Prediction of Cognitive Impairment Using Sleep Lifelog Data and LSTM Model

Junhee Hong, Youngjin Seol, Seunghyun Lee, Janghyeok Yoon (), Jiho Lee, Ki-Su Park and Ji-Wan Ha
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Junhee Hong: Department of Industrial Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea
Youngjin Seol: Department of Industrial Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea
Seunghyun Lee: Department of Industrial Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea
Janghyeok Yoon: Department of Industrial Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea
Jiho Lee: Neopons Inc., 465, Dongdaegu-ro, Dong-gu, Daegu 41260, Republic of Korea
Ki-Su Park: Department of Neurosurgery, Kyungpook National University School of Medicine, 130 Dongdeok-ro, Jung-gu, Daegu 41944, Republic of Korea
Ji-Wan Ha: Department of Speech Pathology, Daegu University, 201 Daegudae-ro, Jillyang-eup, Gyeongsan 38453, Republic of Korea

Mathematics, 2024, vol. 12, issue 20, 1-18

Abstract: Rapid elderly population growth has increased the number of patients with cognitive impairment (CI). Early detection and ongoing medical treatment can slow CI progression and significantly reduce the cost of managing patients. However, distinguishing CI from natural cognitive decline associated with aging is challenging. Previous studies conducted to identify patients with CI using lifelog data did not consider changes in lifelog data over time because each data point was learned individually. This study introduces a model that predicts patients with CI based on sleep lifelog data and analyzes significant sleep factors that influence cognitive decline. This study followed three steps: (1) collecting sleep lifelog data from elderly Korean people and reconstructing sleep lifelog data as time-series data; (2) building a model to classify CI using a time series of sleep lifelog data and a long short-term memory model; and (3) identifying sleep factors that influence the onset of CI using an explainable AI algorithm. The proposed CI classification model achieved a sensitivity of 0.89, a specificity of 0.80, and an area under the receiver operating characteristic curve of 0.92. This study will facilitate the noninvasive screening, diagnosis, and continuous monitoring of CI in the elderly.

Keywords: cognitive impairment; sleep lifelog data; deep learning; health care (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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