Paper Info
Title
Classification of heart rate data using artificial neural network and fuzzy equivalence relation
Abstract
The electrocardiogram is a representative signal containing information about the condition of the heart. The shape and size of the P-QRS-T wave, the time intervals between its various peaks, etc. may contain useful information about the nature of disease afflicting the heart. However, these subtle details cannot be directly monitored by the human observer. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the signal parameters, extracted and analysed using computers, are highly useful in diagnostics. This paper deals with the classification of certain diseases using artificial neural network (ANN) and fuzzy equivalence relations. The heart rate variability is used as the base signal from which certain parameters are extracted and presented to the ANN for classification. The same data is also used for fuzzy equivalence classifier. The feedforward architecture ANN classifier is seen to be correct in about 85% of the test cases, and the fuzzy classifier yields correct classification in over 90% of the cases.
Year
DOI
Venue
2003
10.1016/S0031-3203(02)00063-8
Pattern Recognition
Keywords
Field
DocType
Heart rate,Pattern recognition,ECG,Neural network,Fuzzy equivalence,Disease classification
Fuzzy equivalence,Pattern recognition,Heart rate variability,Artificial intelligence,Test case,Heart rate,Classifier (linguistics),Artificial neural network,Observer (quantum physics),Mathematics,Machine learning,Feed forward
Journal
Volume
Issue
ISSN
36
1
0031-3203
Citations 
PageRank 
References 
74
6.12
2
Authors
5
Name
Order
Citations
PageRank
Rajendra Acharya U14666296.34
P. Subbanna Bhat223320.55
S.S. Iyengar32923381.93
Ashok Rao420019.14
Sumeet Dua527524.31