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

Xinggu Liu^1,2, Zhiming Long^1,2, Zongyuan Li²

¹Med+X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine

|August 29, 2022

Summary

This study introduces an improved algorithm for denoising electrocardiogram (ECG) signals from wearable devices. The method enhances real-time heart health monitoring by reducing noise and signal distortion.

Keywords:

Segment periodical matrix irregular ECG singular value decomposition

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Area of Science:

Biomedical Engineering
Signal Processing

Background:

Real-time monitoring of heart health for cardiac patients using wearable devices is crucial.
Existing methods face challenges with complex noise in electrocardiogram (ECG) signals.

Purpose of the Study:

To develop an improved algorithm for electrocardiogram (ECG) signal denoising.
The algorithm focuses on periodical matrix construction for irregular ECG signals.

Main Methods:

The proposed method involves splitting heartbeats based on RR intervals for periodical segments matrix construction.
Singular value decomposition (SVD) is used, with signal reconstruction based on the maximum singular value.

Main Results:

The algorithm demonstrated superior noise reduction compared to other SVD-based approaches.
Lower signal distortions were observed with the proposed method.

Conclusions:

The developed algorithm shows significant potential for improving the diagnostic accuracy of wearable ECG devices.
It effectively denoises complex artifacts like electromyography noise in real-time ECG sensing.