The method of truncated singular value decomposition (SVD) is proposed for electrocardiogram (ECG) data compression. The signal decomposition capability of SVD is exploited to extract the significant feature components of the ECG by decomposing the ECG into a set of basic patterns with associated scaling factors. The signal information is mostly concentrated within a certain number of singular values with related singular vectors due to the strong interbeat correlation among ECG cycles. Therefore, only the relevant parts of the singular triplets need to be retained as the compressed data for retrieving the original signals. The insignificant overhead can be truncated to eliminate the redundancy of ECG data compression. The Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database was applied to evaluate the compression performance and recoverability in the retrieved ECG signals. The approximate achievement was presented with an average data rate of 143.2 b/s with a relatively low reconstructed error. These results showed that the truncated SVD method can provide efficient coding with high-compression ratios. The computational efficiency of the SVD method in comparing with other techniques demonstrated the method as an effective technique for ECG data storage or signals transmission.

ECG data compression using truncated singular value decomposition

An efficient compression of ECG signals using deep convolutional autoencoders

A two-dimensional (2-D) wavelet-based electrocardiogram (ECG) data compression method is presented which employs a modified set partitioning in hierarchical trees (SPIHT) algorithm. This modified SPIHT algorithm utilizes further the redundancy among medium- and high-frequency subbands of the wavelet coefficients and the proposed 2-D approach utilizes the fact that ECG signals generally show redundancy between adjacent beats and between adjacent samples. An ECG signal is cut and aligned to form a 2-D data array, and then 2-D wavelet transform and the modified SPIHT can be applied. Records selected from the MIT-BIH arrhythmia database are tested. The experimental results show that the proposed method achieves high compression ratio with relatively low distortion and is effective for various kinds of ECG morphologies.

A 2-D ECG compression method based on wavelet transform and modified SPIHT

Time-frequency signal analysis

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

The main goal of any electrocardiogram (ECG) compression algorithm is to reduce the bit rate while keeping the signal distortion at a clinically acceptable level. Percentage root mean square difference (PRD), the commonly used figure of merit, does not directly reveal whether the clinically significant ECG waveform information is preserved or not. We present the results of a study of ECG compression using an upper bound on the PRD. This bound is based on the initial performance of the algorithm and could be specified by the clinician after correlating the quality of the compressed versions of the ECG and the resulting PRD.

E. Kresch

Papers

Constrained ECG compression using best adapted wavelet packet bases