An ECG compression method exploiting a QRS detector for sparse dictionary learning

Abstract

This paper presents a Compressed Sensing (CS) method for electrocardiogram (ECG) using sparse dictionary learning for dimensionality reduction that exploits frames of one heart-depolarization cycle. The ECG signal is first acquired at the Nyquist rate and then segmented into multiple frames, with each frame aligned depending on the QRS complex positions detected by the Pan-Tompkins algorithm. During the training phase, a dictionary built through the Discrete Cosine Transform (DCT) is reduced through the Multiple Measurement Vector (MMV) algorithm. The compression employs the Deterministic Binary Block Diagonal (DBBD) matrix as a sensing matrix. The ECG frames are reconstructed by solving the MMV problem, and individual frames are aligned based on the R-peak value. This proposed method enables efficient data compression while preserving essential ECG signal information. The method achieves a high compression ratio of 12 while maintaining a low PRD, demonstrating its efficiency without compromising signal quality. Reconstruction quality was evaluated using both Weighted Diagnostic Distortion (WDD) and the Wavelet Energy–based Diagnostic Distortion (WEDD) metrics, showing very good to good WDD values up to CR = 12 and WEDD values indicating very good to excellent reconstruction.