Abstract
Sleep apnea is a sleep disorder caused by weakened or suspended breathing during sleep, which seriously affects the work and health of patients. The traditional polysomnography (PSG) detection process is complicated and expensive, which has attracted researchers to explore a rapid detection method based on single-lead ECG signals. However, existing ECG-based sleep apnea detection methods have certain limitations and complexities, mainly relying on human-crafted features. To solve the problem, the paper develops a sleep apnea detection method based on a residual attention mechanism network. The method uses the RR interval signal and the R-peak signal derived from the ECG signal as input, realizes feature extraction through the residual network (ResNet), and adds the SENet attention mechanism to deepen the mining of channel features. Experimental results show that the per-segment accuracy of the proposed method can reach 86.2%. Compared with existing works, its accuracy has increased by 1.1–8.1%. These results show that the proposed residual attention network can effectively use ECG signals to quickly detect sleep apnea. Meanwhile, compared with existing works, the proposed method overcomes the limitations and complexity of human-crafted features in sleep apnea detection research.
Funding source: The Science and Technology Service Network Initiative of the Chinese Academy of Sciences
Award Identifier / Grant number: KFJ-STS-ZDTP-079
Funding source: The Intelligent Interconnected Systems Laboratory of Anhui Province
Award Identifier / Grant number: PA2021AKSK0112
-
Research funding: The work is supported by the Science and Technology Service Network Initiative of the Chinese Academy of Sciences (Grant No. KFJ-STS-ZDTP-079), and Intelligent Interconnected Systems Laboratory of Anhui Province (Grant No. PA2021AKSK0112).
-
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Competing interests: Authors state no conflict of interest.
-
Informed consent: Not applicable.
-
Ethical approval: Not applicable.
References
1. Yaggi, HK, Concato, J, Kernan, WN, Lichtman, JH, Brass, LM, Mohsenin, V. Obstructive sleep apnea as a risk factor for stroke and death. N Engl J Med 2005;353:2034–41. https://doi.org/10.1056/nejmoa043104.Search in Google Scholar
2. Marshall, NS, Wong, KKH, Liu, PY, Cullen, SRJ, Knuiman, MW, Grunstein, RR. Sleep apnea as an independent risk factor for all-cause mortality: the Busselton health study. Sleep 2008;31:1079–85.Search in Google Scholar
3. Rundo, JV, Downey, RIII. Polysomnography. Handb Clin Neurol 2019;160:381–92.10.1016/B978-0-444-64032-1.00025-4Search in Google Scholar PubMed
4. Avcı, C, Akbaş, A. Sleep apnea classification based on respiration signals by using ensemble methods. Bio Med Mater Eng 2015;26 (1 Suppl):S1703–10. https://doi.org/10.3233/bme-151470.Search in Google Scholar PubMed
5. Rolón, RE, Gareis, IE, Larrateguy, LD, Di Persia, LE, Spies, RD, Rufiner, HL. Automatic scoring of apnea and hypopnea events using blood oxygen saturation signals. Biomed Signal Proces 2020;62:102062. https://doi.org/10.1016/j.bspc.2020.102062.Search in Google Scholar
6. Li, KY, Pan, WF, Li, YF, Jiang, Q, Liu, GZ. A method to detect sleep apnea based on deep neural network and hidden Markov model using single-lead ECG signal. Neurocomputing 2018;294:94–101. https://doi.org/10.1016/j.neucom.2018.03.011.Search in Google Scholar
7. Lakhan, P, Ditthapron, A, Banluesombatkul, N, Wilaiprasitporn, T. Deep neural networks with weighted averaged overnight airflow features for sleep apnea-hypopnea severity classification. In: TENCON 2018-2018 IEEE region 10 conference. Jeju, Korea: IEEE; 2018:0441–5 pp.10.1109/TENCON.2018.8650491Search in Google Scholar
8. Leelaarporn, P, Wachiraphan, P, Kaewlee, T, Udsa, T, Chaisaen, R, Choksatchawathi, T, et al.. Sensor-driven achieving of smart living: a review. IEEE Sensor J 2021;21:10369–91. https://doi.org/10.1109/jsen.2021.3059304.Search in Google Scholar
9. Babaeizadeh, S, White, DP, Pittman, SD, Zhou, SH. Automatic detection and quantification of sleep apnea using heart rate variability. J Electrocardiol 2010;43:535–41. https://doi.org/10.1016/j.jelectrocard.2010.07.003.Search in Google Scholar PubMed
10. Varon, C, Caicedo, A, Testelmans, D, Buyse, B, Van Huffel, S. A novel algorithm for the automatic detection of sleep apnea from single-lead ECG. IEEE Trans Biomed Eng 2015;62:2269–78. https://doi.org/10.1109/tbme.2015.2422378.Search in Google Scholar PubMed
11. Sharma, H, Sharma, KK. An algorithm for sleep apnea detection from single-lead ECG using Hermite basis functions. Comput Biol Med 2016;77:116–24. https://doi.org/10.1016/j.compbiomed.2016.08.012.Search in Google Scholar PubMed
