Rocha Leandro Giacomini, Paim Guilherme, Biswas Dwaipayan, Bampi Sergio, Catthoor Francky, Van Hoof Chris, Van Helleputte Nick
Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:1068-1071. doi: 10.1109/EMBC46164.2021.9630942.
Continuous and non-invasive cardiovascular monitoring has gained attention due to the miniaturization of wearable devices. Particularly, wrist-worn photoplethysmography (PPG) sensors present an alternative to electrocardiogram recording for heart rate (HR) monitoring as it is cheaper and non-intrusive for daily activities. Yet, the accuracy of PPG measurements is heavily affected by motion artifacts which are inherent to ambulatory environments. In this paper, we propose a low-complexity LSTM-only neural network for HR estimation from a single PPG channel during intense physical activity. This work explored the trade-off between model complexity and accuracy by exploring different model dataflows, number of layers, and number of training epochs to capture the intrinsic time-dependency between PPG samples. The best model achieves a mean absolute error of 4.47 ± 3.68 bpm when evaluated on 12 IEEE SPC subjects.Clinical relevance- This work aims to improve the quality of HR inference from PPG signals using neural network, enabling continuous vital signal monitoring with little interference in daily activities from embedded monitoring devices.
由于可穿戴设备的小型化，连续无创心血管监测受到了关注。特别是，腕戴式光电容积脉搏波描记法（PPG）传感器为心率（HR）监测提供了一种替代心电图记录的方法，因为它更便宜且对日常活动无侵入性。然而，PPG测量的准确性受到动态伪影的严重影响，而动态伪影是动态环境中固有的。在本文中，我们提出了一种低复杂度的仅含长短期记忆网络（LSTM）的神经网络，用于在剧烈体育活动期间从单个PPG通道估计心率。这项工作通过探索不同的模型数据流、层数和训练轮数来捕捉PPG样本之间内在的时间依赖性，从而研究了模型复杂度和准确性之间的权衡。在对12名IEEE SPC受试者进行评估时，最佳模型的平均绝对误差为4.47±3.68次/分钟。临床相关性——这项工作旨在利用神经网络提高从PPG信号推断心率的质量，从而实现连续生命体征监测，同时嵌入式监测设备对日常活动的干扰很小。
