 Smart Heart Sensor has been developed to simplify the assessment of heart rhythm disorders. The current study aimed to evaluate the diagnostic accuracy and usability of the ECG247 Smart Heart Sensor compared to conventional Holter monitors. Methods. Parallel tests with ECG247 Smart Heart Sensor and a Holter monitor were performed in 151 consecutive patients referred for out-of-hospital long-term ECG recording at Sorlandet Hospital Arendal, Norway. All ECG data were automatically analysed by both systems and evaluated by hospital physicians. Participants were asked to complete a questionnaire scoring usability parameters after the test. Results. A total of 150 patients (62% men, age 54 (±17) years) completed the study. The ECG quality from both monitors was considered satisfactory for rhythm analysis in all patients. AF was identified in 9 (6%) patients during the period with parallel tests. The diagnostic accuracy for automatic AF detection was 95% (95% CI 91–98) for the ECG247 Smart Heart Sensor and 81% (95% CI 74–87) for the Holter system. The proportion of false-positive AF was 4% in tests analysed by the ECG247 algorithm and 16% in tests analysed by the Holter algorithm. Other arrhythmias were absent/rare. The system usability score was significantly better for ECG247 Smart Heart Sensor compared to traditional Holter technology (score 87.4 vs. 67.5, p < 0.001 ). Conclusions. The ECG247 Smart Heart Sensor showed at least comparable diagnostic accuracy for AF and improved usability compared to conventional Holter technology. ECG247 allows for prolonged monitoring and may improve detection of AF. This trial is registered with https://clinicaltrials.gov/ct2/show/NCT04700865.
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Khan, Ali Haider, Muzammil Hussain, and Muhammad Kamran Malik. "Arrhythmia Classification Techniques Using Deep Neural Network." Complexity 2021 (April 20, 2021): 1–10. http://dx.doi.org/10.1155/2021/9919588.
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Electrocardiogram (ECG) is the most common and low-cost diagnostic tool used in healthcare institutes for screening heart electrical signals. The abnormal heart signals are commonly known as arrhythmia. Cardiac arrhythmia can be dangerous, or in most cases, it can cause death. The arrhythmia can be of different types, and it can be detected by an ECG test. The automated screening of arrhythmia classification using ECG beats is developed for ages. The automated systems that can be adapted as a tool for screening arrhythmia classification play a vital role not only for the patients but can also assist the doctors. The deep learning-based automated arrhythmia classification techniques are developed with high accuracy results but still not adopted by healthcare professionals as the generalized approach. The primary concerns that affect the success of the developed arrhythmia detection systems are (i) manual features selection, (ii) techniques used for features extraction, and (iii) algorithm used for classification and the most important is the use of imbalanced data for classification. This study focuses on the recent trends in arrhythmia classification techniques, and through extensive simulations, the performance of the various arrhythmia classification and detection models has been evaluated. Finally, the study presents insights into arrhythmia classification techniques to overcome the limitation in the existing methodologies.
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Joseph Michael Jerard, V., M. Thilagaraj, K. Pandiaraj, M. Easwaran, Petchinathan Govindan, and V. Elamaran. "Reconfigurable Architectures with High-Frequency Noise Suppression for Wearable ECG Devices." Journal of Healthcare Engineering 2021 (December 22, 2021): 1–12. http://dx.doi.org/10.1155/2021/1552641.
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Recent advances in electronics and microelectronics have aided the development of low-cost devices that are widely used as well-being or preventive monitoring devices by many people. Remote health monitoring, which includes wearable sensors, actuators, and modern communication and information systems, offers effective programs that allow people to live peacefully in their own homes while also being protected in some way. High-frequency noise, power-line interface, and baseline drift are prevalent during the data-acquisition system of an ECG signal, and they can limit signal understanding. They (noises) must be isolated in order to provide an appropriate diagnostic of the patient. When removing high-frequency components (noise) from an ECG signal with an FIR filter, the critical path delay increases considerably as the filter's duration increases. To reduce high-frequency noise, simple moving average filters with pipelining and look-ahead transformation techniques are extensively used in this study. With the use of pipelining and look-ahead techniques, the only objective is to increase the clock speed of the designs. The moving average filters (conventional and proposed) were created on an Altera Cyclone IV FPGA EP4CE115F29C7 chip using the Quartus II software v13.1 tool. Finally, performance metrics such logic elements, clock speed, and power consumption were compared and studied thoroughly. The recursive pipelined 8-tap MA filter with look-ahead approach outperforms the other designs (685.48 MHz) in this investigation.
