Machine learning-based classification of circadian rhythm characteristics for mild cognitive impairment in the elderly

Bibliographic Data

ID22089112
AuthorsZhizhen Liu (0000-0001-9645-9120, Fujian University of Traditional Chinese Medicine, corresponding author), Lin Zhang (0000-0001-5966-3827, Fujian University of Traditional Chinese Medicine), Jingsong Wu (0000-0001-6544-2553, Fujian University of Traditional Chinese Medicine), Zhicheng Zheng (0000-0002-9235-9074, Shenzhen University Health Science Center), Jiahui Gao (0009-0002-7030-6497, Fujian University of Traditional Chinese Medicine), Yongsheng Lin (0009-0007-8916-5993, Shenzhen University Health Science Center), Yinghua Liu (0000-0002-8035-0996, Shenzhen University Health Science Center), Haihua Xu (0000-0002-2220-8465, Shenzhen University Health Science Center), Yongjin Zhou (0000-0002-0067-8466, Shenzhen University, corresponding author)
Year2022
Volume10
Pages1036886-1036886
Publication date2022-10-28
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2022.1036886
PMID36388285
OpenAlexW4307704308
LanguageEN
Citations received1
References cited52

Introduction 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. Methods 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. Results 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). Conclusion 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

Audiology · Blood pressure · Cardiology · Circadian rhythm · Cognition · Heart rate · Heart rate variability · Physical medicine and rehabilitation · Physical therapy · Psychiatry · Respiratory Rate · Rhythm · Heart Rate Variability and Autonomic Control · Medicine · Physical Activity and Health · Psychology · Sleep and related disorders · Internal Medicine

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