标题：Dynamic time warping and machine learning for signal quality assessment of pulsatile signals
作者：Li, Q.;Clifford, G.D.
作者机构：[Li, Q] Institute of Biomedical Engineering, School of Medicine, Shandong University, Jinan, Shandong 250012, China, Department of Engineering Science 更多
通讯作者地址：[Li, Q]Shandong Univ, Sch Med, Inst Biomed Engn, Jinan 250012, Shandong, Peoples R China.
关键词：artificial neural network;dynamic time warping;machine learning;multi-layer perceptron;photoplethysmograph;pulsatile signal;signal quality assessment
摘要：In this work, we describe a beat-by-beat method for assessing the clinical utility of pulsatile waveforms, primarily recorded from cardiovascular blood volume or pressure changes, concentrating on the photoplethysmogram (PPG). Physiological blood flow is nonstationary, with pulses changing in height, width and morphology due to changes in heart rate, cardiac output, sensor type and hardware or software pre-processing requirements. Moreover, considerable inter-individual and sensor-location variability exists. Simple template matching methods are therefore inappropriate, and a patient-specific adaptive initialization is therefore required. We introduce dynamic time warping to stretch each beat to match a running template and combine it with several other features related to signal quality, including correlation and the percentage of the beat that appeared to be clipped. The features were then presented to a multi-layer perceptron neural network to learn the relationships between the parameters in the presence of good- and bad-quality pulses. An expert-labeled database of 1055 segments of PPG, each 6s long, recorded from 104 separate critical care admissions during both normal and verified arrhythmic events, was used to train and test our algorithms. An accuracy of 97.5% on the training set and 95.2% on test set was found. The algorithm could be deployed as a stand-alone signal quality assessment algorithm for vetting the clinical utility of PPG traces or any similar quasi-periodic signal.