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Analysis of ECG Signal by Using an FCN Network for Automatic Diagnosis of Obstructive Sleep Apnea

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Abstract

Obstructive sleep apnea (OSA) is a sleep disorder in which a person’s breathing is repeatedly interrupted because of airway obstruction. This study presents a fully convolutional neural network to diagnose the OSA by analyzing the single–lead ECG signal. This model does not employ any fully connected layers for classification, and the entire model only consists of 1D convolutional, activation, Pooling, and Batch Normalization layers. The main idea behind this model is to make decisions based on local segments of the signal, and the final classification is performed by aggregation of all decisions. This model implements the divide and conquer problem–solving method within a deep learning structure by the joint use of a convolutional layer and a global average pooling instead of a dense layer for classification. It was trained and evaluated by using a dataset available on the Physionet website. The accuracy of the proposed model outperformed other algorithms, which employ single lead ECG for the diagnosis of OSA.

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  1. Department of Electrical and Computer Engineering, Urmia University, Nazlo road, Urmia, Iran

    Sarah Ayashm, Mehdi Chehel Amirani & Morteza Valizadeh

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  1. Sarah Ayashm
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Ayashm, S., Chehel Amirani, M. & Valizadeh, M. Analysis of ECG Signal by Using an FCN Network for Automatic Diagnosis of Obstructive Sleep Apnea. Circuits Syst Signal Process 41, 6411–6426 (2022). https://doi.org/10.1007/s00034-022-02091-7

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