Detection of Cheyne-Stokes Breathing using a transformer-based neural network

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Detection of Cheyne-Stokes Breathing using a transformer-based neural network. / Helge, Asbjoern W.; Hanif, Umaer; Joergensen, Villads H.; Jennum, Poul; Mignot, Emmanuel; Sorensen, Helge B.D.

44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022. IEEE, 2022. p. 4580-4583 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Vol. 2022-July).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Helge, AW, Hanif, U, Joergensen, VH, Jennum, P, Mignot, E & Sorensen, HBD 2022, Detection of Cheyne-Stokes Breathing using a transformer-based neural network. in 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022. IEEE, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2022-July, pp. 4580-4583, 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022, Glasgow, United Kingdom, 11/07/2022. https://doi.org/10.1109/EMBC48229.2022.9871537

APA

Helge, A. W., Hanif, U., Joergensen, V. H., Jennum, P., Mignot, E., & Sorensen, H. B. D. (2022). Detection of Cheyne-Stokes Breathing using a transformer-based neural network. In 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022 (pp. 4580-4583). IEEE. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS Vol. 2022-July https://doi.org/10.1109/EMBC48229.2022.9871537

Vancouver

Helge AW, Hanif U, Joergensen VH, Jennum P, Mignot E, Sorensen HBD. Detection of Cheyne-Stokes Breathing using a transformer-based neural network. In 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022. IEEE. 2022. p. 4580-4583. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Vol. 2022-July). https://doi.org/10.1109/EMBC48229.2022.9871537

Author

Helge, Asbjoern W. ; Hanif, Umaer ; Joergensen, Villads H. ; Jennum, Poul ; Mignot, Emmanuel ; Sorensen, Helge B.D. / Detection of Cheyne-Stokes Breathing using a transformer-based neural network. 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022. IEEE, 2022. pp. 4580-4583 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Vol. 2022-July).

Bibtex

@inproceedings{93a9a61ae9a242579b94ca4c3bf1bba0,

title = "Detection of Cheyne-Stokes Breathing using a transformer-based neural network",

abstract = "Annotation of sleep disordered breathing, including Cheyne-Stokes Breathing (CSB), is an expensive and time-consuming process for the clinician. To solve the problem, this paper presents a deep learning-based algorithm for automatic sample-wise detection of CSB in nocturnal polysomnographic (PSG) recordings. 523 PSG recordings were retrieved from four different sleep cohorts and subsequently scored for CSB by three certified sleep technicians. The data was pre-processed and 16 time domain features were extracted and passed into a neural network inspired by the transformer unit. Finally, the network output was post-processed to achieve physiologically meaningful predictions. The algorithm reached a F1-score of 0.76, close to the certified sleep technicians showing that it is possible to automatically detect CSB with the proposed model. The algorithm had difficulties distinguishing between severe obstructive sleep apnea and CSB but this was not dissimilar to technician performance. In conclusion, the proposed algorithm showed promising results and a confirmation of the performance could make it relevant as a screening tool in a clinical setting.",

author = "Helge, {Asbjoern W.} and Umaer Hanif and Joergensen, {Villads H.} and Poul Jennum and Emmanuel Mignot and Sorensen, {Helge B.D.}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022 ; Conference date: 11-07-2022 Through 15-07-2022",

year = "2022",

doi = "10.1109/EMBC48229.2022.9871537",

language = "English",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

publisher = "IEEE",

pages = "4580--4583",

booktitle = "44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022",

}

RIS

TY - GEN

T1 - Detection of Cheyne-Stokes Breathing using a transformer-based neural network

AU - Helge, Asbjoern W.

AU - Hanif, Umaer

AU - Joergensen, Villads H.

AU - Jennum, Poul

AU - Mignot, Emmanuel

AU - Sorensen, Helge B.D.

PY - 2022

Y1 - 2022

N2 - Annotation of sleep disordered breathing, including Cheyne-Stokes Breathing (CSB), is an expensive and time-consuming process for the clinician. To solve the problem, this paper presents a deep learning-based algorithm for automatic sample-wise detection of CSB in nocturnal polysomnographic (PSG) recordings. 523 PSG recordings were retrieved from four different sleep cohorts and subsequently scored for CSB by three certified sleep technicians. The data was pre-processed and 16 time domain features were extracted and passed into a neural network inspired by the transformer unit. Finally, the network output was post-processed to achieve physiologically meaningful predictions. The algorithm reached a F1-score of 0.76, close to the certified sleep technicians showing that it is possible to automatically detect CSB with the proposed model. The algorithm had difficulties distinguishing between severe obstructive sleep apnea and CSB but this was not dissimilar to technician performance. In conclusion, the proposed algorithm showed promising results and a confirmation of the performance could make it relevant as a screening tool in a clinical setting.

AB - Annotation of sleep disordered breathing, including Cheyne-Stokes Breathing (CSB), is an expensive and time-consuming process for the clinician. To solve the problem, this paper presents a deep learning-based algorithm for automatic sample-wise detection of CSB in nocturnal polysomnographic (PSG) recordings. 523 PSG recordings were retrieved from four different sleep cohorts and subsequently scored for CSB by three certified sleep technicians. The data was pre-processed and 16 time domain features were extracted and passed into a neural network inspired by the transformer unit. Finally, the network output was post-processed to achieve physiologically meaningful predictions. The algorithm reached a F1-score of 0.76, close to the certified sleep technicians showing that it is possible to automatically detect CSB with the proposed model. The algorithm had difficulties distinguishing between severe obstructive sleep apnea and CSB but this was not dissimilar to technician performance. In conclusion, the proposed algorithm showed promising results and a confirmation of the performance could make it relevant as a screening tool in a clinical setting.

U2 - 10.1109/EMBC48229.2022.9871537

DO - 10.1109/EMBC48229.2022.9871537

M3 - Article in proceedings

C2 - 36086293

AN - SCOPUS:85138128443

T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

SP - 4580

EP - 4583

BT - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022

PB - IEEE

T2 - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022

Y2 - 11 July 2022 through 15 July 2022

ER -

ID: 329244744