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.

Acharya, A.U.Kumar, A.Subbanna Bhat, P.Lim, C.M.Iyengar, S.S.Kannathal, N.Krishnan, S.M.2026-02-05ClassificaMedical and Biological Engineering and Computing, 2004, 42, 3, pp. 288-2931400118https://doi.org/10.1007/BF02344702https://idr.nitk.ac.in/handle/123456789/279482004Cardiovascular systemDiagnosisDiseasesFuzzy setsNeural networksNeurologyCardiac abnormalitiesHeart rate signalsBiomedical engineeringarticleartificial neural networkautonomic nervous systemcomplete heart blockcomputer analysiscongestive cardiomyopathycontrolled studydata analysisdata basediagnostic accuracydisease classificationelectrocardiographyentropyheart atrium fibrillationheart diseaseheart left bundle branch blockheart rate variabilityheart rhythmheart ventricle extrasystoleheart ventricle fibrillationmathematical computingnon invasive measurementsick sinus syndromesignal processingsinus rhythmArrhythmiaDiagnosis, DifferentialElectrocardiography, AmbulatoryFuzzy LogicHumansNeural Networks (Computer)Signal Processing, Computer-AssistedThe 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.