Compression of ECG Signal Based on Compressive Sensing and the Extraction of Significant Features

Abstract

Diagnoses of heart diseases can be done effectively on long term recordings of ECG signals that preserve the signals' morphologies. In these cases, the volume of the ECG data produced by the monitoring systems grows significantly. To make the mobile healthcare possible, the need for efficient ECG signal compression algorithms to store and/or transmit the signal efficiently has been rising exponentially. Currently, ECG signal is acquired at Nyquist rate or higher, thus introducing redundancies between adjacent heartbeats due to its quasi-periodic structure. Existing compression methods remove these redundancies by achieving compression and facilitate transmission of the patient's imperative information. Based on the fact that these signals can be approximated by a linear combination of a few coefficients taken from different basis, an alternative new compression scheme based on Compressive Sensing (CS) has been proposed. CS provides a new approach concerned with signal compression and recovery by exploiting the fact that ECG signal can be reconstructed by acquiring a relatively small number of samples in the "sparse" domains through well-developed optimization procedures. In this paper, a single-lead ECG compression method has been proposed based on improving the signal sparisty through the extraction of the signal significant features. The proposed method starts with a preprocessing stage that detects the peaks and periods of the Q, R and S waves of each beat. Then, the QRS-complex for each signal beat is estimated. The estimated QRS-complexes are subtracted from the original ECG signal and the resulting error signal is compressed using the CS technique. Throughout this process, DWT sparsifying dictionaries have been adopted. The performance of the proposed algorithm, in terms of the reconstructed signal quality and compression ratio, is evaluated by adopting DWT spatial domain basis applied to ECG records extracted from the MIT-BIH Arrhythmia Database. The results indicate that average compression ratio of 11:1 with PRD1 = 1.2% are obtained. Moreover, the quality of the retrieved signal is guaranteed and the compression ratio achieved is an improvement over those obtained by previously reported algorithms. Simulation results suggest that CS should be considered as an acceptable methodology for ECG compression. M. M. Abo-Zahhad et al.

Splines in Compressed Sensing

“…As we aim to reconstruct the data with minimum measurements it is found meaningless for going CR above of 60%. The wavelets are decomposed at level 4 as in [18]. Ratios for the MIT data 101m, Table.3 indicate the same for 101m.…”

“…There are lots of papers published in the field of CS based ECG reconstruction most of them considers CS as a technique for compression rather than a sensing paradigm [12][13][14][15][16][17]. Recently in May-2015 Abo-Zahhad, et.al published their work [18] which is showing some promising results, In that they have estimated the QRS complex from the available data and subtracted it from the original and CS techniques are applied on the error signal. The major drawback here is that prior information of the data has to known before, and thresholding has also been employed to increase sparsity.…”

A Dedicated Diagnostic Distortion Measure for the Evaluation of Ecg Signal Compression Techniques Adopted for Remote Diagnosis of Cardiovascular Diseases

“…It is based on improving the ECG signal sparsity using QRS-complex estimation based on the peaks and locations of Q, R and S waves. Then, the estimated QRS-complex is subtracted from the original ECG signal and the resulting differential signal is manipulated using CS technique as shown in Figure (3) where fewer measurements are determined from the resulting error signal [21][22]…”

A data-driven compressive sensing framework tailored for energy-efficient wearable sensing

“…This limits the rank of the sensing matrix required for preserving the Restricted Isometry Property (RIP), leading to limited compression ratio (CR). On the other hand, conventional CS frameworks [7,8,5] that adopt predetermined basis for reconstruction underestimate the intricacy of philological signals and overlook the criticality of individual variability to signal fidelity, which results in very limited reconstruction performance especially at high CR [6]. Our previous study [9] has shown that learned dictionaries can better approximate the underlying statistical model of input data.…”