Projekt per år
Sammanfattning
Objective
To develop and validate an automated method for bedside monitoring of sleep state fluctuations in neonatal intensive care units.
Methods
A deep learning-based algorithm was designed and trained using 53 EEG recordings from a long-term (a)EEG monitoring in 30 near-term neonates. The results were validated using an external dataset from 30 polysomnography recordings. In addition to training and validating a single EEG channel quiet sleep, we constructed Sleep State Trend (SST), a bedside-ready means for visualising classifier outputs.
Results
The accuracy of quiet sleep detection in the training data was 90%, and the accuracy was comparable (85-86 %) in all bipolar derivations available from the 4-electrode recordings. The algorithm generalised well to an external dataset, showing 81% overall accuracy despite different signal derivations. SST allowed an intuitive, clear visualisation of the classifier output.
Conclusions
Fluctuations in sleep states can be detected at high fidelity from a single EEG channel, and the results can be visualised as a transparent and intuitive trend in the bedside monitors.
Significance
The Sleep State Trend (SST) may provide caregivers a real-time view of sleep state fluctuations and its cyclicity.
To develop and validate an automated method for bedside monitoring of sleep state fluctuations in neonatal intensive care units.
Methods
A deep learning-based algorithm was designed and trained using 53 EEG recordings from a long-term (a)EEG monitoring in 30 near-term neonates. The results were validated using an external dataset from 30 polysomnography recordings. In addition to training and validating a single EEG channel quiet sleep, we constructed Sleep State Trend (SST), a bedside-ready means for visualising classifier outputs.
Results
The accuracy of quiet sleep detection in the training data was 90%, and the accuracy was comparable (85-86 %) in all bipolar derivations available from the 4-electrode recordings. The algorithm generalised well to an external dataset, showing 81% overall accuracy despite different signal derivations. SST allowed an intuitive, clear visualisation of the classifier output.
Conclusions
Fluctuations in sleep states can be detected at high fidelity from a single EEG channel, and the results can be visualised as a transparent and intuitive trend in the bedside monitors.
Significance
The Sleep State Trend (SST) may provide caregivers a real-time view of sleep state fluctuations and its cyclicity.
Originalspråk | engelska |
---|---|
Tidskrift | Clinical Neurophysiology |
Volym | 143 |
Sidor (från-till) | 75-83 |
Antal sidor | 9 |
ISSN | 1388-2457 |
DOI | |
Status | Publicerad - nov. 2022 |
MoE-publikationstyp | A1 Tidskriftsartikel-refererad |
Vetenskapsgrenar
- 217 Medicinsk teknik
- 3112 Neurovetenskaper
- 3124 Neurologi och psykiatri
Projekt
- 1 Slutfört
-
Advancing automated algorithms in neonatal brain monitoring
Montazeri Moghadam, S. (Projektledare)
01/01/2020 → 31/12/2023
Projekt: Forskningsprojekt