Classification of heart rate data using artificial neural network and fuzzy equivalence relation |
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Authors: | U Rajendra AcharyaAuthor VitaeP Subbanna BhatAuthor Vitae SS IyengarAuthor Vitae Ashok RaoAuthor VitaeSumeet DuaAuthor Vitae |
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Affiliation: | a Ngee Ann Polytechnic, 535 Clementi Road, Singapore, 599 489, Singapore b Karnataka Regional Engineering College, Surathkal, Srinivasnagar, India 574 157 c Department of Computer Science, Louisiana State University, 298 Coates Hall, Baton Rouge, LA 70808, USA d CEDT, IISc, Bangalore, India |
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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. |
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Keywords: | Heart rate Pattern recognition ECG Neural network Fuzzy equivalence Disease classification |
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