Motin Mohammod Abdul, Karmakar Chandan, Palaniswami Marimuthu, Penzel Thomas, Kumar Dinesh
Department of Electrical and Electronic Engineering, Rajshahi University of Engineering and Technology, Kazla, Rajshahi 6204, Bangladesh.
School of IT, Deakin University, Burwood, Melbourne, VIC 3125, Australia.
R Soc Open Sci. 2023 Apr 12;10(4):221517. doi: 10.1098/rsos.221517. eCollection 2023 Apr.
The conventional approach to monitoring sleep stages requires placing multiple sensors on patients, which is inconvenient for long-term monitoring and requires expert support. We propose a single-sensor photoplethysmographic (PPG)-based automated multi-stage sleep classification. This experimental study recorded the PPG during the entire night's sleep of 10 patients. Data analysis was performed to obtain 79 features from the recordings, which were then classified according to sleep stages. The classification results using support vector machine (SVM) with the polynomial kernel yielded an overall accuracy of 84.66%, 79.62% and 72.23% for two-, three- and four-stage sleep classification. These results show that it is possible to conduct sleep stage monitoring using only PPG. These findings open the opportunities for PPG-based wearable solutions for home-based automated sleep monitoring.
传统的睡眠阶段监测方法需要在患者身上放置多个传感器，这对于长期监测来说很不方便，并且需要专家支持。我们提出了一种基于单传感器光电容积脉搏波描记法（PPG）的自动多阶段睡眠分类方法。这项实验研究记录了10名患者整夜睡眠期间的PPG。进行数据分析以从记录中获得79个特征，然后根据睡眠阶段进行分类。使用具有多项式核的支持向量机（SVM）进行分类的结果显示，对于两阶段、三阶段和四阶段睡眠分类，总体准确率分别为84.66%、79.62%和72.23%。这些结果表明仅使用PPG进行睡眠阶段监测是可行的。这些发现为基于PPG的可穿戴解决方案用于家庭自动睡眠监测开辟了机会。
