@inproceedings{CI-Seraphim23,


author = {Mathieu Seraphim and
Paul Dequidt and
Alexis Lechervy and
Florian Yger and
Luc Brun and
Olivier Etard},
editor = {Nicolas Tsapatsoulis and
Andreas Lanitis and
Marios Pattichis and
Constantinos S. Pattichis and
Christos Kyrkou and
Efthyvoulos Kyriacou and
Zenonas Theodosiou and
Andreas Panayides},
title = {Temporal Sequences of {EEG} Covariance Matrices for Automated Sleep
Stage Scoring with Attention Mechanisms},
booktitle = {Computer Analysis of Images and Patterns - 20th International Conference,
{CAIP} 2023, Limassol, Cyprus, September 25-28, 2023, Proceedings,
Part {II}},
series = {Lecture Notes in Computer Science},
volume = {14185},
pages = {67--76},
publisher = {Springer},
year = {2023},
doi = {10.1007/978-3-031-44240-7_7},
url = {Springer:=https://link.springer.com/chapter/10.1007/978-3-031-44240-7_5, HAL:=https://hal.science/hal-04216925v1},
theme = "pattern",
abstract ="Sleep monitoring has traditionally required expensive equipment and expert assessment. Wearable devices are however becoming a viable option for monitoring sleep. This study investigates methods for autonomously identifying sleep segments base on wearable device data. We employ and evaluate machine and deep learning models on the benchmark MESA dataset, with results showing that they outperform traditional methods in terms of accuracy, F1 score, and Matthews Correlation Coefficient (MCC). The most accurate model, namely Light Gradient Boosting Machine, obtained an F1 score of 0.93 and an MCC of 0.73. Additionally, sleep quality metrics were used to assess the models. Furthermore, it should be noted that the proposed approach is device-agnostic, and more accessible and cost-effective than the traditional polysomnography (PSG) methods."

 }