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Rəşadət qızı Bəşirova, Rəşidə. "A new approach to the analysis of electrocardiological signals studied in athletes under stress." SCIENTIFIC WORK 76, no. 3 (March 18, 2022): 173–77. http://dx.doi.org/10.36719/2663-4619/76/168-173-177.
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Elektrokardioqrafiya (EKQ) ürək xəstəliklərinin diaqnostikasında tətbiq edilən əsas metodlardan biridir. Elektrokardioqrafik siqnallarının riyazi kibernetik metodlar əsasında təhlili avtomatlaşdırılmış diaqnostika probleminin mühüm tərkib hissəsidir. Bu sahədə aparılan elmi-tədqiqat işləri və praktiki fəaliyyət sürətli inkişaf mərhələsini yaşayır. Elektrokardioloji siqnalların texniki vasitələri informasiyanın qəbul edilməsi və yığılması sistemlərilə uzlaşdırılan mürəkkəb hesablama kompleksləridir. Kardioqrafik informasiyanın riyazi-kibernetik vasitələr əsasında təhlili və avtomatlaşdırılmış diaqnostika problemnin həlli istiqamətində aparılan tədqiqatlar əks etdirilir. Ənənəvi spektral metodologiyasından fərqli olaraq tətbiq edilən veyvlet alqoritmlərinin bir sıra mühüm üstünlükləri göstərilir. Açar sözlər: elektrofiziologiya, EKQ siqnalı, ilkin emal, veyvlet alqoritmləri, emal qurğuları, tibbi-kardioloji informasiya, tətbiqi proqramlar paketi Rashida Rashadat Bashirova A new approach to the analysis of electrocardiological signals studied in athletes under stress Abstract ECG is one of the main methods used in the diagnosis of heart disease. Analysis of electrocardiography (ECG) signals based on mathematical cybernetic methods is an important part of the problem of automated diagnostics. Research and practical activities in this area are experiencing a period of rapid development. The technical means of electrocardiological signals are complex computational complexes combined with information extraction and collection systems. The analysis of cardiographic data on the basis of mathematical and cybernetic tools and the researches carried out to correct the automatic diagnostic problem are reflected. In contrast to traditional spectral methodologies, a number of important advantages of the applied wave algorithms have been demonstrated. Key words: electrophysiology, ECG signal, initial processing, wavelet algorithms, processing devices, medical-cardiological information, application software package
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Satheesh Pandian, C., and A. M. Kalpana. "HybDeepNet: ECG Signal Based Cardiac Arrhythmia Diagnosis Using a Hybrid Deep Learning Model." Information Technology and Control 52, no. 2 (July 15, 2023): 416–32. http://dx.doi.org/10.5755/j01.itc.52.2.33302.
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To monitor electrical indications from the heart and assess its performance, the electrocardiogram (ECG) is the most common and routine diagnostic instrument employed. Cardiac arrhythmias are only one example of the many heart conditions people might have. ECG records are used to diagnose an arrhythmia, an abnormal cardiac beat that can cause a stroke in extreme circumstances. However, due to the extensive data that an ECG contains, it is quite difficult to glean the necessary information through visual analysis. Therefore, it is crucial to develop an effective (automatic) method to analyze the vast amounts of data available from ECG. For decades, researchers have focused on developing methods to automatically and computationally categorize and identify cardiac arrhythmias. However, monitoring for arrhythmias in real-time is challenging. To streamline the detection and classification process, this research presents a hybrid deep learning-based technique. There are two major contributions to this study. To automate the noise reduction and feature extraction, 1D ECG data are first transformed into 2D Scalogram images. Following this, a combined approach called the Residual attention-based 2D-CNN-LSTM-CNN (RACLC) is recommended by merging multiple learning models, specifically the 2D convolutional neural network (CNN) and the Long Short-Term Memory (LSTM) system, based on research findings. The name of this model comes from a combination of the two deep learning. Both the beats themselves, which provide morphological information, and the beats paired with neighboring segments, which provide temporal information, are essential. Our suggested model simultaneously collects time-domain and morphological ECG signal data and combines them. The application of the attention block to the network helps to strengthen the valuable information, acquire the confidential message in the ECG signal, and boost the efficiency of the model when it comes to categorization. To evaluate the efficacy of the proposed RACLC method, we carried out a complete experimental investigation making use of the MIT-BIH arrhythmia database, which is used by a large number of researchers. The results of our experiments show that the automated detection method we propose is effective.
