Please use this identifier to cite or link to this item: http://localhost:80/xmlui/handle/123456789/1181
Title: ECG Beat Classification Using Cross-Wavelet And LVQ
Authors: DATTA, Saibal
Keywords: Publications
Cross-Wavelet Spectrum
Cross-Wavelet Coherence Spectrum
LVQ
Saibal Dutta
Issue Date: 10-Dec-2016
Abstract: This paper describes an automatic classification system based on combination of cross-wavelet and Learning Vector Quantization (LVQ) for the purpose of automatic heartbeat detection. The feature extractor is based on cross-wavelet approach, using the time frequency information. The ANN classifier uses a Learning Vector Quantization (LVQ) method which classifies the ECG beats into two categories: normal beats and abnormal beats. The ECG (electrocardiogram) signals in the MIT-BIH arrhythmia database are adopted as reference data. Total 98530 heart beats are used for testing the above classifier. The total classification accuracy (TCA) was about 91.66%.
URI: http://hdl.handle.net/123456789/1181
ISBN: 978 -93-86171-12-2
Appears in Collections:Electrical Engineering (Publications)

Files in This Item:
File Description SizeFormat 
IC3_ECG BEAT CLASSIFICATION USING CROSS-WAVELET AND LVQ.pdf577.8 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.