Photoplethysmography (PPG) is often affected by interference, which could lead to incorrect judgment of physiological information. Therefore, performing a quality assessment before extracting physiological information is crucial. This paper proposed a new PPG signal quality assessment by fusing multi-class features with multi-scale series information to address the problems of traditional machine learning methods with low accuracy and deep learning methods requiring a large number of samples for training. The multi-class features were extracted to reduce the dependence on the number of samples, and the multi-scale series information was extracted by a multi-scale convolutional neural network and bidirectional long short-term memory to improve the accuracy. The proposed method obtained the highest accuracy of 94.21%. It showed the best performance in all sensitivity, specificity, precision, and F1-score metrics, compared with 6 quality assessment methods on 14 700 samples from 7 experiments. This paper provides a new method for quality assessment in small samples of PPG signals and quality information mining, which is expected to be used for accurate extraction and monitoring of clinical and daily PPG physiological information.
光电容积脉搏（PPG）易受干扰的影响，导致生理信息的误判，因此在生理信息检测前对PPG信号的质量进行评估至关重要。针对传统机器学习方法准确度不高、深度学习方法需要大量样本训练的问题，本文提出了一种融合多类特征与多尺度时序信息的PPG信号质量评估的方法。提取多类特征以减少方法对样本的依赖，采用多尺度卷积和双向长短时记忆网络提取多尺度时序信息，以提高质量评估结果的准确率。所提方法在7项试验的14 700组样本上，获得94.21%的准确率，与6种质量评估方法相比，在全部的敏感性、特异性、精准率、F1分数指标上呈现出最好的性能。本文提供了一种PPG信号小样本质量评估和质量信息挖掘的新方法，有望用于临床及日常PPG生理信息的准确提取与监测。.
Keywords: Multi-class features; Multi-scale time series information; Photoplethysmography; Quality assessment.