Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:3382-3386. doi: 10.1109/EMBC48229.2022.9871494.
Photoplethysmography (PPG) signal comprises physiological information related to cardiorespiratory health. High-quality PPG signals are necessary to extract cardiores-piratory information accurately. Motion artifacts can easily corrupt PPG signals due to human locomotion, leading to noise enriched, poor quality signals. Several rule-based and Machine-Learning (ML) - based approaches for PPG signal quality estimation are available, but those algorithms' efficacy is questionable. So, the authors propose a lightweight CNN architecture for signal quality assessment by employing a novel Quantum Pattern Recognition (QPR) technique. The proposed algorithm is validated on manually annotated data obtained from the University of Queensland database. A total of 28366, 5s signal segments are preprocessed and transformed into image files of 20 x 500 pixels for input to the 2D CNN architecture. The developed model classifies the PPG signal as 'good' and 'bad' with an accuracy of 98.3% with 99.3% sensitivity, 94.5% specificity and 98.9% F1-score. The experimental analysis concludes that slim module based architecture and novel Spatio-temporal pattern recognition technique improve the system's performance. The proposed approach is suitable for a resource-constrained wearable implementation.
光电容积脉搏波（PPG）信号包含与心肺健康相关的生理信息。为了准确提取心肺信息，需要高质量的 PPG 信号。由于人体运动，运动伪影很容易使 PPG 信号失真，导致信号噪声丰富，质量较差。现已有一些基于规则和基于机器学习（ML）的 PPG 信号质量估计方法，但这些算法的效果值得怀疑。因此，作者提出了一种轻量级的卷积神经网络（CNN）架构，通过采用新颖的量子模式识别（QPR）技术来评估信号质量。该算法在昆士兰大学数据库中获得的手动标注数据上进行了验证。共预处理了 28366 个 5s 的信号段，并将其转换为 20x500 像素的图像文件，输入到 2D CNN 架构中。开发的模型将 PPG 信号分类为“良好”和“不良”，准确率为 98.3%，灵敏度为 99.3%，特异性为 94.5%，F1 得分为 98.9%。实验分析得出结论，基于 slim 模块的架构和新颖的时空模式识别技术提高了系统的性能。该方法适用于资源受限的可穿戴设备实现。
