Title: LSTM-based electroencephalography analysis for sleep disorder subjects

Authors: Sumedha S. Borde; Varsha R. Ratnaparkhe

Addresses: Department of Electronics and Computer Engineering, Maharashtra Institute of Technology, Aurangabad, Maharashtra, India ' Department of Electronics and Telecommunication Engineering, Government Engineering College, Aurangabad, Maharashtra, India

Abstract: Electroencephalogram (EEG) is a complex, nonlinear signal which requires extensive training for detection of changes due to sleep disorder. Most of the traditional machine learning algorithms has been used in the past for detection of sleep disorder subjects. Recently deep learning has demonstrated a very promising approach for sensing EEG signals as it has excellent capacity of extracting features from raw signals. The proposed work aims to differentiate sleep disorder subjects from normal subjects using a deep learning-based model. To examine this, open-source EEG dataset from ten different electrodes of six sleep disorder subjects and six normal subjects is used here. Long short-term memory (LSTM) model, a class of recurrent neural network (RNN) is proposed for detection of sleep disorder subjects. Finally, in Table 3, accuracies are compared which are obtained in various models applied on same dataset. It is clearly predicted that the offered LSTM based technique gives classification performance of 70.75% accuracy as compared to other techniques in literature survey. Along with accuracy, recall of 88.34%, precision of 65.35% and specificity of 53.17% is evaluated for proposed LSTM model.

Keywords: electroencephalogram; EEG; deep learning; long short term memory; LSTM; recurrent neural network; RNN; recall.

DOI: 10.1504/IJAPR.2024.146814

International Journal of Applied Pattern Recognition, 2024 Vol.7 No.3/4, pp.193 - 204

Accepted: 07 Feb 2023
Published online: 19 Jun 2025 *