A novel ECG data compression algorithm using best mother wavelet selection

Abstract

This paper presents a novel electrocardiogram data compression algorithm based on discrete wavelet transform using best mother wavelet selection for minimum percentage root mean square difference. Compression performance of the proposed algorithm has been evaluated using 48 records of ECG signal which are taken from MIT-BIH arrhythmia database. The proposed algorithm provides a fast Daubechies mother wavelet selection approach based on minimum value of percent root-mean-square difference. For effective encoding of transform coefficients, combination of backward difference and run length encoding is used. Average values of compression ratio, percent root mean square difference, quality score, root mean square error, percent root mean square difference normalized and signal to noise ratio offered by the proposed algorithm are 15.02, 0.23, 67.68, 2.19, 66.96 and 3.92 respectively over 48 records of ECG signal.