AI and IoT Enabled Sleep Stage Classification

Dimitrios Zografakis, Dimitrios Zografakis, Panagiotis Tsakanikas, Panagiotis Tsakanikas, Ioanna Roussaki, Ioanna Roussaki, Konstantina-Maria Giannakopoulou, Konstantina-Maria Giannakopoulou

2023

Abstract

Sleep is a key aspect affecting health, cognitive functionality, and human psychology on all occasions. Therefore, on the one hand, sleep greatly impacts the quality of life, while on the other hand poor health and/or psychology often deteriorate the quality of sleep. Moving beyond the golden standard for sleep studies, i.e. polysomnography, and building on the current state of the art in wearables, this paper aims to propose a deep learning approach that focuses on sleep stage classification, introducing the timeseries related information input to the classification. In this respect, smartwatch sensor measurements are used and a series of methods have been tested. The proposed approach constitutes a preliminary work on sleep stage classification introducing a novel approach of feature engineering incorporating the time-related information concerning the transition of the sleep stages via a Long Short-Term Memory (LSTM) encoding of the accelerometer data from smartwaches. The obtained results are compared with the outcomes of existing related approaches on the same open dataset as previously published. The respective evaluation exhibits promising findings and shortcomings compared to previous approaches and polysomnography analysis correspondingly. In addition, the choice of appropriate evaluation metrics has emerged, since traditional classification metrics such as accuracy, are not appropriate to capture the real performance in terms of the transition of the stages sequence in the resulted hypnograms.

Paper Citation

in Harvard Style

Zografakis D., Tsakanikas P., Roussaki I. and Giannakopoulou K. (2023). AI and IoT Enabled Sleep Stage Classification. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS; ISBN 978-989-758-631-6, SciTePress, pages 155-161. DOI: 10.5220/0011631300003414

in Bibtex Style

@conference{biosignals23,
author={Dimitrios Zografakis and Panagiotis Tsakanikas and Ioanna Roussaki and Konstantina-Maria Giannakopoulou},
title={AI and IoT Enabled Sleep Stage Classification},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS},
year={2023},
pages={155-161},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011631300003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS
TI - AI and IoT Enabled Sleep Stage Classification
SN - 978-989-758-631-6
AU - Zografakis D.
AU - Tsakanikas P.
AU - Roussaki I.
AU - Giannakopoulou K.
PY - 2023
SP - 155
EP - 161
DO - 10.5220/0011631300003414
PB - SciTePress