12. Song, C, Liu, K, Zhang, X, Chen, L, Xian, X. An obstructive sleep apnea detection approach using a discriminative hidden Markov model from ECG signals. IEEE Trans Bio-Med Eng 2015;63:1532–42. https://doi.org/10.1109/tbme.2015.2498199.Search in Google Scholar PubMed
13. Banluesombatkul, N, Rakthanmanon, T, Wilaiprasitporn, T. Single channel ECG for obstructive sleep apnea severity detection using a deep learning approach. In: TENCON 2018-2018 IEEE region 10 conference. Jeju, Korea: IEEE; 2018:2011–6 pp.10.1109/TENCON.2018.8650429Search in Google Scholar
14. Gupta, K, Bajaj, V, Ansari, IA. OSACN-Net: automated classification of sleep apnea using deep learning model and smoothed Gabor spectrograms of ECG signal. IEEE Trans Instrum Meas 2021;71:1–9. https://doi.org/10.1109/tim.2021.3132072.Search in Google Scholar
15. Penzel, T, Moody, GB, Mark, RG, Goldberger, AL, Peter, JH. The apnea-ECG database. In: Computers in cardiology (Cat 00CH37163). Cambridge, MA, USA: IEEE; 2000, 27:255–8 pp.Search in Google Scholar
16. Goldberger, AL, Amaral, LAN, Glass, L, Hausdorff, JM, Ivanov, PC, Mark, RG, et al.. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 2000;101:e215–20. https://doi.org/10.1161/01.cir.101.23.e215.Search in Google Scholar PubMed
17. He, K, Zhang, X, Ren, S, Sun, J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. Las Vegas, NV, USA: IEEE; 2016:770–8 pp.10.1109/CVPR.2016.90Search in Google Scholar
18. Hu, J, Shen, L, Sun, G. Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. Salt Lake City, UT, USA: IEEE; 2018:7132–41 pp.10.1109/CVPR.2018.00745Search in Google Scholar
19. Pan, J, Tompkins, WJ. A real-time QRS detection algorithm. IEEE Trans Bio-Med Eng 1985;32:230–6. https://doi.org/10.1109/tbme.1985.325532.Search in Google Scholar
20. Hamilton, P. Open source ECG analysis. Comput Cardiol 2002;101–4. https://doi.org/10.1109/CIC.2002.1166717.Search in Google Scholar
21. Hammad, M, Pławiak, P, Wang, K, Acharya, UR. ResNet-attention model for human authentication using ECG signals. Expet Syst 2021;38:e12547. https://doi.org/10.1111/exsy.12547.Search in Google Scholar
22. Jing, E, Zhang, H, Li, Z, Liu, Y, Ji, Z, Ganchev, I. ECG heartbeat classification based on an improved ResNet-18 model. Comput Math Methods Med 2021;1–13. https://doi.org/10.1155/2021/6649970.Search in Google Scholar PubMed PubMed Central
23. Vicar, T, Hejc, J, Novotna, P, Ronzhina, M, Janousek, O. ECG abnormalities recognition using convolutional network with global skip connections and custom loss function. In: 2020 Computing in cardiology. Rimini, Italy: IEEE; 2020:1–4 pp.10.22489/CinC.2020.189Search in Google Scholar
24. Feng, K, Qin, H, Wu, S, Pan, W, Liu, G. A sleep apnea detection method based on unsupervised feature learning and single-lead electrocardiogram. IEEE Trans Instrum Meas 2020;70:1–12. https://doi.org/10.1109/tim.2020.3017246.Search in Google Scholar
25. Faal, M, Almasganj, F. Obstructive sleep apnea screening from unprocessed ECG signals using statistical modelling. Biomed Signal Process 2021;68:102685. https://doi.org/10.1016/j.bspc.2021.102685.Search in Google Scholar
26. Surrel, G, Aminifar, A, Rincon, F, Murali, S, Atienza, D. Online obstructive sleep apnea detection on medical wearable sensors. IEEE Trans Biomed Circuits Syst 2018;12:762–73. https://doi.org/10.1109/tbcas.2018.2824659.Search in Google Scholar
27. Pinho, A, Pombo, N, Silva, BMC, Bousson, K, Garcia, N. Towards an accurate sleep apnea detection based on ECG signal: the quintessential of a wise feature selection. Appl Soft Comput 2019;83:105568. https://doi.org/10.1016/j.asoc.2019.105568.Search in Google Scholar
28. Viswabhargav, CSSS, Tripathy, RK, Acharya, UR. Automated detection of sleep apnea using sparse residual entropy features with various dictionaries extracted from heart rate and EDR signals. Comput Biol Med 2019;108:20–30. https://doi.org/10.1016/j.compbiomed.2019.03.016.Search in Google Scholar PubMed
29. Christov, II. Real time electrocardiogram QRS detection using combined adaptive threshold. Biomed Eng Online 2004;3:1–9. https://doi.org/10.1186/1475-925x-3-28.Search in Google Scholar PubMed PubMed Central
30. Kalidas, V, Tamil, L. Real-time QRS detector using stationary wavelet transform for automated ECG analysis. In: 2017 IEEE 17th international conference on bioinformatics and bioengineering (BIBE). Washington, DC, USA: IEEE; 2017:457–61 pp.10.1109/BIBE.2017.00-12Search in Google Scholar
31. Elgendi, M, Jonkman, M, De Boer, F. Frequency bands effects on QRS detection. Biosignals 2010;2003:2002.Search in Google Scholar
32. Wang, T, Lu, C, Yang, M, Hong, F, Liu, C. A hybrid method for heartbeat classification via convolutional neural networks, multilayer perceptrons and focal loss. Peer J Comput Sci 2020;6:e324. https://doi.org/10.7717/peerj-cs.324.Search in Google Scholar PubMed PubMed Central
© 2022 Walter de Gruyter GmbH, Berlin/Boston
