Depression in Obstructive Sleep Apnea Patients: Is Using Complex Deep Learning Structures Worth It?

Mostafa Moussa, Yahya Alzaabi, Ahsan Khandoker

2023

Abstract

The prevalence and severity of depression make it imperative to develop a means to automatically detect it, so as to alleviate the associated mental effort and cost of seeing a dedicated professional. Depression can also co-exist with other conditions, such as Obstructive Sleep Apnea Syndrome (OSAS). In this paper, we build upon our previous work involving sleep staging, detection of OSAS, and detection of depression in OSAS patients, but focus solely on the latter of the three. We use features extracted from EEG, ECG, and breathing signals of 80 subjects suffering from OSAS and half of which also with depression, using 75 % of this 80subject dataset for training and 10-fold cross-validation and the remainder for testing. We train three models to classify depression: a random forest (RF), a three-layer artificial neural network (3-ANN), and a gated-recurrent unit long short-term memory (GRU-LSTM) recurrent neural network. Our analysis shows that, like our previous work, the 3-ANN is still the best performing model, with the GRU-LSTM following closely behind at an accuracy of 79.0 % and 78.6 %, respectively, but with a smaller F1-score at 80.0 % and 81.6 %. However, we believe that the large increase in computation time and number of learnable parameters does not justify the use of GRU-LSTM over a simple ANN.

Paper Citation

in Harvard Style

Moussa M., Alzaabi Y. and Khandoker A. (2023). Depression in Obstructive Sleep Apnea Patients: Is Using Complex Deep Learning Structures Worth It?. 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 203-211. DOI: 10.5220/0011655600003414

in Bibtex Style

@conference{biosignals23,
author={Mostafa Moussa and Yahya Alzaabi and Ahsan Khandoker},
title={Depression in Obstructive Sleep Apnea Patients: Is Using Complex Deep Learning Structures Worth It?},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS},
year={2023},
pages={203-211},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011655600003414},
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 - Depression in Obstructive Sleep Apnea Patients: Is Using Complex Deep Learning Structures Worth It?
SN - 978-989-758-631-6
AU - Moussa M.
AU - Alzaabi Y.
AU - Khandoker A.
PY - 2023
SP - 203
EP - 211
DO - 10.5220/0011655600003414
PB - SciTePress