ECG Data Compression Using of Empirical Wavelet Transform for Telemedicine and e-Healthcare Systems

Abstract

In this article, a highly adaptable method the empirical wavelet transform (EWT) is utilized to compress electrocardiogram (ECG) data. EWT and run-length encoding (RLE)-based technique is used for data compression of ECG rhythms. EWT is chosen because it is highly adaptable and can decompose a non-stationary signal into different frequency modes efficiently. The modified RLE is used to acquire the high reduction performance. The projected method is tested with MIT-BIH arrhythmia database and experiments are carried out in MATLAB R2016b. Performance of the proposed algorithm is evaluated in terms of compression ratio (CR), percent root mean squire difference (PRD), signal-to-noise ratio (SNR), retained energy (RE) and quality score (QS). Result shows a high CR (31%), low PRD (0.0750) and high QS (414). Comparative analysis of the performance of projected technique with several existing techniques is also done, which shows that the proposed technique is superior in terms of PRD and CR. WT is also used to detect the R-peaks (location and amplitude) using amplitude thresholding. The program took 4.452793 s to run.