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Automatic prediction of obstructive sleep apnea event using deep learning algorithm based on ECG and thoracic movement signals

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Pages 52-57 | Received 15 Sep 2023, Accepted 23 Dec 2023, Published online: 19 Jan 2024

References

  • Ralls F, Cutchen L. A contemporary review of obstructive sleep apnea. Curr Opin Pulm Med. 2019;25(6):578–593. doi: 10.1097/MCP.0000000000000623.
  • Benjafield AV, Ayas NT, Eastwood PR, et al. Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. Lancet Respir Med. 2019;7(8):687–698. doi: 10.1016/S2213-2600(19)30198-5.
  • Senaratna CV, Perret JL, Lodge CJ, et al. Prevalence of obstructive sleep apnea in the general population: a systematic review. Sleep Med Rev. 2017;34:70–81. doi: 10.1016/j.smrv.2016.07.002.
  • Berry RB, Brooks R, Gamaldo C, et al. AASM scoring manual updates for 2017 (version 2.4). J Clin Sleep Med. 2017;13(5):665–666. doi: 10.5664/jcsm.6576.
  • Taghizadegan Y, Jafarnia Dabanloo N, Maghooli K, et al. Prediction of obstructive sleep apnea using ensemble of recurrence plot convolutional neural networks (RPCNNs) from polysomnography signals. Med Hypotheses. 2021;154:110659. doi: 10.1016/j.mehy.2021.110659.
  • Huysmans D, Borzée P, Buyse B, et al. Sleep diagnostics for home monitoring of sleep apnea patients. Front Digit Health. 2021;3:685766. 3 doi: 10.3389/fdgth.2021.685766.
  • He F, Liu T, Tao D. Why ResNet works? Residuals generalize. IEEE Trans Neural Netw Learn Syst. 2020;31(12):5349–5362. doi: 10.1109/TNNLS.2020.2966319.
  • He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016;2016:770–778.
  • Zhang H, Wu C, Zhang Z, et al. ResNeSt: split-attention networks[J]. 2020.
  • Papini GB, Fonseca P, Margarito J, et al. On the generalizability of ECG-based obstructive sleep apnea monitoring: merits and limitations of the apnea-ECG database. Annu Int Conf IEEE Eng Med Biol Soc. 2018;2018:6022–6025.
  • Niroshana SMI, Zhu X, Nakamura K, et al. A fused-image-based approach to detect obstructive sleep apnea using a single-lead ECG and a 2D convolutional neural network. PLoS One. 2021;16(4):e0250618. doi: 10.1371/journal.pone.0250618.
  • Ye G, Yin H, Chen T, et al. FENet: a frequency extraction network for obstructive sleep apnea detection. IEEE J Biomed Health Inform. 2021;25(8):2848–2856. doi: 10.1109/JBHI.2021.3050113.
  • Maali Y, Al-Jumaily A. Multi neural networks investigation based sleep apnea prediction[J. ]. Procedia Computer Science. 2013;24(1):97–102. doi: 10.1016/j.procs.2013.10.031.
  • Koley BL, Dey D. Automatic detection of sleep apnea and hypopnea events from single channel measurement of respiration signal employing ensemble binary SVM classifiers[J. ]. Measurement. 2013;46(7):2082–2092. doi: 10.1016/j.measurement.2013.03.016.
  • Jarchi D, Andreu-Perez J, Kiani M, et al. Recognition of patient groups with sleep related disorders using bio-signal processing and deep learning. Sensors (Basel). 2020;20(9):2594. doi: 10.3390/s20092594.
  • Al-Angari HM, Sahakian AV. Automated recognition of obstructive sleep apnea syndrome using support vector machine classifier. IEEE Trans Inf Technol Biomed. 2012;16(3):463–468. doi: 10.1109/TITB.2012.2185809.
  • Penzel T, Moody GB, Mark RG, et al. The apnea-ECG database[C]//computers in cardiology 2000. IEEE.2000;27:255–258. doi: 10.1109/CIC.2000.898505.
  • Shaffer F, Ginsberg JP. An overview of heart rate variability metrics and norms. Front Public Health. 2017;5:258. doi: 10.3389/fpubh.2017.00258.

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