ECG compression using rule based thresholding of wavelet coefficients

Abstract

The electrocardiogram (ECG), one of the most vital medical signals, represents the electrical activity of a heart. ECG is a well-established diagnostic tool for cardiac abnormality. For the goal of efficient and convenient processing ofECG, automated and computerized ECG processing has become a major topic of research in the field of biomedical engineering. Modem clinical systems require the storage and transmission of large amount of ECG signals. Efficient data compression is needed in order to reduce the amount of data. In ECG signal compression algorithms, the aim is to reach maximum compression ratio, while keeping the relevant diagnostic information in the reconstructed signal. Wavelets have recently emerged as powerful tool for signal compression. In this work, an ECG compression algorithm is presented which is based on energy compaction property of the wavelet coefficients. The wavelet transform decomposes the signal into multi-resolution bands. The lowest resolution band (approximation band) is the smallest band in size and it includes high amplitude approximation coefficients. The wavelet coefficients other than these included by the approximation band, detail coefficients, have small magnitudes. Most of the energy is captured by the approximation coefficients of the lowest resolution band. In this work, we develop three threshold selection rules based on the energy compaction property of the wavelet coefficients. All the rules are applied to lead II of different records of MIT-BIH Arrhythmia Database. Among the three rules, the best rule which offers high compression ratio (CR) with low percent root mean square difference (PRD) than the other two rules is selected. A set of 30 records is taken as test data from MIT-BIH Arrhythmia Database for testing the compression algorithm by the best rule. A compression mtio of 15.12:1 is achieved with a very good reconstructed signal quality (pRD=2.33%). The algorithm provides improved performance in terms of computational efficiency and compression rate where the clinically significant features in the reconstructed ECG signal are preserved. The proposed method yields to good results in comparison with other wavelet transform based compression methods described in the literature.