Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:3387-3391. doi: 10.1109/EMBC48229.2022.9871678.
Photoplethysmography (PPG) is a non-invasive technique used in wearable devices to collect various vital signs, including heart rate and heart rate variability. The signal is highly susceptible to motion artifacts, which is inevitable in health monitoring and may lead to inaccurate decision-making. Studies in the literature proposed time series analysis, signal decomposition, and machine learning methods to reconstruct PPG signals or reduce noise. However, they are limited to short-term noisy signals or to noise caused by certain physical activities. In this paper, we propose a deep convolutional generative adversarial network (GAN) method to reconstruct distorted PPG signals. Our method exploits the temporal information extracted from the corrupted signal and preceding data to perform PPG reconstruction. The model is trained and tested using data collected by smartwatches in a home-based health monitoring application. We evaluate the proposed GAN method in comparison to three state-of-the-art PPG reconstruction methods. The evaluation includes noisy PPG signals with different durations and SNR values. The proposed method outperforms the other methods by obtaining the least error rates. The results indicate that the proposed method is effective for improving PPG signal quality to produce reliable heart rate and heart rate variability.
光电容积脉搏波描记术（PPG）是一种用于可穿戴设备的非侵入性技术，可收集各种生命体征，包括心率和心率变异性。该信号非常容易受到运动伪影的影响，这在健康监测中是不可避免的，可能导致决策不准确。文献中的研究提出了时间序列分析、信号分解和机器学习方法，以重建 PPG 信号或降低噪声。然而，它们仅限于短期噪声信号或某些身体活动引起的噪声。在本文中，我们提出了一种深度卷积生成对抗网络（GAN）方法来重建失真的 PPG 信号。我们的方法利用从受污染信号和先前数据中提取的时间信息来执行 PPG 重建。该模型使用基于家庭的健康监测应用程序中智能手表收集的数据进行训练和测试。我们将提出的 GAN 方法与三种最先进的 PPG 重建方法进行了评估。评估包括具有不同持续时间和 SNR 值的噪声 PPG 信号。提出的方法通过获得最低的错误率，优于其他方法。结果表明，该方法对于提高 PPG 信号质量以产生可靠的心率和心率变异性非常有效。
