The heart rate is a non-stationary signal, and its variation can contain indicators of current disease or warnings about impending cardiac diseases. The indicators can be present at all times or can occur at random, during certain intervals of the day. However, to study and pinpoint abnormalities in large quantities of data collected over several hours is strenuous and time consuming. Hence, heart rate variation measurement (instantaneous heart rate against time) has become a popular, non-invasive tool for assessing the autonomic nervous system. Computer-based analytical tools for the in-depth study and classification of data over day-long intervals can be very useful in diagnostics. The paper deals with the classification of cardiac rhythms using an artificial neural network and fuzzy relationships. The results indicate a high level of efficacy of the tools used, with an accuracy level of 80-85%. © IFMBE: 2004.
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Classification of cardiac abnormalities using heart rate signals
Journal Title
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Abstract
2004
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Keywords
Cardiovascular system, Diagnosis, Diseases, Fuzzy sets, Neural networks, Neurology, Cardiac abnormalities, Heart rate signals, Biomedical engineering, article, artificial neural network, autonomic nervous system, complete heart block, computer analysis, congestive cardiomyopathy, controlled study, data analysis, data base, diagnostic accuracy, disease classification, electrocardiography, entropy, heart atrium fibrillation, heart disease, heart left bundle branch block, heart rate variability, heart rhythm, heart ventricle extrasystole, heart ventricle fibrillation, mathematical computing, non invasive measurement, sick sinus syndrome, signal processing, sinus rhythm, Arrhythmia, Diagnosis, Differential, Electrocardiography, Ambulatory, Fuzzy Logic, Humans, Neural Networks (Computer), Signal Processing, Computer-Assisted
Citation
Medical and Biological Engineering and Computing, 2004, 42, 3, pp. 288-293