title: Cardiac state diagnosis using adaptive neuro-fuzzy technique.
abstract: Analysis of heart rate has become a popular noninvasive tool for assessing the activities of the autonomic nervous system (ANS). These signals may either contain indicators of a current disease or even warnings about impending diseases. However, to manually study and pinpoint heart abnormalities in voluminous data is strenuous and time consuming. Here, an adaptive neuro-fuzzy network is used to classify heart abnormalities in 10 different cardiac states and shown to be effective. The results indicate a high level of efficacy of tools used with an accuracy level of more than 94%.
authors.affiliation: ECE Division, NgeeAnn Polytechnic, 535, Clementi Road, Singapore 599489, Singapore. kna2@np.edu.sg <kna2@np.edu.sg>
authors.forename: N
authors.initials: N
authors.lastname: Kannathal
authors.forename: C M
authors.initials: CM
authors.lastname: Lim
authors.forename: U
authors.initials: U
authors.lastname: Rajendra Acharya
authors.forename: P K
authors.initials: PK
authors.lastname: Sadasivan
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis* / Q000503:physiopathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D017143:Fuzzy Logic; D006339:Heart Rate; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*