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논문 기본 정보

자료유형
학술저널
저자정보
Yoo Rha Hong (Kosin University College of Medicine) Kyungwon Kim (Pusan National University) Eunsoo Moon (Pusan National University Hospital) Jeonghyun Park (Pusan National University Hospital) Chi Eun Oh (Kosin University College of Medicine) Jung Hyun Lee (Kosin University College of Medicine) Min Yoon (Pukyung National University)
저널정보
대한정신약물학회 Clinical Psychopharmacology and Neuroscience Clinical Psychopharmacology and Neuroscience 제21권 제2호
발행연도
2023.5
수록면
279 - 287 (9page)
DOI
10.9758/cpn.2023.21.2.279

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초록· 키워드

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Objective: Even though studies using machine learning on sleep-wake states have been performed, studies in various conditions are still necessary. This study aimed to examine the performance of the prediction model of locomotor activ ities on sleep-wake states using machine learning algorithms. Methods: The processed data using moving average of locomotor activities were used as predicting features. The sleep-wake states were used as true labels. The prediction models were established by machine learning classifiers such as support vector machine with radial basis function (SVM-RBF), linear discriminant analysis (LDA), naïve Bayes, and random forest (RF). The prediction model was evaluated by a six-fold cross validation. Results: The SVM-RBF and RF showed acceptable performance within a window of moving average from 480 to 1,200 seconds. The highest accuracy (0.869) was shown by the RF at the interval of 480 seconds. Meanwhile, the highest area under the curve (0.939) was shown by LDA at the interval of 870 seconds. Conclusion: This study suggested that the prediction model on sleep-wake state using machine learning could show an improvement of the model performance when using moving average with raw data. The prediction model using locomotor activity can be useful in research on sleep-wake state.

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