光容积图
手腕
可穿戴计算机
金标准(测试)
信号(编程语言)
回廊的
医学
持续监测
计算机科学
人工智能
模式识别(心理学)
计算机视觉
外科
放射科
工程类
嵌入式系统
程序设计语言
滤波器(信号处理)
运营管理
作者
Nikhilesh Pradhan,Sreeraman Rajan,Andy Adler
标识
DOI:10.1088/1361-6579/ab225a
摘要
Wearable devices with embedded photoplethysmography (PPG) sensors enable continuous monitoring of cardiovascular activity, allowing for the detection cardiovascular problems, such as arrhythmias. However, the quality of wrist-based PPG is highly variable, and is subject to artifacts from motion and other interferences. The goal of this paper is to evaluate the signal quality obtained from wrist-based PPG when used in an ambulatory setting.Ambulatory data were collected over a 24 h period for 10 elderly, and 16 non-elderly participants. Visual assessment is used as the gold standard for PPG signal quality, with inter-rater agreement evaluated using Fleiss' Kappa. With this gold standard, 5 classifiers were evaluated using a modified 13-fold cross-validation approach.A Random Forest quality classification algorithm showed the best performance, with an accuracy of 74.5%, and was then used to evaluate 24 h long ambulatory wrist-based PPG measurements.In general, data quality was high at night, and low during the day. Our results suggest wrist-based PPG may be best for continuous cardiovascular monitoring applications during the night, but less useful during the day unless methods can be identified to improve low quality signal segments.
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