Wavelet-based electrocardiogram signal compression methods and their performances: A prospective review.

Cardiovascular disease (CVD) is one of the most widespread health problems with unpredictable and life-threatening consequences. The electrocardiogram (ECG) is commonly recorded for computer-aided CVD diagnosis, human emotion recognition and person authentication systems. For effective detection and diagnosis of cardiac diseases, the ECG signals are continuously recorded, processed, stored, and transmitted via wire/wireless communication networks. But long-term continuous cardiac monitoring system generates huge volume of ECG data daily. Therefore, a reliable and efficient ECG signal compression method is highly demanded to meet the real-time constraints including limited channel capacity, memory and battery-power of remote cardiac monitoring, ECG record management and telecardiology systems. In such scenarios, the main objective of the ECG signal compression is to reduce the data rate for effective transmission and/or storage purposes without significantly distorting the clinical features of different kinds of PQRST morphologies contained in the recorded ECG signal. Numerous ECG compression methods have been proposed by exploiting the intra-beat correlation, inter-beat correlation and intra-channel correlation of the ECG signals. This paper presents a prospective review of wavelet-based ECG compression methods and their performances based upon findings obtained from various experiments conducted using both clean and noisy ECG signals. This paper briefly describes different kinds of compression techniques used in the one-dimensional wavelet-based ECG compression methods. Then, the performance of each of the wavelet-based compression methods is tested and validated using the standard MIT-BIH arrhythmia databases and performance metrics. The pros and cons of different wavelet-based compression methods are demonstrated based upon the experimental results. Finally, various practical issues involved in the validation procedures, reconstructed signal quality assessment, and performance comparisons are highlighted by considering the future research studies based on the recent powerful digital signal processing techniques and computing platform. [ABSTRACT FROM AUTHOR]