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Qin, Jing, Fujie Gao, Zumin Wang, Lu Liu, and Changqing Ji. "Arrhythmia Detection Based on WGAN-GP and SE-ResNet1D." Electronics 11, no. 21 (October 23, 2022): 3427. http://dx.doi.org/10.3390/electronics11213427.
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A WGAN-GP-based ECG signal expansion and an SE-ResNet1D-based ECG classification method are proposed to address the problem of poor modeling results due to the imbalanced sample distribution of ECG data sets. The network architectures of WGAN-GP and SE-ResNet1D are designed according to the characteristics of ECG signals so that they can be better applied to the generation and classification of ECG signals. First, ECG data were generated using WGAN-GP on the MIT-BIH arrhythmia database to balance the dataset. Then, the experiments were performed using the AAMI category and inter-patient data partitioning principles, and classification experiments were performed using SE-ResNet1D on the imbalanced and balanced datasets, respectively, and compared with three networks, VGGNet, DenseNet and CNN+Bi-LSTM. The experimental results show that using WGAN-GP to balance the dataset can improve the accuracy and robustness of the model classification, and the proposed SE-ResNet1D outperforms the comparison model, with a precision of 95.80%, recall of 96.75% and an F1 measure of 96.27% on the balanced dataset. Our methods have the potential to be a useful diagnostic tool to assist cardiologists in the diagnosis of arrhythmias.
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Attin, Mina, Lu Wang, S. M. Reza Soroushmehr, Chii-Dean Lin, Hector Lemus, Maxwell Spadafore, and Kayvan Najarian. "Digitization of Electrocardiogram From Telemetry Prior to In-hospital Cardiac Arrest." Biological Research For Nursing 18, no. 2 (August 27, 2015): 230–36. http://dx.doi.org/10.1177/1099800415602092.
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Background: Analyzing telemetry electrocardiogram (ECG) data over an extended period is often time-consuming because digital records are not widely available at hospitals. Investigating trends and patterns in the ECG data could lead to establishing predictors that would shorten response time to in-hospital cardiac arrest (I-HCA). This study was conducted to validate a novel method of digitizing paper ECG tracings from telemetry systems in order to facilitate the use of heart rate as a diagnostic feature prior to I-HCA. Methods: This multicenter study used telemetry to investigate full-disclosure ECG papers of 44 cardiovascular patients obtained within 1 hr of I-HCA with initial rhythms of pulseless electrical activity and asystole. Digital ECGs were available for seven of these patients. An algorithm to digitize the full-disclosure ECG papers was developed using the shortest path method. The heart rate was measured manually (averaging R-R intervals) for ECG papers and automatically for digitized and digital ECGs. Results: Significant correlations were found between manual and automated measurements of digitized ECGs ( p < .001) and between digitized and digital ECGs ( p < .001). Bland–Altman methods showed bias = .001 s, SD = .0276 s, lower and upper 95% limits of agreement for digitized and digital ECGs = .055 and −.053 s, and percentage error = 0.22%. Root mean square (rms), percentage rms difference, and signal to noise ratio values were in acceptable ranges. Conclusion: The digitization method was validated. Digitized ECG provides an efficient and accurate way of measuring heart rate over an extended period of time.
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He, Yan, Bin Fu, Jian Yu, Renfa Li, and Rucheng Jiang. "Efficient Learning of Healthcare Data from IoT Devices by Edge Convolution Neural Networks." Applied Sciences 10, no. 24 (December 15, 2020): 8934. http://dx.doi.org/10.3390/app10248934.
