作者=王妃。Shelyag S。,Karmakar C。,朱镕基你们Fossion R。埃利斯j·G。德拉蒙德s p。,Angelova m . TITLE =区分急性与慢性失眠与机器学习活动检测仪时间序列数据量= =网络前沿生理学杂志》2年= 2022 URL = //www.thespel.com/articles/10.3389/fnetp.2雷竞技rebat022.1036832 DOI = 10.3389 /抽象fnetp.2022.1036832 ISSN = 2674 - 0109 =急性和慢性失眠有不同的原因和可能需要不同的治疗方法。他们调查multi-night夜间活动检测仪数据从两个睡眠研究。两种不同的可戴活动检测仪设备被用来衡量体育活动。这需要数据预处理和转换平滑设备之间的区别。统计、功率谱、分形和熵分析从活动检测仪数据被用来推导特性。睡眠参数也从信号中提取。 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.