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Original Research

End-to-End Sleep Staging Using Nocturnal Sounds from Microphone Chips for Mobile Devices

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Pages 1187-1201 | Published online: 25 Jun 2022

References

  • Zhai B, Perez-Pozuelo I, Clifton EA, Palotti J, Guan Y. Making sense of sleep: multimodal sleep stage classification in a large, diverse population using movement and cardiac sensing. Proc ACM Interactive Mobile Wearable Ubiquit Technol. 2020;4(2):1–33. doi:10.1145/3397325
  • Walch O, Huang Y, Forger D, Goldstein C. Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device. Sleep. 2019;42(12):zsz180. doi:10.1093/sleep/zsz180
  • Liang Z, Chapa-Martell MA. Accuracy of Fitbit wristbands in measuring sleep stage transitions and the effect of user-specific factors. JMIR mHealth uHealth. 2019;7(6):e13384. doi:10.2196/13384
  • Toften S, Pallesen S, Hrozanova M, Moen F, Grønli J. Validation of sleep stage classification using non-contact radar technology and machine learning (Somnofy®). Sleep Med. 2020;75:54–61. doi:10.1016/j.sleep.2020.02.022
  • Zaffaroni A, Doheny EP, Gahan L, et al. Non-contact estimation of sleep staging. In: EMBEC & NBC 2017. Springer; 2017:77–80.
  • Lauteslager T, Kampakis S, Williams AJ, Maslik M, Siddiqui F. Performance evaluation of the circadia contactless breathing monitor and sleep analysis algorithm for sleep stage classification. In: proceedings from the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC); 2020.
  • Yue S, Yang Y, Wang H, et al. Bodycompass: monitoring sleep posture with wireless signals. Proc ACM Interactive Mobile Wearable Ubiquit Technol. 2020;4(2):1–25. doi:10.1145/3397311
  • Piriyajitakonkij M, Warin P, Lakhan P, et al. SleepPoseNet: multi-view learning for sleep postural transition recognition using UWB. IEEE J Biomed Health Inform. 2020;25(4):1305–1314. doi:10.1109/JBHI.2020.3025900
  • Dafna E, Tarasiuk A, Zigel Y. Sleep staging using nocturnal sound analysis. Sci Rep. 2018;8(1):1–14. doi:10.1038/s41598-018-31748-0
  • Ghahjaverestan NM, Akbarian S, Hafezi M, et al. Sleep/wakefulness detection using tracheal sounds and movements. Nat Sci Sleep. 2020;12:1009. doi:10.2147/NSS.S276107
  • Nakano H, Furukawa T, Tanigawa T. Tracheal sound analysis using a deep neural network to detect sleep apnea. J Clin Sleep Med. 2019;15(8):1125–1133. doi:10.5664/jcsm.7804
  • Kalkbrenner C, Brucher R, Kesztyüs T, Eichenlaub M, Rottbauer W, Scharnbeck D. Automated sleep stage classification based on tracheal body sound and actigraphy. German Med Sci. 2019;17. doi:10.3205/000268
  • Berry RB, Brooks R, Gamaldo CE, Harding SM, Marcus C, Vaughn BV. The AASM manual for the scoring of sleep and associated events. Am Acad Sleep Med. 2012;176:2012.
  • Eklund -V-V. Data augmentation techniques for robust audio analysis. 2019.
  • Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. In: proceedings from the Advances in neural information processing systems; 2017.
  • Hori T, Watanabe S, Zhang Y, Chan W. Advances in joint CTC-attention based end-to-end speech recognition with a deep CNN encoder and RNN-LM. arXiv preprint. 2017;arXiv:170602737.
  • Howard J, Ruder S. Universal language model fine-tuning for text classification. arXiv preprint. 2018;arXiv:180106146.
  • Xue B, Deng B, Hong H, Wang Z, Zhu X, Feng DD. Non-contact sleep stage detection using canonical correlation analysis of respiratory sound. IEEE J Biomed Health Inform. 2019;24(2):614–625. doi:10.1109/JBHI.2019.2910566
  • Liang S-F, Kuo C-E, Hu Y-H, Cheng Y-S. A rule-based automatic sleep staging method. J Neurosci Methods. 2012;205(1):169–176. doi:10.1016/j.jneumeth.2011.12.022
  • Andreotti F, Phan H, Cooray N, Lo C, Hu MT, De Vos M. Multichannel sleep stage classification and transfer learning using convolutional neural networks. In: proceedings from the 2018 40th annual international conference of the IEEE Engineering in medicine and biology society (EMBC); 2018.
  • Andreotti F, Phan H, De Vos M. Visualising convolutional neural network decisions in automatic sleep scoring. In: proceedings from the CEUR Workshop Proceedings; 2018.
  • Supratak A, Dong H, Wu C, Guo Y. DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Trans Neural Syst Rehabil Eng. 2017;25(11):1998–2008. doi:10.1109/TNSRE.2017.2721116
  • Phan H, Andreotti F, Cooray N, Chén OY, De Vos M. Automatic sleep stage classification using single-channel EEG: learning sequential features with attention-based recurrent neural networks. In: proceedings from the 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC); 2018.
  • Guillot A, Sauvet F, During EH, Thorey V. Dreem open datasets: multi-scored sleep datasets to compare human and automated sleep staging. IEEE Trans Neural Syst Rehabil Eng. 2020;28(9):1955–1965. doi:10.1109/TNSRE.2020.3011181
  • Li Y, Gu Z, Lin Z, Yu Z, Li Y. An automatic sleep staging model combining feature learning and sequence learning. In: proceedings from the 2020 12th International Conference on Advanced Computational Intelligence (ICACI); 2020.
  • Seo H, Back S, Lee S, Park D, Kim T, Lee K. Intra-and inter-epoch temporal context network (IITNet) using sub-epoch features for automatic sleep scoring on raw single-channel EEG. Biomed Signal Process Control. 2020;61:102037. doi:10.1016/j.bspc.2020.102037
  • Phan H, Chén OY, Tran MC, Koch P, Mertins A, De Vos M. XSleepNet: multi-view sequential model for automatic sleep staging. IEEE Trans Pattern Anal Mach Intell. 2021;1. doi:10.1109/TPAMI.2021.3070057
  • Guillot A, Thorey V. RobustSleepNet: transfer learning for automated sleep staging at scale. arXiv preprint. 2021;arXiv:210102452.
  • Phan H, Chén OY, Koch P, Mertins A, De Vos M. Fusion of end-to-end deep learning models for sequence-to-sequence sleep staging. In: proceedings from the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2019.
  • Phan H, Andreotti F, Cooray N, Chén OY, De Vos M. SeqSleepNet: end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging. IEEE Trans Neural Syst Rehabil Eng. 2019;27(3):400–410. doi:10.1109/TNSRE.2019.2896659
  • Sun H, Ganglberger W, Panneerselvam E, et al. Sleep staging from electrocardiography and respiration with deep learning. Sleep. 2020;43(7):zsz306. doi:10.1093/sleep/zsz306
  • Chinoy ED, Cuellar JA, Huwa KE, et al. Performance of seven consumer sleep-tracking devices compared with polysomnography. Sleep. 2021;44(5):zsaa291. doi:10.1093/sleep/zsaa291