论文标题
使用量子模式识别和轻巧的CNN体系结构对光杀解物图信号的信号质量评估
Signal Quality Assessment of Photoplethysmogram Signals using Quantum Pattern Recognition and lightweight CNN Architecture
论文作者
论文摘要
光摄影学（PPG）信号包括与心肺健康有关的生理信息。但是，在记录时，这些PPG信号很容易被运动伪影和身体运动损坏，从而导致噪音丰富，质量差。因此，必须确保准确提取心肺度信息的高质量信号。尽管存在一些基于规则的和机器学习（ML）的基于PPG信号质量估计的方法，但这些算法的功效值得怀疑。因此，这项工作提出了使用新型量子模式识别（QPR）技术的轻巧CNN体系结构，用于信号质量评估。拟议的算法对从昆士兰大学数据库获得的手动注释数据进行了验证。预处理总共28366，5S信号段被预处理并转换为20 x 500像素的图像文件。图像文件被视为2D CNN体系结构的输入。开发的模型将PPG信号分类为“好”或“坏”，精度为98.3％，灵敏度为99.3％，特异性为94.5％和98.9％的F1-SCORE。最后，针对所收集的PPG数据库的嘈杂的“ Welltory App”验证了所提出的框架的性能。即使在嘈杂的环境中，拟议的建筑也证明了其能力。实验分析得出的结论是，纤细的体系结构以及一种新颖的时空模式识别技术可改善系统的性能。因此，提出的方法可用于对资源受限的可穿戴实现进行分类良好和坏的PPG信号。
Photoplethysmography (PPG) signal comprises physiological information related to cardiorespiratory health. However, while recording, these PPG signals are easily corrupted by motion artifacts and body movements, leading to noise enriched, poor quality signals. Therefore ensuring high-quality signals is necessary to extract cardiorespiratory information accurately. Although there exists several rule-based and Machine-Learning (ML) - based approaches for PPG signal quality estimation, those algorithms' efficacy is questionable. Thus, this work proposes a lightweight CNN architecture for signal quality assessment 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. The image files are treated as an input to the 2D CNN architecture. The developed model classifies the PPG signal as `good' or `bad' with an accuracy of 98.3% with 99.3% sensitivity, 94.5% specificity and 98.9% F1-score. Finally, the performance of the proposed framework is validated against the noisy `Welltory app' collected PPG database. Even in a noisy environment, the proposed architecture proved its competence. Experimental analysis concludes that a slim architecture along with a novel Spatio-temporal pattern recognition technique improve the system's performance. Hence, the proposed approach can be useful to classify good and bad PPG signals for a resource-constrained wearable implementation.
