用卷积自编码器提升耳道心电图信噪比，改善心率检测精度。
In-ear ECG Signal Enhancement with Denoising Convolutional Autoencoders
- 设计去噪卷积自编码器，从耳道心电信号中提取干净心电波形。
- 信噪比中位数提升5.9分贝，心率估计误差降低近70%。
- 适用于可穿戴设备，对异常心电形态也有良好重建能力。
心脏偶极子可传播至耳朵，使耳道成为消费级可穿戴设备记录心电图（ECG）的常见位置。然而，耳道心电记录因信号幅值小且受脑电（EEG）等生理信号干扰，常存在显著噪声，影响心血管特征提取。本文提出一种去噪卷积自编码器（DCAE），用于增强耳道心电信息，生成更清晰的心电输出。模型在45名健康参与者的数据集上评估，包含耳道心电与对应干净导联I心电。结果表明，信噪比中位数提升5.9分贝；心率估计平均绝对误差降低近70%，R波峰值检测精度中位数达90%。此外，使用真实心电数据合成、加入粉红噪声并包含异常心律形态的仿真数据集训练验证，结果表明该模型能有效去除噪声源，并实现具有临床意义的心电波形重建。
原文摘要 · Abstract (English)
The cardiac dipole has been shown to propagate to the ears, now a common site for consumer wearable electronics, enabling the recording of electrocardiogram (ECG) signals. However, in-ear ECG recordings often suffer from significant noise due to their small amplitude and the presence of other physiological signals, such as electroencephalogram (EEG), which complicates the extraction of cardiovascular features. This study addresses this issue by developing a denoising convolutional autoencoder (DCAE) to enhance ECG information from in-ear recordings, producing cleaner ECG outputs. The model is evaluated using a dataset of in-ear ECGs and corresponding clean Lead I ECGs from 45 healthy participants. The results demonstrate a substantial improvement in signal-to-noise ratio (SNR), with a median increase of 5.9 dB. Additionally, the model significantly improved heart rate estimation accuracy, reducing the mean absolute error by almost 70% and increasing R-peak detection precision to a median value of 90%. We also trained and validated the model using a synthetic dataset, generated from real ECG signals, including abnormal cardiac morphologies, corrupted by pink noise. The results obtained show effective removal of noise sources with clinically plausible waveform reconstruction ability.
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