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Wireless and mobile health applications promote the development of smart healthcare. Effective diagnosis and feedbacks of remote health data pose significant challenges due to streaming data, high noise, network latency and user privacy. Therefore, we explore efficient edge and cloud design to maintain electrocardiogram classification performance while reducing the communication cost. These contributions include: (1) We introduce a hybrid smart medical architecture named edge convolutional neural networks (EdgeCNN) that balances the capability of edge and cloud computing to address the issue for agile learning of healthcare data from IoT devices. (2) We present an effective deep learning model for electrocardiogram (ECG) inference, which can be deployed to run on edge smart devices for low-latency diagnosis. (3) We design a data enhancement method for ECG based on deep convolutional generative adversarial network to expand ECG data volume. (4) We carried out experiments on two representative datasets to evaluate the effectiveness of the deep learning model of ECG classification based on EdgeCNN. EdgeCNN shows superior to traditional cloud medical systems in terms of network Input/Output (I/O) pressure, architecture cost and system high availability. The deep learning model not only ensures high diagnostic accuracy, but also has advantages in aspect of inference time, storage, running memory and power consumption.
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Rayavarapu, Swarajya Madhuri, Tammineni Shanmukha Prashanthi, Gottapu Santosh Kumar, Yenneti Laxmi Lavanya, and Gottapu Sasibhushana Rao. "A Generative Adversarial Network Based Approach for Synthesis of Deep Fake Electrocardiograms." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 3 (April 4, 2023): 223–27. http://dx.doi.org/10.17762/ijritcc.v11i3.6340.
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Analyzing the data from an electrocardiogram (ECG) can reveal important details about a patient's heart health. A key component of modern medicine is the use of AI and ML-based computer-aided diagnosis tools to aid in making life-or-death decisions. It is common practice to use them in cardiology for the automatic early diagnosis of a variety of potentially fatal illnesses. The machine learning algorithm's need for a large amount of training data to build the learning model is an empirical challenge in the medical domain. To address this challenge, study into methods for creating synthetic patient data has blossomed. There is a higher risk of privacy invasion due to the need for massive amounts of training data for deep learning automated medical diagnostic systems that may help assess the state of the heart from this signal. To combat this issue, researchers have tried to create artificial ECG readings by analyzing only the statistical distributions of the accessible authentic training data.The primary goal of this study is to learn how generative adversarial networks can be used to create artificial ECG signals for use as training data in a classification task. In this study, we used both GAN and WGAN for generation of artificial ECG signals.
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Hossain, Parwez, Abdo Karim Tourkmani, Dimitri Kazakos, Mark Jones, and David Anderson. "Emergency corneal grafting in the UK: a 6-year analysis of the UK Transplant Registry." British Journal of Ophthalmology 102, no. 1 (May 11, 2017): 26–30. http://dx.doi.org/10.1136/bjophthalmol-2016-309870.
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BackgroundCorneal graft (CG) surgery is the most common and successful tissue transplant worldwide. A small and important group of patients are operated in emergency situations, typically to save a perforated eye. Our knowledge of the indications and outcomes of emergency corneal graft (eCG) is limited.MethodsRetrospective, multifactorial analysis of all CGs registered by the UK Transplant Service from April 1999 to March 2005.ResultsA total of 12 976 CGs were performed. 1330 (11.4%) were eCGs including 433 regrafts. Actual perforation occurred in 876 (65.9%) patients. 420 (31.5%) grafts were for tectonic purposes alone and 217 (16.3%) were also grafted for visual rehabilitation. The main diagnostic categories were infection (39.4%), non-infectious ulcerative keratitis (32.2%) and other causes (ectasias, previous ocular surgery, injury, dystrophies and opacification). Graft survival of first eCG at 1, 2 and 5 years was 78%, 66% and 47%, respectively. Best-corrected visual acuity of surviving grafts at 1 year was: 6/12 or better in 29.9%, 6/18 to 6/60 in 38.4%, counting finger to LP in 30.6% and NPL in 1%, with worsening of vision in only 8.7% of the patients.ConclusionThis study which is the largest of its kind shows that despite the seriousness of the critical corneal pathology and the surgical challenges that it poses, the outcomes of eCG are favourable with most patients keeping their eyesight and avoiding immediate rejection. These clinical outcomes show the value of eye banking facilities that are developed to support corneal tissue supply for eCG.
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Минина and E. Minina. "A New Approach to Examine the Relationship between Functional Preparedness and Electrogenesis in the Athletes Using the Etalon Cardiocycle." Journal of New Medical Technologies. eJournal 8, no. 1 (November 5, 2014): 1–5. http://dx.doi.org/10.12737/5950.
