Constant Q-transform-based deep learning architecture for detection of obstructive sleep apnea

• Autorzy: Kandukuri Usha Rani , Prakash Allam Jaya , Patro Kiran Kumar , Neelapu Bala Chakravarthy , Tadeusiewicz Ryszard , Pławiak Paweł

Identyfikatory

DOI

10.34768/amcs-2023-0036

• Języki publikacji: EN

Abstrakty

EN

Obstructive sleep apnea (OSA) is a long-term sleep disorder that causes temporary disruption in breathing while sleeping. Polysomnography (PSG) is the technique for monitoring different signals during the patient’s sleep cycle, including electroencephalogram (EEG), electromyography (EMG), electrocardiogram (ECG), and oxygen saturation (SpO2). Due to the high cost and inconvenience of polysomnography, the usefulness of ECG signals in detecting OSA is explored in this work, which proposes a two-dimensional convolutional neural network (2D-CNN) model for detecting OSA using ECG signals. A publicly available apnea ECG database from PhysioNet is used for experimentation. Further, a constant Q-transform (CQT) is applied for segmentation, filtering, and conversion of ECG beats into images. The proposed CNN model demonstrates an average accuracy, sensitivity and specificity of 91.34%, 90.68% and 90.70%, respectively. The findings obtained using the proposed approach are comparable to those of many other existing methods for automatic detection of OSA.

Słowa kluczowe

EN

sleep apnea; convolutional neural network; constant Q-transform; deep learning; single lead ECG signal; non apnea; obstructive sleep apnea

PL

bezdech senny; sieć neuronowa konwolucyjna; uczenie głębokie; sygnał EKG; obturacyjny bezdech senny

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2023
• Tom: Vol. 33, no. 3
• Strony: 493--506
• Opis fizyczny: Bibliogr. 47 poz., rys., tab., wykr.

Twórcy

autor

Kandukuri Usha Rani

• Department of Biotechnology and Medical Engineering, National Institute of Technology, Sector-1, Rourkela, Odisha, 769008, India

autor

Prakash Allam Jaya

• School of Computing Science and Engineering (SCOPE), VIT Vellore, Tiruvalam Road, Katpadi, Vellore, Tamil Nadu, 632014, India

autor

Patro Kiran Kumar

• Department of Electronics and Communication Engineering, Aditya Institute of Technology and Management, Main Street, Tekkali, Andhra Pradesh, 532201, India

autor

Neelapu Bala Chakravarthy

• Department of Biotechnology and Medical Engineering, National Institute of Technology, Sector-1, Rourkela, Odisha, 769008, India

autor

Tadeusiewicz Ryszard

• Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Cracow, Poland

autor

Pławiak Paweł

• Department of Computer Science, Cracow University of Technology, ul. Warszawska 24, 31-155 Cracow, Poland
• Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, ul. Bałtycka 5, 44-100 Gliwice, Poland

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