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 - 2022/7
Y1 - 2022/7
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.
KW - Cheyne-Stokes Respiration/diagnosis
KW - Humans
KW - Neural Networks, Computer
KW - Sleep/physiology
KW - Sleep Apnea Syndromes/diagnosis
KW - Sleep Apnea, Obstructive/diagnosis
UR - http://www.scopus.com/inward/record.url?scp=85138128443&partnerID=8YFLogxK
U2 - 10.1109/EMBC48229.2022.9871537
DO - 10.1109/EMBC48229.2022.9871537
M3 - Conference article
C2 - 36086293
SN - 2375-7477
VL - 2022
SP - 4580
EP - 4583
JO - Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society
JF - Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society
ER -