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

• Published in: International 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
• Language: English
• 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

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...