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EASM: An efficient AttnSleep model for sleep Apnea detection from EEG signals

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Abstract

This paper addresses the crucial task of automatic sleep stage classification to assist sleep experts in diagnosing sleep disorders such as sleep apnea and insomnia. The proposed solution presents a novel attention-based deep learning model called, Efficient Attention-sleep Model (EASM), designed specifically for sleep apnea detection using EEG signals. EASM incorporates a streamlined architecture that includes a modified Muti-Resolution Convolutional Neural Network (MRCNN), Adaptive Feature Recalibration (AFR), and a simplified Temporal Context Encoder (TCE) module to reduce complexity. To mitigate overfitting, ridge regression is utilized, which incorporates a penalty term to enhance model generalization. Furthermore, the proposed EASM utilizes a class-balanced focal loss function to address data imbalance issues. The effectiveness of EASM is evaluated on two publicly available datasets, SLEEP EDF-20 and SLEEP EDF-78. Comparative analysis of EASM against state-of-the-art models demonstrates its superior performance in terms of accuracy, training time, and model complexity. Notably, the proposed model achieves a 50% reduction in training time and a 55.7% decrease in complexity compared to the Attnsleep model. The EASM achieves a classification accuracy of 85.8% with minimum loss when compared to the Attnsleep model.

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Data Availability

The Data used in this work is available in the public domain.

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Authors and Affiliations

  1. Christ (Deemed to be University), Delhi-NCR Campus, India

    Madan Singh & Indu Verma

  2. Dronacharya Govt. College, Gurugram, India

    Sujata Chauhan

  3. ABV-Indian Institute of Information Technology & Management, Gwalior, India

    Anil Kumar Rajput & Alok Kumar Tiwari

Authors
  1. Madan Singh
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  2. Sujata Chauhan
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  3. Anil Kumar Rajput
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  4. Indu Verma
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  5. Alok Kumar Tiwari
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Correspondence to Anil Kumar Rajput.

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Singh, M., Chauhan, S., Rajput, A.K. et al. EASM: An efficient AttnSleep model for sleep Apnea detection from EEG signals. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19118-7

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  • DOI: https://doi.org/10.1007/s11042-024-19118-7

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