NDLI: Classification of heart sounds using a multilayer feed-forward neural network

Content Provider	IEEE Xplore Digital Library
Author	Shamsuddin, N. Mustafa, M.N. Husin, S. Taib, M.N.
Copyright Year	2005
Description	Author affiliation: Electronics & Information Technol. Centre, Selangor, Malaysia (Shamsuddin, N.; Mustafa, M.N.; Husin, S.)
Abstract	Cardiovascular diseases are among chronic diseases that seriously threaten human health. Medical experts sometimes listen to heart sounds through auscultation system, either from stethoscope or PCG (phonocardiogram), as one of the ways in diagnosing cardiovascular diseases. The auscultation process is subjective and largely depends on the experience, skill, knowledge or hearing ability of the physician to interpret heart conditions. This paper examines heart valve-related diseases using a multilayer feed-forward neural network (MFNN). The heart sounds were digitally filtered with moving average filter, segmented with sliding window and then reduced to an arbitrary frame of 64 points. After that, it is pre-processed by fast Fourier transform prior to feeding into the neural network for classification. To check for network robustness, the heart sound samples were injected with different level of noise. A total of 55 types of sample data were generated and used for classification. The study produces a 100% correct classification of eleven heart-valve diseases.
Starting Page	87
Ending Page	90
File Size	1699455
Page Count	4
File Format	PDF
ISBN	0780393708
DOI	10.1109/ASENSE.2005.1564512
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-09-05
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Heart Neural networks Cardiac disease Humans Feedforward neural networks Cardiovascular diseases Feedforward systems Digital filters Medical diagnostic imaging Multi-layer neural network
Content Type	Text
Resource Type	Article

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