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Sleep apnea detection from ECG signal using deep CNN-based structures

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Abstract

In this paper, transfer learning is used for the adaptation of pre-trained deep convolutional neural networks (DCNNs) to find the best appropriate method for the classification of obstructive sleep apnea (OSA) using electrocardiogram (ECG) signals. The Physio Apnea-ECG data set has been used for the evaluation of the proposed method. In deep learning algorithms, especially in image data classification, more data leads to better performance. For this reason, in this paper, we propose a novel technique as follows. First, the ECG signal is divided into 2-s segments and filtered, then the recurrence plots (RP) algorithm is used to convert these segments into two-dimensional images. The RP is an advanced tool that can indicate how resemblances between particular orders vary over time. These plots are generally used for the qualitative evaluation of the time series in dynamic systems. Finally, in the classification stage, 5 pre-trained DCNN models on ImageNet datasets including EfficienNet-B0, EfficienNet-B1, EfficienNet-B2, Inception-v3, and Xception are considered for final decision. By using these methods, the classification accuracies of 88.67%, 90.59%, 90.52%, 93.33%, and 93.19%, were obtained respectively. In this research, by analyzing the performance of the models used, we can see that by increasing the input image size, the number of network parameters, its depth, and the classification performance is improved. Also, the performance of the Inception-v3 model which has the largest input image size and number of parameters with 93.33% accuracy, is better than other models for OSA detection.

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Authors and Affiliations

  1. School of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran

    Ahmad Ayatollahi, Sajjad Afrakhteh, Fatemeh Soltani & Ehsan Saleh

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  1. Ahmad Ayatollahi
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  2. Sajjad Afrakhteh
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  3. Fatemeh Soltani
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  4. Ehsan Saleh
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Correspondence to Ahmad Ayatollahi.

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Appendix

Appendix

figure a

Train-Validation accuracy Xception model for fold (1–5).

figure b

Train-Validation diagram of InceptionV3 model for fold (1–5).

figure c

Train-Validation accuracy EfficientNetB2 model for fold (1–5).

figure d

Train-Validation accuracy EfficientNetB1 model for fold (1–5).

figure e

Train-Validation accuracy diagram of EfficientNetB0 model for fold (1–5).

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Ayatollahi, A., Afrakhteh, S., Soltani, F. et al. Sleep apnea detection from ECG signal using deep CNN-based structures. Evolving Systems 14, 191–206 (2023). https://doi.org/10.1007/s12530-022-09445-1

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