Feature extraction from ECG signals using wavelet transforms for disease diagnostics

This paper deals with a modified combined wavelet transform technique that has been developed to analyse multilead electrocardiogram signals for cardiac disease diagnostics. Two wavelets have been used, i.e. a quadratic spline wavelet (QSWT) for QRS detection and the Daubechies six coefficient (DU6)...

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Published in:International journal of systems science 2002-10, Vol.33 (13), p.1073-1085
Main Authors: Saxena, S. C., Kumar, V., Hamde, S. T.
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Language:English
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description This paper deals with a modified combined wavelet transform technique that has been developed to analyse multilead electrocardiogram signals for cardiac disease diagnostics. Two wavelets have been used, i.e. a quadratic spline wavelet (QSWT) for QRS detection and the Daubechies six coefficient (DU6) wavelet for P and T detection. After detecting the fundamental electrocardiogram waves, the desired electrocardiogram parameters for disease diagnostics are extracted. The software has been validated by extensive testing using the CSE DS-3 database and the MIT/BIH database. A procedure has been evolved using electrocardiogram parameters with a point scoring system for diagnosis of cardiac diseases, namely tachycardia, bradycardia left ventricular hypertrophy, and right ventricular hypertrophy. As the diagnostic results are not yet disclosed by the CSE group, two alternate diagnostic criteria have been used to check the diagnostic authenticity of the test results. The consistency and reliability of the identified and measured parameters were confirmed when both the diagnostic criteria gave the same results
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title Feature extraction from ECG signals using wavelet transforms for disease diagnostics
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