ARCHIVES
Original Article
Computational Phenotyping of Sleep Disorder Using Machine Learning
Abubakar Sithik M1
Lipika K S2
Lakshmi3
Nivedita4
Likhitha K V5
1 Professor, Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bengaluru, Karnataka, India. 2 3 4 5 Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bengaluru, Karnataka, India.
Published Online: November-December 2025
Pages: 173-180
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20250506026
References
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12. N. Salari, A. Hosseinian-Far, M. Mohammadi, H. Ghasemi, H. Khazaie, A. Danesh khah, and A. Ahmadi, ‘‘Detection of sleep apnea using machine learning algorithms based on ECG signals: A comprehensive systematic review,’’ Expert Syst. Appl., vol. 187, Jan. 2022, Art. no. 115950.
13. C. Li, Y. Qi, X. Ding, J. Zhao, T. Sang, and M. Lee, ‘‘A deep learning method approach for sleep stage classification with EEG spectrogram,’’ Int. J. Environ. Res. Public Health, vol. 19, no. 10, p. 6322, May 2022.
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16. S.Satapathy,D.Loganathan,H.K.Kondaveeti,andR.Rath,‘‘Performance analysis of machine learning algorithms on automated sleep staging feature sets,’’ CAAI Trans. Intell. Technol., vol. 6, no. 2, pp. 155–174, Jun. 2021.
17. M. Bahrami and M. Forouzanfar, ‘‘Sleep apnea detection from single-lead ECG: A comprehensive analysis of machine learning and deep learning algorithms,’’ IEEE Trans. Instrum. Meas., vol. 71, pp. 1–11, 2022.
18. J. Ramesh, N. Keeran, A. Sagahyroon, and F. Aloul, ‘‘Towards validating the effectiveness of obstructive sleep apnea classification from electronic health records using machine learning,’’ Healthcare, vol. 9, no. 11, p. 1450, Oct. 2021.
19. S. K. Satapathy, H. K. Kondaveeti, S. R. Sreeja, H. Madhani, N. Rajput, and D. Swain, ‘‘A deep learning approach to automated sleep stages classification using multi-modal signals,’’ Proc. Computer. Sci., vol. 218, pp. 867–876, Jan. 2023.
20. O. Yildirim, U. Baloglu, and U. Acharya, ‘‘A deep learning model for automated sleep stages classification using PSG signals,’’ Int. J. Environ. Res. Public Health, vol. 16, no. 4, p. 599, Feb. 2019.
21. S. Akbar, A. Ahmad, M. Hayat, A. U. Rehman, S. Khan, and F. Ali, ‘‘IAtbP-Hyb-EnC: Prediction of antitubercular peptides via heterogeneous feature representation and genetic algorithm based ensemble learning model,’’ Comput. Biol. Med., vol. 137, Oct. 2021, Art. no. 104778.
2. Y. Li, C. Peng, Y. Zhang, Y. Zhang, and B. Lo, ‘‘Adversarial learning for semi-supervised pediatric sleep staging with single-EEG channel,’’ Methods, vol. 204, pp. 84–91, Aug. 2022.
3. E. Alickovic and A. Subasi, ‘‘Ensemble SVM method for automatic sleep stage classification,’’ IEEE Trans. Instrum. Meas., vol. 67, no. 6, pp. 1258–1265, Jun. 2018.
4. D. Shrivastava, S. Jung, M. Saadat, R. Sirohi, and K. Crewson, ‘‘How to interpret the results of a sleep study,’’ J. Community Hospital Internal Med. Perspect., vol. 4, no. 5, p. 24983, Jan. 2014.
5. V.Singh,V.K.Asari,andR.Rajasekaran,‘‘Adeepneuralnetworkforearly detection and prediction of chronic kidney disease,’’ Diagnostics, vol. 12, no. 1, p. 116, Jan. 2022.
6. J. Van Der Donckt, J. Van Der Donckt, E. Deprost, N. Vandenbussche, M. Rademaker, G. Vandewiele, and S. Van Hoecke, ‘‘Do not sleep on traditional machine learning: Simple and interpretable techniques are competitive to deep learning for sleep scoring,’’ Biomed. Signal Process. Control, vol. 81, Mar. 2023, Art. no. 104429.
