Classification of sleep apnea using EMD-based features and PSO-trained neural networks

Sajjad Afrakhteh; Ahmad Ayatollahi; Fatemeh Soltani

doi:10.1515/bmt-2021-0025

Abstract

In this study, we propose a method for detecting obstructive sleep apnea (OSA) based on the features extracted from empirical mode decomposition (EMD) and the neural networks trained by particle swarm optimization (PSO) in the classification phase. After extracting the features from the intrinsic mode functions (IMF) of each heart rate variability (HRV) signal of each segment, these features were applied to the input of popular classifiers such as multi-layer perceptron neural networks (MLPNN), Naïve Bayes, linear discriminant analysis (LDA), k-nearest neighborhood (KNN), and support vector machines (SVM) were applied. The results show that the MLPNN learned with back propagation (BP) algorithm has a diagnostic accuracy of less than 90%, and this may be due to being derivative based property of the BP algorithm, which causes trapping in the local minima. For Improving MLPNN’s performance, we used the PSO algorithm instead of the BP method in training part. Therefore, the MLPNN’s accuracy improved from 89.36 to 97.66% after the application of the PSO algorithm. The proposed method has also reached to 97.78 and 97.96% in sensitivity and specificity, respectively. So, it can be concluded that the proposed method achieves better or comparable results when compared with the previous works in this field.

Keywords: diagnosis; EMD; feature extraction; MLPNN; obstructive sleep apnea; PSO

Corresponding author: Sajjad Afrakhteh, Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, 16846-13114, Iran, E-mail: s_afrakhteh94@elec.iust.ac.ir

Acknowledgments

Not applicable.

Research funding: None declared.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: Authors state no conflict of interest.
Informed consent: Informed consent was obtained from all individuals included in this study.
Ethical approval: The local Institutional Review Board deemed the study exempt from review.

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/bmt-2021-0025).

Received: 2021-01-29

Accepted: 2021-04-12

Published Online: 2021-05-03

Published in Print: 2021-10-26

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Supplementary Material