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

doi:10.1016/j.measurement.2025.119177

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

2025-10-05

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DOI：10.1016/j.measurement.2025.119177

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• Compressed sensing using sparse dictionary learning with Discrete Cosine Transform (DCT) and MMV-OMP (Multiple Measurement Vector - Orthogonal Matching Pursuit) allows efficient ECG compression by using frames from one heart-depolarization cycle, aligned by the QRS complex. • A Pan-Tompkins detector, improved with multi-lead adjustment, is used to accurately find QRS complexes. • The Deterministic Binary Block Diagonal (DBBD) matrix is chosen as a sensing matrix since its combination with DCT functions provides good compression performance while keeping the design simple and low in complexity. • Compressed frames from each ECG recording are reconstructed simultaneously using MMV-OMP. • The method reaches a high compression ratio of up to 12, while keeping PRD (Percent Root-Mean-Square Difference) low, while diagnostic quality is well preserved, with WDD (Weighted Diagnostic Distortion ) results rated from good to very good, and WEDD (Wavelet Energy–based Diagnostic Distortion ) indicating very good to excellent reconstruction.

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

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