IEEE Trans Biomed Eng. 2022 Aug;69(8):2512-2523. doi: 10.1109/TBME.2022.3148171. Epub 2022 Jul 18.
The accurate detection of physiologically-related events in photopletismographic (PPG) and phonocardiographic (PCG) signals, recorded by wearable sensors, is mandatory to perform the estimation of relevant cardiovascular parameters like the heart rate and the blood pressure. However, the measurement performed in uncontrolled conditions without clinical supervision leaves the detection quality particularly susceptible to noise and motion artifacts. This work proposes a new fully-automatic computational framework, based on convolutional networks, to identify and localize fiducial points in time as the foot, maximum slope and peak in PPG signal and the S1 sound in the PCG signal, both acquired by a custom chest sensor, described recently in the literature by our group. The event detection problem was reframed as a single hybrid regression-classification problem entailing a custom neural architecture to process sequentially the PPG and PCG signals. Tests were performed analysing four different acquisition conditions (rest, cycling, rest recovery and walking). Cross-validation results for the three PPG fiducial points showed identification accuracy greater than 93 % and localization error (RMSE) less than 10 ms. As expected, cycling and walking conditions provided worse results than rest and recovery, however reaching an accuracy greater than 90 % and a localization error less than 15 ms. Likewise, the identification and localization error for S1 sound were greater than 90 % and less than 25 ms. Overall, this study showcased the ability of the proposed technique to detect events with high accuracy not only for steady acquisitions but also during subject movements. We also showed that the proposed network outperformed traditional Shannon-energy-envelope method in the detection of S1 sound, reaching detection performance comparable to state of the art algorithms. Therefore, we argue that coupling chest sensors and deep learning processing techniques may disclose wearable devices to unobtrusively acquire health information, being less affected by noise and motion artifacts.
在可穿戴传感器记录的光电容积脉搏（PPG）和心音（PCG）信号中，准确检测与生理相关的事件对于估计心率和血压等相关心血管参数至关重要。然而，在不受临床监督的非受控条件下进行的测量，使得检测质量特别容易受到噪声和运动伪影的影响。本工作提出了一种新的完全自动的计算框架，基于卷积网络，用于识别和定位时间上的基准点，如 PPG 信号中的足、最大斜率和峰值，以及 PCG 信号中的 S1 音，这两个信号都是由我们小组最近在文献中描述的定制胸部传感器采集的。事件检测问题被重新定义为一个单一的混合回归-分类问题，需要一个定制的神经架构来顺序处理 PPG 和 PCG 信号。测试是在分析了四种不同采集条件（休息、骑车、休息恢复和步行）的情况下进行的。对于三个 PPG 基准点的交叉验证结果表明，识别准确率大于 93%，定位误差（均方根误差）小于 10ms。不出所料，骑车和步行条件的结果比休息和恢复条件差，但准确率大于 90%，定位误差小于 15ms。同样，S1 音的识别和定位误差大于 90%，小于 25ms。总的来说，这项研究展示了所提出的技术不仅能够在稳定采集时，而且能够在受试者运动时，以高精度检测事件的能力。我们还表明，所提出的网络在 S1 音的检测中优于传统的 Shannon 能量包络方法，达到了与最先进算法相当的检测性能。因此，我们认为，结合胸部传感器和深度学习处理技术，可使可穿戴设备能够更轻松地获取健康信息，而不受噪声和运动伪影的影响。
