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Deep learning framework for automatic detection and classification of sleep apnea severity from polysomnography signals

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Abstract

Sleep apnea (SA) is a sleep-related breathing disorder characterized by breathing pauses during sleep. A person’s sleep schedule is significantly influenced by that person’s hectic lifestyle, which may include unhealthy eating habits and their line of work. Polysomnographic (PSG) sleep studies examine sleep-related disorders by recording various biosignals from the human body. However, SA classification methods could be more robust in terms of performance because they rely on feature-engineering strategies or employ a particular signal from PSG recording for diagnosis. This study aims to classify the severity of SA according to the apnea–hypopnea index (AHI) into normal, mild, and moderate-to-severe groups using oxygen saturation (SpO2), electroencephalogram (EEG), and electrocardiogram (ECG) signals. The proposed deep neural network (DNN)-bidirectional long short-term memory (DNN-BiLSTM) framework addresses the issue of low detection accuracy in analysis. The DNN-BiLSTM approach employs features extracted from a multiscale dilation attention 1D convolutional neural network (MSDA-1DCNN) as input for detection and classification purposes. The MSDA-1DCNN network extracts deep features from processed SpO2, EEG, and ECG signals. The developed firefly combined electric fish optimization (FCEFO) algorithm improves performance by optimizing the hidden neuron count of the DNN and the learning rate of the BiLSTM framework. The performance measures proved the effectiveness of the model over conventional machine and deep learning approaches. With the integration of deep features, the proposed DNN-BiLSTM model provides enhanced performance in terms of accuracy and precision. Thus, the proposed approach is progressive and can be used for medical diagnosis.

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Data availability

Data supporting the findings of this study are available in [Physionet] at https://doi.org/https://doi.org/10.13026/C23K5S.

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Acknowledgements

The Department of Science and Technology supported this work under Grant [No. SEED/TIDE/2018/20/G]. We thank Dr. Somusivabalan, a pulmonologist and senior consultant at the Rela Institute in Chennai, for his consistent guidance and assistance in completing this work.

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Authors and Affiliations

  1. Department of Artificial Intelligence and Data Science, Sri Sai Ram Engineering College, Chennai, India

    A. Raja Brundha

  2. Department of Computer Science Engineering, Velammal Engineering College, Chennai, India

    A. Lakshmi Sangeetha & A. Balajiganesh

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  1. A. Raja Brundha
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  2. A. Lakshmi Sangeetha
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  3. A. Balajiganesh
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Correspondence to A. Raja Brundha.

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Raja Brundha, A., Lakshmi Sangeetha, A. & Balajiganesh, A. Deep learning framework for automatic detection and classification of sleep apnea severity from polysomnography signals. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-09889-3

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