Key result
New wearable multichannel PCG and ECG device improves noise suppression and CVD pre-screening accuracy.
Why the study?
Does a wearable PCG and ECG device with an iterative Wiener filter and CNN classifier improve noise suppression and cardiovascular disease pre-screening accuracy?
Does a wearable PCG and ECG device with an iterative Wiener filter and CNN classifier improve noise suppression and cardiovascular disease pre-screening accuracy?
A novel wearable PCG and ECG device utilizing an iterative Wiener filter and CNN classifier demonstrates improved noise suppression for cardiovascular disease pre-screening.
Should not yet alter CVD pre-screening practice; leaves open prospective validation of wearable devices.
In this paper, we present a new wearable multichannel phonocardiography (PCG) and electrocardiography (ECG) device for cardiovascular disease (CVD) pre-screening and monitoring developed recently by researchers at Curtin University in collaboration with Ticking Heart, a health-tech start-up. An iterative Wiener filter based noise cancelation algorithm is proposed to improve the integrity of heart sound signals. We show that compared with an existing approach, the proposed algorithm has a better performance in suppressing the noise at 200-300 Hz. A convolutional neural network based classifier is implemented which exploits both the ECG and PCG signals to improve the pre-screening accuracy of CVD.
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Rong et al. (2023) studied Cardiovascular disease. Wearable multichannel PCG and ECG device with iterative Wiener filter and CNN classifier vs. Existing approach was evaluated on Noise suppression at 200-300 Hz and pre-screening accuracy. A new wearable multichannel PCG and ECG device utilizing an iterative Wiener filter and CNN classifier improved noise suppression at 200-300 Hz and pre-screening accuracy for cardiovascular disease.
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