光容积图
多导睡眠图
脉搏率
标准差
可穿戴计算机
呼吸频率
计算机科学
节拍(声学)
心跳
心率
金标准(测试)
语音识别
物理医学与康复
统计
医学
呼吸暂停
数学
血压
麻醉
计算机视觉
内科学
嵌入式系统
声学
滤波器(信号处理)
物理
作者
Philippe Renevey,Ricard Delgado-Gonzalo,Alia Lemkaddem,Christophe Verjus,Selina Ladina Combertaldi,Björn Rasch,Brigitte Leeners,Franziska Dammeier,Florian Kuubler
标识
DOI:10.1109/embc.2018.8512881
摘要
Sleep monitoring provides valuable insights into the general health of an individual and helps in the diagnostic of sleep-derived illnesses. Polysomnography, is considered the gold standard for such task. However, it is very unwieldy and therefore not suitable for long-term analysis. Here, we present a non-intrusive wearable system that, by using photoplethysmography, it can estimate beat-to-beat intervals, pulse rate, and breathing rate reliably during the night. The performance of the proposed approach was evaluated empirically in the Department of Psychology at the University of Fribourg. Each participant was wearing two smart-bracelets from Ava as well as a complete polysomnographic setup as reference. The resulting mean absolute errors are 17.4ms (MAPE 1.8%) for the beat-to-beat intervals, 0.13beats-per-minute (MAPE 0.20%) for the pulse rate, and 0.9breaths-per-minute (MAPE 6.7%) for the breath rate.
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