An improved adaptive periodical segment matrix algorithm for ECG denoising based on singular value decomposition

BACKGROUND: Wearable devices that monitor heart health of cardiac disease patients in real time are in great demand. OBJECTIVE: We propose an algorithm of improved segment periodical matrix construction for irregular electrocardiogram (ECG) signal denoising. METHOD: While splitting the heartbeat based on each RR interval for periodical segments matrix construction, the as-filtered ECG signal is reconstructed by the maximum singular value after a singular value decomposition. RESULTS: The results demonstrate a higher noise reduction effect with lower signal distortions of our methods compared to several singular value decomposition counterpart approaches. CONCLUSION: Our method has great potential to enhance wearable devices diagnosis accuracy by denoising the complex noises such as electromyography artifacts in real-time ECG sensing.