MSED: A Multi-Modal Sleep Event Detection Model for Clinical Sleep Analysis
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MSED : A Multi-Modal Sleep Event Detection Model for Clinical Sleep Analysis. / Zahid, Alexander Neergaard; Jennum, Poul; Mignot, Emmanuel; Sorensen, Helge B.D.
In: IEEE Transactions on Biomedical Engineering, Vol. 70, No. 9, 2023, p. 2508-2518.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - MSED
T2 - A Multi-Modal Sleep Event Detection Model for Clinical Sleep Analysis
AU - Zahid, Alexander Neergaard
AU - Jennum, Poul
AU - Mignot, Emmanuel
AU - Sorensen, Helge B.D.
N1 - Publisher Copyright: © 1964-2012 IEEE.
PY - 2023
Y1 - 2023
N2 - Clinical sleep analysis require manual analysis of sleep patterns for correct diagnosis of sleep disorders. However, several studies have shown significant variability in manual scoring of clinically relevant discrete sleep events, such as arousals, leg movements, and sleep disordered breathing (apneas and hypopneas). We investigated whether an automatic method could be used for event detection and if a model trained on all events (joint model) performed better than corresponding event-specific models (single-event models). We trained a deep neural network event detection model on 1653 individual recordings and tested the optimized model on 1000 separate hold-out recordings. F1 scores for the optimized joint detection model were 0.70, 0.63, and 0.62 for arousals, leg movements, and sleep disordered breathing, respectively, compared to 0.65, 0.61, and 0.60 for the optimized single-event models. Index values computed from detected events correlated positively with manual annotations (r2 = 0.73, r2 = 0.77, r2 = 0.78, respectively). We furthermore quantified model accuracy based on temporal difference metrics, which improved overall by using the joint model compared to single-event models. Our automatic model jointly detects arousals, leg movements and sleep disordered breathing events with high correlation with human annotations. Finally, we benchmark against previous state-of-The-Art multi-event detection models and found an overall increase in F1 score with our proposed model despite a 97.5% reduction in model size.
AB - Clinical sleep analysis require manual analysis of sleep patterns for correct diagnosis of sleep disorders. However, several studies have shown significant variability in manual scoring of clinically relevant discrete sleep events, such as arousals, leg movements, and sleep disordered breathing (apneas and hypopneas). We investigated whether an automatic method could be used for event detection and if a model trained on all events (joint model) performed better than corresponding event-specific models (single-event models). We trained a deep neural network event detection model on 1653 individual recordings and tested the optimized model on 1000 separate hold-out recordings. F1 scores for the optimized joint detection model were 0.70, 0.63, and 0.62 for arousals, leg movements, and sleep disordered breathing, respectively, compared to 0.65, 0.61, and 0.60 for the optimized single-event models. Index values computed from detected events correlated positively with manual annotations (r2 = 0.73, r2 = 0.77, r2 = 0.78, respectively). We furthermore quantified model accuracy based on temporal difference metrics, which improved overall by using the joint model compared to single-event models. Our automatic model jointly detects arousals, leg movements and sleep disordered breathing events with high correlation with human annotations. Finally, we benchmark against previous state-of-The-Art multi-event detection models and found an overall increase in F1 score with our proposed model despite a 97.5% reduction in model size.
KW - Computational sleep science
KW - deep neural network
KW - object detection
U2 - 10.1109/TBME.2023.3252368
DO - 10.1109/TBME.2023.3252368
M3 - Journal article
C2 - 37028083
AN - SCOPUS:85149411681
VL - 70
SP - 2508
EP - 2518
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
SN - 0018-9294
IS - 9
ER -
ID: 367308874