Enhanced wavelet transformation for feature extraction in highly variated ECG signal

This paper proposes an adaptive signal extraction method that uses Discrete Wavelet Transformation coupled with adaptive parameters to address variated heartwave signal due to varying heartrates. The characteristic features of the heartwave signal comprises of the P-wave, QRS-complex, Twave, onset and offset of P-wave and T-wave. Using statistically deduced parameters, the PR and QT interval parameters were incorporated where the signal extraction method can be made adaptive to varying heartrate that resulted in a very reliable signal extraction methods. Work was tested on the public database where individuals underwent treadmill testing and 95% of the heartwave signal characteristics were successfully extracted.