Rani S, Shelyag S, Karmakar C, Zhu Ye, Fossion R, Ellis J G, Drummond S P A, Angelova M
School of Information Technology, Deakin University, Geelong, VIC, Australia.
Centro de Ciencias de la Complejidad (C3) and Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, CDMX, Mexico.
Front Netw Physiol. 2022 Nov 28;2:1036832. doi: 10.3389/fnetp.2022.1036832. eCollection 2022.
Acute and chronic insomnia have different causes and may require different treatments. They are investigated with multi-night nocturnal actigraphy data from two sleep studies. Two different wrist-worn actigraphy devices were used to measure physical activities. This required data pre-processing and transformations to smooth the differences between devices. Statistical, power spectrum, fractal and entropy analyses were used to derive features from the actigraphy data. Sleep parameters were also extracted from the signals. The features were then submitted to four machine learning algorithms. The best performing model was able to distinguish acute from chronic insomnia with an accuracy of 81%. The algorithms were then used to evaluate the acute and chronic groups compared to healthy sleepers. The differences between acute insomnia and healthy sleep were more prominent than between chronic insomnia and healthy sleep. This may be associated with the adaptation of the physiology to prolonged periods of disturbed sleep for individuals with chronic insomnia. The new model is a powerful addition to our suite of machine learning models aiming to pre-screen insomnia at home with wearable devices.
急性失眠和慢性失眠有不同的病因，可能需要不同的治疗方法。通过两项睡眠研究的多晚夜间活动记录仪数据对它们进行调查。使用了两种不同的腕戴式活动记录仪设备来测量身体活动。这需要进行数据预处理和转换，以消除设备之间的差异。采用统计分析、功率谱分析、分形分析和熵分析从活动记录仪数据中提取特征。还从信号中提取睡眠参数。然后将这些特征提交给四种机器学习算法。表现最佳的模型能够以81%的准确率区分急性失眠和慢性失眠。然后使用这些算法评估急性和慢性失眠组与健康睡眠者的情况。急性失眠与健康睡眠之间的差异比慢性失眠与健康睡眠之间的差异更为显著。这可能与慢性失眠患者的生理机能对长期睡眠障碍的适应有关。新模型是我们旨在通过可穿戴设备在家中对失眠进行预筛查的机器学习模型套件中的一个强大补充。
