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.

Simple item page
dc.contributor.authorAcharya, A.U.
dc.contributor.authorKumar, A.
dc.contributor.authorSubbanna Bhat, P.
dc.contributor.authorLim, C.M.
dc.contributor.authorIyengar, S.S.
dc.contributor.authorKannathal, N.
dc.contributor.authorKrishnan, S.M.
dc.date.accessioned2026-02-05T11:00:19Z
dc.date.issuedClassification of cardiac abnormalities using heart rate signals
dc.description.abstract2004
dc.identifier.citationMedical and Biological Engineering and Computing, 2004, 42, 3, pp. 288-293
dc.identifier.issn1400118
dc.identifier.urihttps://doi.org/10.1007/BF02344702
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/27948
dc.subjectCardiovascular system
dc.subjectDiagnosis
dc.subjectDiseases
dc.subjectFuzzy sets
dc.subjectNeural networks
dc.subjectNeurology
dc.subjectCardiac abnormalities
dc.subjectHeart rate signals
dc.subjectBiomedical engineering
dc.subjectarticle
dc.subjectartificial neural network
dc.subjectautonomic nervous system
dc.subjectcomplete heart block
dc.subjectcomputer analysis
dc.subjectcongestive cardiomyopathy
dc.subjectcontrolled study
dc.subjectdata analysis
dc.subjectdata base
dc.subjectdiagnostic accuracy
dc.subjectdisease classification
dc.subjectelectrocardiography
dc.subjectentropy
dc.subjectheart atrium fibrillation
dc.subjectheart disease
dc.subjectheart left bundle branch block
dc.subjectheart rate variability
dc.subjectheart rhythm
dc.subjectheart ventricle extrasystole
dc.subjectheart ventricle fibrillation
dc.subjectmathematical computing
dc.subjectnon invasive measurement
dc.subjectsick sinus syndrome
dc.subjectsignal processing
dc.subjectsinus rhythm
dc.subjectArrhythmia
dc.subjectDiagnosis, Differential
dc.subjectElectrocardiography, Ambulatory
dc.subjectFuzzy Logic
dc.subjectHumans
dc.subjectNeural Networks (Computer)
dc.subjectSignal Processing, Computer-Assisted
dc.titleThe 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.

Files

Collections

Journal Articles