Machine learning-based classification of circadian rhythm characteristics for mild cognitive impairment in the elderly.
Machine learning-based classification of circadian rhythm characteristics for mild cognitive impairment in the elderly.
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DOI：
10.3389/fpubh.2022.1036886
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发表时间：
2022
影响因子：
5.2
通讯作者：
Zhou, Yongjin
中科院分区：
文献类型：
--
作者：
Liu, Zhizhen;Zhang, Lin;Wu, Jingsong;Zheng, Zhicheng;Gao, Jiahui;Lin, Yongsheng;Liu, Yinghua;Xu, Haihua;Zhou, Yongjin
关键词：
使用腕戴式传感器进行生态瞬态评估可能比自我报告的问卷调查提供更有效的身体活动，久坐时间，睡眠和昼夜节律的评估，但尚未广泛用于研究轻度认知障碍及其特征的相关性。31名认知能力正常者和68名MCI参与者使用三轴加速度计和夜间照片容积脉搏波信号进行了14天的监测。两种机器学习算法：利用白天体力活动、久坐时间和夜间生理功能（包括心率、心率变异性、呼吸频率和血氧饱和度）的数据，结合主观尺度特征，构建梯度提升决策树和极限梯度提升。比较了不同模型的准确率、精确率、召回率、F1值和AUC，并通过基于主题的留一法验证了训练和模型的有效性。MCI组在8：00 ~ 11：00的低体力活动状态明显高于认知正常组（P < 0.05），高体力活动状态的日节律趋势MCI组普遍低于认知正常组（P < 0.05）。久坐状态下的峰值节律出现在12：00-15：00和20：00。MCI组夜间6 h HRV参数中rMSSD、HRV高频输出功率和HRV低频输出功率的峰值节律在凌晨3：00消失，LHratio夜间节律波动幅度增大，相位紊乱，血氧饱和度在90 ~ 95%之间及90%以下者，各时段均有显著性差异（P < 0.05）。结合主观尺度的多特征数据MCI分类的两种机器学习算法的F1值分别为XGBoost（78.02）和GBDT（84.04）。通过收集PSQI Scale数据，结合腕戴式传感器监测的昼夜节律特征，我们能够构建具有良好区分力的XGBoost和GBDT机器学习模型，从而为识别MCI高风险家庭和社区成员提供预警解决方案。
Using wrist-wearable sensors to ecological transient assessment may provide a more valid assessment of physical activity, sedentary time, sleep and circadian rhythm than self-reported questionnaires, but has not been used widely to study the association with mild cognitive impairment and their characteristics. 31 normal cognitive ability participants and 68 MCI participants were monitored with tri-axial accelerometer and nocturnal photo volumetric pulse wave signals for 14 days. Two machine learning algorithms: gradient boosting decision tree and eXtreme gradient boosting were constructed using data on daytime physical activity, sedentary time and nighttime physiological functions, including heart rate, heart rate variability, respiratory rate and oxygen saturation, combined with subjective scale features. The accuracy, precision, recall, F1 value, and AUC of the different models are compared, and the training and model effectiveness are validated by the subject-based leave-one-out method. The low physical activity state was higher in the MCI group than in the cognitively normal group between 8:00 and 11:00 (P < 0.05), the daily rhythm trend of the high physical activity state was generally lower in the MCI group than in the cognitively normal group (P < 0.05). The peak rhythms in the sedentary state appeared at 12:00–15:00 and 20:00. The peak rhythms of rMSSD, HRV high frequency output power, and HRV low frequency output power in the 6h HRV parameters at night in the MCI group disappeared at 3:00 a.m., and the amplitude of fluctuations decreased; the amplitude of fluctuations of LHratio nocturnal rhythm increased and the phase was disturbed; the oxygen saturation was between 90 and 95% and less than 90% were increased in all time periods (P < 0.05). The F1 value of the two machine learning algorithms for MCI classification of multi-feature data combined with subjective scales were XGBoost (78.02) and GBDT (84.04). By collecting PSQI Scale data combined with circadian rhythm characteristics monitored by wrist-wearable sensors, we are able to construct XGBoost and GBDT machine learning models with good discrimination, thus providing an early warning solution for identifying family and community members with high risk of MCI.
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影响因子：
4.1
作者：
Tsai LT;Boyle E;Brønd JC;Kock G;Skjødt M;Hvid LG;Caserotti P
通讯作者：
Caserotti P
影响因子：
4.8
作者：
Baillet, Marion;Cosin, Charlotte;Mayo, Willy
通讯作者：
Mayo, Willy
影响因子：
4.3
作者：
Berry, Richard B.;Budhiraja, Rohit;Tangredi, Michelle M.
通讯作者：
Tangredi, Michelle M.
影响因子：
3.9
作者：
Yu S;Hsu CY;Chuang HY;Yang CC;Lai CL;Yu HS
通讯作者：
Yu HS
DOI：
10.1249/mss.0000000000001344
发表时间：
2017-11-01
期刊：
MEDICINE AND SCIENCE IN SPORTS AND EXERCISE
影响因子：
--
作者：
Brond, Jan Christian;Andersen, Lars Bo;Arvidsson, Daniel
通讯作者：
Arvidsson, Daniel