Authors:
Dimitrios Zografakis
1
;
2
;
Panagiotis Tsakanikas
1
;
2
;
Ioanna Roussaki
1
;
2
and
Konstantina-Maria Giannakopoulou
1
;
2
Affiliations:
1
Institute of Communication and Computer Systems, 10682 Athens, Greece
;
2
School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece
Keyword(s):
Sleep Stages, IoT, Feature Engineering, Artificial Intelligence, Polysomnography, Machine Learning.
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 obtaine
d 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.
(More)