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Hybridization of soft-computing algorithms with neural network for prediction obstructive sleep apnea using biomedical sensor measurements

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Abstract

Sleep apnea (SA) is a common respiratory disorder, especially among obese people. It is caused by either the relaxation of the upper respiratory tract muscles or the failure of the neural signal to reach the muscles responsible for breathing, both of which interrupt the patient’s sleep–wake cycles. The traditional method for diagnosing this disorder, based on polysomnography, is complicated, vexing, expensive, time-consuming, and requires both sleep centers and specialized staff capable of connecting electrodes to the patient’s body. This paper proposes an SA prediction system based on merging five soft computing algorithms, specifically, combining the multi-verse optimizer (MVO) with an artificial neural network (ANN) to leverage measurements from heart rate, SpO2, and chest movement sensors. The most substantial novelty of this research is the hybridization of MVO and ANN (MVO-ANN), which improves the ANN performance by selecting the best learning rate and number of neurons in hidden ANN layers. This enables highly accurate prediction of sleep apnea events. This work’s experimental results reveal that the MVO-ANN performs better than other algorithms, with mean absolute errors of 0.042, 0.202, and 0.166 for training, testing, and validation of the ANN. In addition, the SA prediction system achieved an accuracy of 98.67%, a sensitivity of 96.71%, and a specificity of 99.24%. These results provide good evidence that the proposed method can reliably predict respiratory events in people suffering from SA.

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Acknowledgements

The author would like to thank the staff of the Department of Medical Instrumentation Techniques Engineering, Electrical Engineering Technical College, Middle Technical University, and Al-Kafeel Super Specialty Hospital in Karbala for their support during this study.

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Authors and Affiliations

  1. Middle Technical University, Electrical Engineering Technical College, Baghdad, Iraq

    Mustafa Habeeb Chyad & Sadik Kamel Gharghan

  2. College of Medicine, University of Al-Ameed, Karbala, Iraq

    Mustafa Habeeb Chyad

  3. College of Medicine, Al-Nahrain University, Baghdad, Iraq

    Haider Qasim Hamood

  4. Karbala Health Department, Imam Husain Medical City, Karbala, Iraq

    Ahmed Saleh Hameed Altayyar

  5. Department of Civil Engineering, Wasit University, Wasit, Iraq

    Salah L. Zubaidi

  6. Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400, Serdang, Malaysia

    Hussein Mohammed Ridha

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  1. Mustafa Habeeb Chyad
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Correspondence to Sadik Kamel Gharghan.

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Chyad, M.H., Gharghan, S.K., Hamood, H.Q. et al. Hybridization of soft-computing algorithms with neural network for prediction obstructive sleep apnea using biomedical sensor measurements. Neural Comput & Applic 34, 8933–8957 (2022). https://doi.org/10.1007/s00521-022-06919-w

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