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

A direct waveform mean-shape vector quantization (MSVQ) is proposed here as an alternative for electrocardiographic (ECG) signal compression. In this method, the mean values for short ECG signal segments are quantized as scalars and compression of the single-lead ECG by average beat substraction and residual differencing their waveshapes coded through a vector quantizer. An entropy encoder is applied to both, mean and vector codes, to further increase compression without degrading the quality of the reconstructed signals. In this paper, the fundamentals of MSVQ are discussed, along with various parameters specifications such as duration of signal segments, the wordlength of the mean-value quantization and the size of the vector codebook. The method is assessed through percent-residual-difference measures on reconstructed signals, whereas its computational complexity is analyzed considering its real-time implementation. As a result, MSVQ has been found to be an efficient compression method, leading to high compression ratios (CRs) while maintaining a low level of waveform distortion and, consequently, preserving the main clinically interesting features of the ECG signals. CRs in excess of 39 have been achieved, yielding low data rates of about 140 bps. This compression factor makes this technique especially attractive in the area of ambulatory monitoring.

Mean-shape vector quantizer for ECG signal compression

Wavelet threshold based TDL and TDR algorithms for real-time ECG signal compression

An electrocardiogram (ECG) data compression scheme is presented using the gain-shape vector quantization. The proposed approach utilizes the fact that ECG signals generally show redundancy among adjacent heartbeats and adjacent samples. An ECG signal is QRS detected and segmented according to the detected fiducial points. The segmented heartbeats are vector quantized, and the residual signals are calculated and encoded using the AREA algorithm. The experimental results show that with the proposed method both visual quality and the objective quality are excellent even in low bit rates. An average PRD of 5.97% at 127 b/s is obtained for the entire 48 records in the MIT-BIH database. The proposed method also outperforms others for the same test dataset.

Beat-based ECG compression using gain-shape vector quantization

In spite of development in digital storage and communication technology, the demand for data compression is ever increasing. The ECG data requires about 40–50 MB per channel space for 24-h recording. Limitations of storage size, higher bandwidth and the extra transmission time to these signals over different communication channels force to study an efficient compression algorithm. The primary objective is to retain the most useful clinical information while compressing the ECG signals to an acceptable size. The literature proposes many algorithms to implement ECG compression. It is the observation that, among all, the wavelet-based algorithms provide better compression performance. This paper is a review of most promising algorithms of ECG compression with emphasis to wavelet-based ECG signal compression.

S. Saha

Papers

ECG compression by multirate processing of beats