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A Deep Learning-based Framework for Automated Obstructive Sleep Apnea Detection using ECG Signals

by Pucha Srinivasa Pavan, N. Ramakrishnaiah
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 18
Year of Publication: 2025
Authors: Pucha Srinivasa Pavan, N. Ramakrishnaiah
10.5120/ijca2025925260

Pucha Srinivasa Pavan, N. Ramakrishnaiah . A Deep Learning-based Framework for Automated Obstructive Sleep Apnea Detection using ECG Signals. International Journal of Computer Applications. 187, 18 ( Jul 2025), 1-6. DOI=10.5120/ijca2025925260

@article{ 10.5120/ijca2025925260,
author = { Pucha Srinivasa Pavan, N. Ramakrishnaiah },
title = { A Deep Learning-based Framework for Automated Obstructive Sleep Apnea Detection using ECG Signals },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2025 },
volume = { 187 },
number = { 18 },
month = { Jul },
year = { 2025 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number18/a-deep-learning-based-framework-for-automated-obstructive-sleep-apnea-detection-using-ecg-signals/ },
doi = { 10.5120/ijca2025925260 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-07-09T01:07:36.602924+05:30
%A Pucha Srinivasa Pavan
%A N. Ramakrishnaiah
%T A Deep Learning-based Framework for Automated Obstructive Sleep Apnea Detection using ECG Signals
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 18
%P 1-6
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Obstructive sleep apnea (OSA) is a prevalent sleep disorder associated with severe health complications, including cardiovascular diseases and cognitive decline. Traditional diagnostic methods, such as polysomnography (PSG), are expensive, time-consuming, and require clinical supervision. This study proposes a deep learningbased framework for automated sleep apnea detection using singlelead electrocardiogram (ECG) signals. The proposed model leverages wavelet transform for feature extraction, heart rate variability (HRV) analysis, and a deep neural network (DNN) optimized with Bayesian optimization for classification. The ECG5000 dataset is utilized to train and validate the model, achieving a classification accuracy of 93.51%, outperforming conventional methods. The results demonstrate the potential of an ECG-based deep learning approach for scalable, cost-effective, and real-time OSA detection in wearable healthcare applications.