7. H. O. Ilhan, ‘‘Sleep stage classification via ensemble and conventional machine learning methods using single channel EEG signals,’’ Int. J. Intel. Syst. Appl. Eng., vol. 4, no. 5, pp. 174–184, Dec. 2017.
8. Y. Yang, Z. Gao, Y. Li, and H. Wang, ‘‘A CNN identified by reinforcement learning-based optimization framework for EEG-based state evaluation,’’ J. Neural Eng., vol. 18, no. 4, Aug. 2021, Art. no. 046059.
9. Y. J. Kim, J. S. Jeon, S.-E. Cho, K. G. Kim, and S.-G. Kang, Prediction models for obstructive sleep apneain Korean adults using machine learning techniques,’’ Diagnostics, vol. 11, no. 4, p. 612, Mar. 2021.
10. Z. Mousavi, T. Y. Rezaii, S. Sheykhivand, A. Farzamnia, and S. N. Razavi, ‘‘Deep convolutional neural network for classification of sleep stages from single-channel EEG signals,’’ J. Neurosci. Methods, vol. 324, Aug. 2019, Art. no. 108312.
11. S. Djanian, A. Bruun, and T. D. Nielsen, ‘‘Sleep classification using consumer sleep technologies and AI: A review of the current landscape,’’ Sleep Med., vol. 100, pp. 390–403, Dec. 2022.
12. N. Salari, A. Hosseinian-Far, M. Mohammadi, H. Ghasemi, H. Khazaie, A. Danesh khah, and A. Ahmadi, ‘‘Detection of sleep apnea using machine learning algorithms based on ECG signals: A comprehensive systematic review,’’ Expert Syst. Appl., vol. 187, Jan. 2022, Art. no. 115950.
13. C. Li, Y. Qi, X. Ding, J. Zhao, T. Sang, and M. Lee, ‘‘A deep learning method approach for sleep stage classification with EEG spectrogram,’’ Int. J. Environ. Res. Public Health, vol. 19, no. 10, p. 6322, May 2022.
14. H. Han and J. Oh, ‘‘Application of various machine learning techniques to predict obstructive sleep apnea syndrome severity,’’ Sci. Rep., vol. 13, no. 1, p. 6379, Apr. 2023.
15. M. Bahrami and M. Forouzanfar, ‘‘Detection of sleep apnea from single lead ECG: Comparison of deep learning algorithms,’’ in Proc. IEEE Int. Symp. Med. Meas. Appl. (MeMeA), Jun. 2021, pp. 1–5.
16. S.Satapathy,D.Loganathan,H.K.Kondaveeti,andR.Rath,‘‘Performance analysis of machine learning algorithms on automated sleep staging feature sets,’’ CAAI Trans. Intell. Technol., vol. 6, no. 2, pp. 155–174, Jun. 2021.
17. M. Bahrami and M. Forouzanfar, ‘‘Sleep apnea detection from single-lead ECG: A comprehensive analysis of machine learning and deep learning algorithms,’’ IEEE Trans. Instrum. Meas., vol. 71, pp. 1–11, 2022.
18. J. Ramesh, N. Keeran, A. Sagahyroon, and F. Aloul, ‘‘Towards validating the effectiveness of obstructive sleep apnea classification from electronic health records using machine learning,’’ Healthcare, vol. 9, no. 11, p. 1450, Oct. 2021.
19. S. K. Satapathy, H. K. Kondaveeti, S. R. Sreeja, H. Madhani, N. Rajput, and D. Swain, ‘‘A deep learning approach to automated sleep stages classification using multi-modal signals,’’ Proc. Computer. Sci., vol. 218, pp. 867–876, Jan. 2023.
20. O. Yildirim, U. Baloglu, and U. Acharya, ‘‘A deep learning model for automated sleep stages classification using PSG signals,’’ Int. J. Environ. Res. Public Health, vol. 16, no. 4, p. 599, Feb. 2019.
21. S. Akbar, A. Ahmad, M. Hayat, A. U. Rehman, S. Khan, and F. Ali, ‘‘IAtbP-Hyb-EnC: Prediction of antitubercular peptides via heterogeneous feature representation and genetic algorithm based ensemble learning model,’’ Comput. Biol. Med., vol. 137, Oct. 2021, Art. no. 104778.
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