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In modern sport, the result depends on many aspects, including technical, tactical, physical, physiological and psychological factors, as well as heredity and fitness athletes. Diagnostics functional status and identification of mechanisms of formation of the adaptive response of the organism to the load allows to analyzing these factors for individual components. Well known, the myocardium is sensitive the indicator´s ability to consume oxygen, which can be a limiting factor in aerobic functional capacities and reserves not only of the heart muscle, but also the entire body. With this increasing needs of the myocardium in oxygen, including under increasing load, lead to ischemic disorders, in the event which suffer all the processes of membrane electrogenesis: excitability of myocardial cells, bioelectric automaticity processes in the myocardium, including the processes of de - and repolarization. It is found that the original features of the reference cardiocycle (EC) single-channel ECG that can be computed in hardware-software complex FAZAGRAF® with finger electrodes have an additional diagnostic value in quantifying the level of the functional state of the myocardium EC, which is formed in the time domain to the coordinates of the averaged phase trajectory and main traditional ECG signs doesn’t depend on the lead systems may reflect the characteristics of electrogenesis. The high degree of single-channel correlation indices EC ECG with quantitative values of functional training athletes (power, economy and effectiveness) allows to use EC in the diagnosis of functional disorders of cardiac hemodynamic to identify units in need of rehabilitation, correction and optimization.
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Simoliuniene, R., M. Tamosiunas, V. Saferis, A. Vainoras, L. Gargasas, and A. Krisciukaitis. "Efficiency Evaluation of Autonomic Heart Control by Using the Principal Component Analysis of ECG P-Wave." Methods of Information in Medicine 49, no. 02 (2010): 161–67. http://dx.doi.org/10.3414/me9306.
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Summary Background: Cardiac output is controlled by the autonomic nervous system by changing the heart rate and/or the contractions of the heart muscle in response to the hemodynamic needs of the whole body. Malfunction of these mechanisms causes the postural orthostatic tachycardia syndrome and/or the chronic fatigue syndrome. Evaluation of functionality and efficiency of the control mechanisms could give valuable diagnostic information in the early stages of dysfunction of the heart control systems and help to monitor the healing process in rehabilitation period after interventions. Objectives: In this study we demonstrate how P-wave changes evoked by an ortho-static test could be quantitatively evaluated by using the method based on the principal component analysis. Methods: ECG signals were recorded during an orthostatic test performed according to the typical protocol in three groups of volunteer subjects representing healthy young and older persons, part of which had transient periods of supraventricular arrhythmias. Quantitative evaluation of P-wave morphology changes was performed by means of principal component analysis-based method. Results: Principal component-based estimates showed certain variety of P-wave shape during orthostatic test, what revealed a possibility to evaluate the properties of para-sympathetic heart control. Conclusions: Quantitative evaluation of ECG P-wave changes evoked by an orthostatic test by using a newly developed method provides a quantitative estimate for functionality and efficiency of the heart rate control mechanisms. The method could be used in eHealth systems.
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Ali, Hassan, Hein Htet Naing, and Raziq Yaqub. "An IoT Assisted Real-Time High CMRR Wireless Ambulatory ECG Monitoring System with Arrhythmia Detection." Electronics 10, no. 16 (August 4, 2021): 1871. http://dx.doi.org/10.3390/electronics10161871.
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The absence of cardiovascular disease (CVD) diagnostic and management solutions cause significant morbidity among populations in rural areas and the coronavirus disease of 2019 (COVID-19) emergency. To tackle this problem, in this paper, the development of an Internet of things (IoT) assisted ambulatory electrocardiogram (ECG) monitoring system is presented. The system’s wearable single-channel data acquisition device supports 25 h of continuous operation. A right leg drive (RLD) circuit supported analog frontend (AFE) with a high common mode rejection ratio (CMRR) of 121 dB and a digitally implemented notch filter is used to suppress power-line frequency interference. The wearable device continuously sends the collected ECG data via Bluetooth to the user’s smartphone. An application on the user’s smartphone renders real-time ECG trace and heart rate and detects abnormal heart rhythms. This data are then shared in real-time with the user’s doctor via a real-time cloud database. An application on the doctor’s smartphone allows real-time visualization of this data and detection of arrhythmias. Simulations and experimental results demonstrate that reliable ECG signals can be captured with low latency and the heart rate computation is comparable to a commercial application. Low cost, scalability, low latency, real-time ECG monitoring, and improved performance of the system make the system highly suitable for the real-time remote identification and management of CVDs in users of rural areas and in the COVID-19 pandemic.
