Interdisciplinary Program in Biohealth-Machinery Convergence Engineering, Kangwon National University, Chuncheon-si, 24341, Korea.
Program of Mechanical and Biomedical Engineering, College of Engineering, Chuncheon-si, 24341, Korea.
Sci Rep. 2022 Feb 24;12(1):3133. doi: 10.1038/s41598-022-07107-5.
Photoplethysmography (PPG) is easy to measure and provides important parameters related to heart rate and arrhythmia. However, automated PPG methods have not been developed because of their susceptibility to motion artifacts and differences in waveform characteristics among individuals. With increasing use of telemedicine, there is growing interest in application of deep neural network (DNN) technology for efficient analysis of vast amounts of PPG data. This study is about an algorithm for measuring a patient's PPG and comparing it with their own data stored previously and with the average data of several groups. Six deep neural networks were used to normalize the PPG waveform according to the heart rate by removing uninformative regions from the PPG, distinguishing between heartbeat and reflection pulses, dividing the heartbeat waveform into 10 segments and averaging the values according to each segments. PPG data were measured using telemedicine in both groups. Group 1 consisted of healthy people aged 25 to 35 years, and Group 2 consisted of patients between 60 and 75 years of age taking antihypertensive medications. The proposed algorithm could accurately determine which group the subject belonged with the newly measured PPG data (AUC = 0.998). On the other hand, errors were frequently observed in identification of individuals (AUC = 0.819).
光电容积脉搏波描记法（PPG）易于测量，并提供与心率和心律失常相关的重要参数。然而，由于其容易受到运动伪影的影响以及个体之间的波形特征差异，因此尚未开发出自动 PPG 方法。随着远程医疗的应用日益增多，人们越来越感兴趣的是应用深度神经网络（DNN）技术对大量 PPG 数据进行高效分析。本研究介绍了一种测量患者 PPG 的算法，并将其与之前存储的自身数据以及几组平均数据进行比较。使用六个深度神经网络根据心率去除 PPG 中的无信息区域，区分心跳和反射脉冲，将心跳波形分成 10 个部分，并根据每个部分的平均值来归一化 PPG 波形。两组均使用远程医疗测量 PPG 数据。第 1 组由年龄在 25 至 35 岁的健康人组成，第 2 组由年龄在 60 至 75 岁之间服用抗高血压药物的患者组成。该算法可以使用新测量的 PPG 数据准确地确定受试者所属的组别（AUC=0.998）。另一方面，在识别个体时经常会出现错误（AUC=0.819）。
