心率变异性
光容积图
心跳
计算机科学
去趋势波动分析
心率
人工智能
模式识别(心理学)
医学
心脏病学
内科学
数学
血压
无线
电信
计算机安全
缩放比例
几何学
作者
Nuno Cercas Pinheiro,Ricardo Couceiro,J. Henriques,Jens Muehlsteff,I. Quintal,Lino Gonçalves,P. Carvalho
标识
DOI:10.1109/embc.2016.7591347
摘要
Heart rate variability (HRV) represents one of the most promising markers of the autonomic nervous system (ANS) regulation. However, it requires the acquisition of the ECG signal in order to reliably detect the RR intervals, which is not always easily and comfortably available in personal health applications. Additionally, due to progress in single spot optical sensors, photoplethysmography (PPG) is an interesting alternative for heartbeat interval measurements, since it is a more convenient and a less intrusive measurement technique. Driven by the technological advances in such sensors, wrist-worn devices are becoming a commodity, and the interest in the assessment of HRV indexes from the PPG analysis (pulse rate variability - PRV) is rising. In this study, we investigate the hypothesis of using PRV features as surrogates for HRV indexes, in three different contexts: healthy subjects at rest, healthy subjects after physical exercise and subjects with cardiovascular diseases (CVD). Additionally, we also evaluate which are the characteristic points better suited for PRV analysis in these contexts, i.e. the PPG waveform characteristic points leading to the PRV features that present the best estimates of HRV (correlation and error analysis). The achieved results suggest that the PRV can be often used as an alternative for HRV analysis in healthy subjects, with significant correlations above 82%, for both time and frequency features. Contrarily, in the post-exercise and CVD subjects, time and (most importantly) frequency domain features shall be used with caution (mean correlations ranging from 68% to 88%).
科研通智能强力驱动
Strongly Powered by AbleSci AI