小波融合CNN，精准去除可穿戴心电图噪声
Wavelet Integrated Convolutional Neural Network for ECG Signal Denoising
- 引入小波变换层提取心电信号特有频率特征
- 在-10到10 dB低信噪比下仍保持高效去噪
- 适合干电极可穿戴设备的实时心电降噪
可穿戴式干电极心电测量存在高强度噪声干扰问题。由于心电信号与噪声频段重叠，传统去噪方法效果有限。本文提出一种集成小波变换层的卷积神经网络模型，通过小波变换提取干净心电信号的特定频率特征，实现全频域噪声抑制。实验在信噪比（SNR）范围为-10至10 dB的噪声信号上验证，结果表明该方法在低SNR条件下去噪性能更优，能有效恢复真实心电行为。
原文摘要 · Abstract (English)
Wearable electrocardiogram (ECG) measurement using dry electrodes has a problem with high-intensity noise distortion. Hence, a robust noise reduction method is required. However, overlapping frequency bands of ECG and noise make noise reduction difficult. Hence, it is necessary to provide a mechanism that changes the characteristics of the noise based on its intensity and type. This study proposes a convolutional neural network (CNN) model with an additional wavelet transform layer that extracts the specific frequency features in a clean ECG. Testing confirms that the proposed method effectively predicts accurate ECG behavior with reduced noise by accounting for all frequency domains. In an experiment, noisy signals in the signal-to-noise ratio (SNR) range of -10-10 are evaluated, demonstrating that the efficiency of the proposed method is higher when the SNR is small.
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