Modeling Sleep Quality Depending on Objective Actigraphic Indicators Based on Machine Learning Methods

Bitkina, Olga Vl.; Park, Jaehyun; Kim, Jungyoon

doi:10.3390/ijerph19169890

Cited by 10 publications

(4 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, machine learning and deep learning techniques have been applied to actigraphy data. The most comparable attempts have used a single data modality or single channel data [26, 43, 44]. In summary, these papers use different sized training populations, different data sources, and applied numerous different approaches to analyze the data.…”

Section: Discussionmentioning

confidence: 99%

A Personalized and Adaptive Distribution Classification of Actigraphy Segments into Sleep-Wake States

Vandegriffe,

Samaranayake,

Thimgan

2023

Preprint

View full text Add to dashboard Cite

Wearable actimeters have the potential to greatly improve our understanding sleep in natural environments and in long-term experiments. Current technologies have served the sleep community well, but they have known weaknesses that introduce errors that can compromise reliable and relevant clinical and research sleep and wakefulness profiles from these data. Newer data collection technologies, such as microelectromechanical systems (MEMS), offer opportunities to gather movement data in different forms and at higher frequencies, making new analytical methods possible and potentially advantageous. We have developed a novel statistical algorithm, called the Wasserstein Algorithm for Classifying Sleep and Wakefulness (WACSAW), that is based on optimal transport statistics and uses MEMS data as its input. WACSAW segments group data into periods with similar movement patterns and uses optimal transport methods to generate a Wasserstein profile for each segment. The second utilization of optimal transport methodology measures the difference between each segment profile and a hypothetical segment of idealized sleep. Characteristic functions, derived from individual activity segments, were clustered and classified the segment as sleep or wakefulness. WACSAW was initially developed on a 6-person cohort and applied to an additional 16 independent participants. WACSAW returned >$95% overall accuracy in sleep and wakefulness assignments validated against participant logs. Compared to the Actiwatch Spectrum Plus, WACSAW delivered a ~10% improvement in accuracy, sensitivity, and selectivity and showed a reduced standard error between participants, indicating WACSAW conformed to individualized data. In addition, we directly compared WACSAW to GGIR, a current used algorithm designed to accept MEMS data. WACSAW showed an improvement in overall statistics and handled the time series and segmentation differently, which may contribute unique information to activity recordings. Here, we provide novel statistical approach to actimetry that improves sleep/wakefulness designations, adapts to individuals, provides interim metrics that further interpretations and is open source for modification.

show abstract

Section: Discussionmentioning

confidence: 99%

A Personalized and Adaptive Distribution Classification of Actigraphy Segments into Sleep-Wake States

Vandegriffe,

Samaranayake,

Thimgan

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In [ 30 ], a cross-validated model was proposed for classifying sleep quality based on the goal of the act graph data. The final classification model demonstrated acceptable performance metrics and accuracy when it was assessed using two machine learning techniques: support vector machines (SVM) and K-nearest neighbors (KNN).…”

Section: Related Workmentioning

confidence: 99%

Potential of Machine Learning for Predicting Sleep Disorders: A Comprehensive Analysis of Regression and Classification Models

Alazaidah,

Samara,

Aljaidi

et al. 2023

Diagnostics

View full text Add to dashboard Cite

Sleep disorder is a disease that can be categorized as both an emotional and physical problem. It imposes several difficulties and problems, such as distress during the day, sleep-wake disorders, anxiety, and several other problems. Hence, the main objective of this research was to utilize the strong capabilities of machine learning in the prediction of sleep disorders. In specific, this research aimed to meet three main objectives. These objectives were to identify the best regression model, the best classification model, and the best learning strategy that highly suited sleep disorder datasets. Considering two related datasets and several evaluation metrics that were related to the tasks of regression and classification, the results revealed the superiority of the MultilayerPerceptron, SMOreg, and KStar regression models compared with the other twenty three regression models. Furthermore, IBK, RandomForest, and RandomizableFilteredClassifier showed superior performance compared with other classification models that belonged to several learning strategies. Finally, the Function learning strategy showed the best predictive performance among the six considered strategies in both datasets and with respect to the most evaluation metrics.

show abstract

“…Sleep disorders, stemming from factors such as stress, anxiety, poor sleep hygiene, and daytime overexcitation, affect individuals, particularly women in various stages of menopause. Studies reveal a prevalence of sleep disorders ranging from 16 to 42% in premenopausal women, 39–47% in perimenopausal women, and 35% to 60% in postmenopausal women 1 – 3 . The transition to menopause introduces physiological and hormonal changes that disrupt sleep patterns, contributing to a spectrum of sleep-related challenges 4 , 5 .…”

Section: Introductionmentioning

confidence: 99%

Knowledge, attitude, and practice toward sleep disorders and sleep hygiene among perimenopausal women

Shi,

Wang

et al. 2024

Sci Rep

View full text Add to dashboard Cite

This cross-sectional study aimed to explore the knowledge, attitude and practice (KAP) toward sleep disorders and sleep hygiene among perimenopausal women, who were enrolled in Dezhou region of Shandong Province between July and September 2023. A total of 720 valid questionnaires were collected (mean age: 51.28 ± 4.32 years old), and 344 (47.78%) reported experiencing insomnia. The mean scores for knowledge, attitude, practice, and Dysfunctional Beliefs and Attitudes about Sleep (DBAS) were 15.73 ± 7.60 (possible range: 0–36), 29.35 ± 3.15 (possible range: 10–50), 28.54 ± 4.03 (possible range: 10–50), and 6.79 ± 1.90 (possible range: 0–10), respectively. Path analysis showed that knowledge had direct effects on attitude (β = 0.04, 95% CI 0.01–0.07, P = 0.001), and DBAS (β = 0.04, 95% CI 0.02–0.05, P < 0.001). Knowledge had direct effects (β = 0.11, 95% CI 0.08–0.15, P < 0.001) and indirect (β = 0.02, 95% CI 0.00–0.03, P = 0.002) effect on practice. Moreover, attitude also had a direct impact on practice (β = 0.34, 95% CI 0.25–0.43, P < 0.001). In conclusion, perimenopausal women exhibited insufficient knowledge, negative attitude, inactive practice toward sleep disorders and sleep hygiene, and unfavorable DBAS, emphasizing the need for targeted healthcare interventions.

show abstract

Modeling Sleep Quality Depending on Objective Actigraphic Indicators Based on Machine Learning Methods

Cited by 10 publications

References 51 publications

A Personalized and Adaptive Distribution Classification of Actigraphy Segments into Sleep-Wake States

A Personalized and Adaptive Distribution Classification of Actigraphy Segments into Sleep-Wake States

Potential of Machine Learning for Predicting Sleep Disorders: A Comprehensive Analysis of Regression and Classification Models

Knowledge, attitude, and practice toward sleep disorders and sleep hygiene among perimenopausal women

Contact Info

Product

Resources

About