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Sharma, Manish, Jaypal Singh Rajput, Ru San Tan, and U. Rajendra Acharya. "Automated Detection of Hypertension Using Physiological Signals: A Review." International Journal of Environmental Research and Public Health 18, no. 11 (May 29, 2021): 5838. http://dx.doi.org/10.3390/ijerph18115838.
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Arterial hypertension (HT) is a chronic condition of elevated blood pressure (BP), which may cause increased incidence of cardiovascular disease, stroke, kidney failure and mortality. If the HT is diagnosed early, effective treatment can control the BP and avert adverse outcomes. Physiological signals like electrocardiography (ECG), photoplethysmography (PPG), heart rate variability (HRV), and ballistocardiography (BCG) can be used to monitor health status but are not directly correlated with BP measurements. The manual detection of HT using these physiological signals is time consuming and prone to human errors. Hence, many computer-aided diagnosis systems have been developed. This paper is a systematic review of studies conducted on the automated detection of HT using ECG, HRV, PPG and BCG signals. In this review, we have identified 23 studies out of 250 screened papers, which fulfilled our eligibility criteria. Details of the study methods, physiological signal studied, database used, various nonlinear techniques employed, feature extraction, and diagnostic performance parameters are discussed. The machine learning and deep learning based methods based on ECG and HRV signals have yielded the best performance and can be used for the development of computer-aided diagnosis of HT. This work provides insights that may be useful for the development of wearable for continuous cuffless remote monitoring of BP based on ECG and HRV signals.
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Javeed, Ashir, Shafqat Ullah Khan, Liaqat Ali, Sardar Ali, Yakubu Imrana, and Atiqur Rahman. "Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions." Computational and Mathematical Methods in Medicine 2022 (February 3, 2022): 1–30. http://dx.doi.org/10.1155/2022/9288452.
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One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. Traditional medical procedures (angiography) for the diagnosis of heart disease have higher cost as well as serious health concerns. Therefore, researchers have developed various automated diagnostic systems based on machine learning (ML) and data mining techniques. ML-based automated diagnostic systems provide an affordable, efficient, and reliable solutions for heart disease detection. Various ML, data mining methods, and data modalities have been utilized in the past. Many previous review papers have presented systematic reviews based on one type of data modality. This study, therefore, targets systematic review of automated diagnosis for heart disease prediction based on different types of modalities, i.e., clinical feature-based data modality, images, and ECG. Moreover, this paper critically evaluates the previous methods and presents the limitations in these methods. Finally, the article provides some future research directions in the domain of automated heart disease detection based on machine learning and multiple of data modalities.
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Gori, O., R. Raggini, R. Sant’Angelo, G. Tricomi, G. De Paoli, S. Grittani, and G. Piraccini. "Improve health care access is possible. A case report." European Psychiatry 64, S1 (April 2021): S791. http://dx.doi.org/10.1192/j.eurpsy.2021.2092.
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IntroductionWithin the hub and spoke organizational model provided by the Emilia Romagna Region for assistance to people with autism spectrum disorder (ASD), Cesena ward of psychiatry represents the hospital Hub. Here, a dedicated team (doctors, psychologist, case manager) creates individualized pathways to ensure second-level specialist diagnostics and the management of comorbidities affecting subjects diagnosed with Autism Spectrum Disorder and Intellectual Disability (ID).ObjectivesWe report the case of a 23-year-old man, who from the age of 6 was opposed to any instrumental diagnostic investigation.MethodsIn order to guarantee the patient’s full collaboration in carrying out essential diagnostic activities, short behavioural paths were created including video modelling. The Vi.Co app was used and a new app was created to target behaviors that could not be included in Vi.CoResultsIt was thus possible to make the patient compliant with the execution of blood samples, ECG, MRI of the brain in sedation and CT dental scan.ConclusionsIn our case, communication support systems and behavioral strategies have proved to be excellent allies in significantly improving the quality of care for our young patient. Considering the worst prognosis of pathologies and the reduced life expectancy of subjects suffering from ASD and ID, known in the literature, in our opinion, the first essential step becomes facilitating access to care for these patients.DisclosureNo significant relationships.