References
  1. M. Bahrami and M. Forouzanfar. Sleep apnea detection from single-lead ecg: A comprehensive analysis of machine learning and deep learning algorithms. IEEE Transactions on Instrumentation and Measurement, 71:1–11, 2022.
  2. E. Carter and R.Wilson. Advancements in ai-driven sleep apnea detection. IEEE Journal of Biomedical Health Informatics, 25(8):2845–2857, 2021.
  3. G. Chen and J. Wu. Wavelet transform and lstm-based model for sleep apnea detection. IEEE Sensors Journal, 21:2345– 2356, 2021.
  4. T. Chen and X. Liu. Feature extraction and cnn-based classification of ecg signals for sleep apnea diagnosis. Journal of Healthcare Engineering, 2021:1–10, 2021.
  5. J. Choi, H. Lee, and S. Kim. A comparative study of deep learning models for sleep apnea detection from ecg signals. IEEE Transactions on Neural Networks and Learning Systems, 33(3):974–986, 2022.
  6. R. K. Das and M. Ghosh. A review of machine learning approaches for sleep apnea detection. Sensors, 23(4):1859, 2023.
  7. D. Dey, S. Chaudhuri, and S. Munshi. Obstructive sleep apnea detection using convolutional neural network-based deep learning framework. Biomedical Engineering Letters, 8:95– 100, 2018.
  8. Author Names from Base Paper. An adaptive sleep apnea detection model using multi cascaded atrous-based deep learning schemes with hybrid artificial humming bird pity beetle algorithm. IEEE Access, 11:113114–113133, 2023.
  9. S. Ghosh and A. Banerjee. Time-frequency feature extraction for ecg-based sleep apnea detection. IEEE Transactions on Biomedical Engineering, 69(5):2032–2042, 2022.
  10. A. Gupta and P. Sharma. Ecg-based sleep apnea detection using hybrid cnn-lstm architecture. Biomedical Signal Processing and Control, 71:103087, 2022.
  11. K. Gupta, V. Bajaj, and I. A. Ansari. Osacn-net: Automated classification of sleep apnea using deep learning model and smoothed gabor spectrograms of ecg signal. IEEE Transactions on Instrumentation and Measurement, 71:1–9, 2021.
  12. H. Kim, J. Park, and S. Lee. Sleep apnea detection using single-lead ecg and deep learning models. IEEE Sensors Journal, 21(12):14477–14485, 2021.
  13. H. Korkalainen, J. Aakko, S. Nikkonen, and T. Lepp¨anen. Accurate deep learning-based sleep staging in a clinical population with suspected obstructive sleep apnea. IEEE Journal of Biomedical and Health Informatics, 24(7):2073–2081, 2019.
  14. J. Lee and H. Park. Heart rate variability analysis for sleep apnea detection using recurrent neural networks. Frontiers in Physiology, 13:924153, 2022.
  15. X. Li and M. Chan. Feature engineering for ecg classification using deep learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31:1021–1033, 2023.
  16. G. Martin and J. Thomas. Real-time sleep apnea detection using edge computing and ai models. IEEE Internet of Things Journal, 10(7):10532–10544, 2023.
  17. C. Martinez and R. Gupta. Ecg signal processing using attention-based deep learning for sleep apnea detection. IEEE Transactions on Biomedical Engineering, 70:319–330, 2023.
  18. S. S. Mostafa, F. Mendonc¸a, A. G. Ravelo-Garc´ıa, and F. Morgado-Dias. A systematic review of detecting sleep apnea using deep learning. Sensors, 19(22):4934, 2019.
  19. D. Mukherjee, K. Dhar, F. Schwenker, and R. Sarkar. Ensemble of deep learning models for sleep apnea detection: an experimental study. Sensors, 21(16):5425, 2021.
  20. H. Nakamura and T. Kimura. Feature selection for ecg-based sleep apnea detection using genetic algorithms. Expert Systems with Applications, 198:116865, 2022.
  21. A. Patel and R. Shah. Automated ecg classification for sleep apnea detection using attention-based deep learning. IEEE Access, 11:10245–10256, 2023.
  22. M. Patel and S. Kumar. Deep learning-based obstructive sleep apnea classification from ecg signals. Neural Computing and Applications, 35:1173–1185, 2023.
  23. R. K. Pathinarupothi, E. S. Rangan, E. A. Gopalakrishnan, R. Vinaykumar, and K. P. Soman. Single sensor techniques for sleep apnea diagnosis using deep learning. In 2017 IEEE International Conference on Healthcare Informatics (ICHI), pages 524–529, 2017.
  24. A. Roy and B. Verma. Automatic sleep stage classification using deep belief networks. Artificial Intelligence in Medicine, 128:102162, 2022.
  25. A. Sheta, H. Turabieh, T. Thaher, and M. Mafarja. Diagnosis of obstructive sleep apnea from ecg signals using machine learning and deep learning classifiers. Applied Sciences, 11(14):6622, 2021.
  26. R. Singh and P. Verma. Efficient ecg-based sleep apnea detection using deep residual networks. IEEE Transactions on Biomedical Engineering, 69(2):667–677, 2022.
  27. E. Urtnasan, J. U. Park, E. Y. Joo, and K. J. Lee. Identification of sleep apnea severity based on deep learning from a short-term normal ecg. Journal of Korean Medical Science, 35(47):1–11, 2020.
  28. S. Vattamthanam, G. B. Mrudula, and C. S. Kumar. Sleep apnea classification using deep neural network. In 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), pages 133– 136, 2020.
  29. E.Wang, I. Koprinska, and B. Jeffries. Sleep apnea prediction using deep learning. IEEE Journal of Biomedical and Health Informatics, 27(11):5644–5654, 2023.
  30. K. Wang and L. Zhang. Automated detection of sleep apnea using deep transfer learning on ecg signals. IEEE Access, 9:112530–112540, 2021.
  31. M. Yildirim and S. Aslan. Hybrid cnn-rnn model for automatic sleep apnea detection. Computers in Biology and Medicine, 148:105837, 2022.
  32. B. Zhang and X. Li. Sleep apnea severity prediction using wavelet transform and deep cnn. Biomedical Engineering Online, 20:58, 2021.
  33. L. Zhang and Q. Xu. Graph neural networks for sleep apnea classification using ecg signals. IEEE Transactions on Neural Networks and Learning Systems, 34:890–900, 2023.
  34. T. Zhang and M. Singh. End-to-end deep learning approach for ecg-based sleep apnea detection. IEEE Transactions on Biomedical Circuits and Systems, 17:432–442, 2023.
  35. L. Zhao, Y. Wang, and X. Zhou. A hybrid cnn-lstm model for ecg-based sleep apnea classification. IEEE Transactions on Biomedical Circuits and Systems, 14(4):753–762, 2020.
Index Terms

Computer Science
Information Sciences

Keywords

Sleep Apnea Deep Learning ECG Classification Wavelet Transform HRV Analysis Bayesian Optimization Wearable Health Monitoring

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