Time–frequency localization using three-tap biorthogonal wavelet filter bank for electrocardiogram compressions

A joint time–frequency localized three-band biorthogonal wavelet filter bank to compress Electrocardiogram signals is proposed in this work. Further, the use of adaptive thresholding and modified run-length encoding resulted in maximum data volume reduction while guaranteeing reconstructing quality. Using signal-to-noise ratio, compression ratio (C(R)), maximum absolute error (E(MA)), quality score (Q(s)), root mean square error, compression time (C(T)) and percentage root mean square difference the validity of the proposed approach is studied. The experimental results deduced that the performance of the proposed approach is better when compared to the two-band wavelet filter bank. The proposed compression method enables loss-less data transmission of medical signals to remote locations for therapeutic usage.

Efficient QRS complex detection algorithm based on Fast Fourier Transform

An ECG signal, generally filled with noise, when de-noised, enables a physician to effectively determine and predict the condition and health of the heart. This paper aims to address the issue of denoising a noisy ECG signal using the Fast Fourier Transform based bandpass filter. Multi-stage adaptive peak detection is then applied to identify the R-peak in the QRS complex of the ECG signal. The result of test simulations using the MIT/BIH Arrhythmia database shows high sensitivity and positive predictivity (PP) of 99.98 and 99.96% respectively, confirming the accuracy and reliability of proposed algorithm for detecting R-peaks in the ECG signal.