Liu Xinggu, Long Zhiming, Li Zongyuan, Huang Shudong, Wang Zhuqing
Med+X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Mechanic Engineering Department, University of Sichuan, Chengdu, Sichuan, China.
Technol Health Care. 2023;31(1):269-281. doi: 10.3233/THC-220316.
Wearable devices that monitor heart health of cardiac disease patients in real time are in great demand.
We propose an algorithm of improved segment periodical matrix construction for irregular electrocardiogram (ECG) signal denoising.
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.
The results demonstrate a higher noise reduction effect with lower signal distortions of our methods compared to several singular value decomposition counterpart approaches.
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.
对实时监测心脏病患者心脏健康的可穿戴设备有巨大需求。
我们提出一种用于不规则心电图（ECG）信号去噪的改进分段周期矩阵构建算法。
在基于每个RR间期分割心跳以构建周期段矩阵时，经滤波的ECG信号在奇异值分解后通过最大奇异值进行重构。
结果表明，与几种奇异值分解对应方法相比，我们的方法具有更高的降噪效果和更低的信号失真。
我们的方法通过对实时ECG传感中的复杂噪声（如肌电伪迹）进行去噪，在提高可穿戴设备诊断准确性方面具有巨大潜力。
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