Investigation of Elderly Patient Actimetries for Night Sleep/Wake Phases Prediction

This paper presents a Machine Learning (ML) application that involves Long Short-Term Memory (LSTM) Artificial Neural Network (ANN). The proposed LSTM-ANN. architecture addresses the prediction challenge of sleep/wake phases in order to minimize night wake stages in elderly patients. Accurate predic...

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Bibliographic Details
Published inInternational Conference on Control, Decision and Information Technologies (Online) pp. 2633 - 2638
Main Authors Zard, Radjia, Ali, Jaouher Ben, Laamiri, Nacira, Belmin, Joel, Bouchouicha, Moez, Naeck, Roomila, Ginoux, Jean-Mark
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2024
Subjects
Accuracy
Actimetry
Brain modeling
Electroencephalography
Electrooculography
Long short term memory
LSTM-ANN
Machine learning
Older adults
PHM
Prediction
Predictive models
Prognostics and health management
Sleep
Sleep/Wake phases
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Summary:This paper presents a Machine Learning (ML) application that involves Long Short-Term Memory (LSTM) Artificial Neural Network (ANN). The proposed LSTM-ANN. architecture addresses the prediction challenge of sleep/wake phases in order to minimize night wake stages in elderly patients. Accurate predictions guarantee a good sleep quality for elderly patients and support medication adherence. For this, Vivago® Care watch technology (IST Vivago® Oy) is used in this work. Each watch was advantaged by actimetric functionality. Watches were worn on the wrist of the considered elderly patients. Automatically, one recording was sent every minute to the base station located in the nursery room, where the recorded data are stored and backed up. Experimental results are encouraging and also they are a rationale and evidence for the allocation of investments for developing online monitoring systems for sleep/wake phase's prediction using only previous historical data. Our proposed LSTM-ANN proposal despite the novelty of this subject and the lack of literature, highlights prototypes results. is accurate for Personal Health Management (PHM) applications.
ISSN:2576-3555
DOI:10.1109/CoDIT62066.2024.10708582