A convolutional neural network outperforming state-of-the-art sleep staging algorithms for both preterm and term infants.
J Neural Eng
; 17(1): 016028, 2020 01 14.
Article
en En
| MEDLINE
| ID: mdl-31689694
ABSTRACT
OBJECTIVE:
To classify sleep states using electroencephalogram (EEG) that reliably works over a wide range of preterm ages, as well as term age.APPROACH:
A convolutional neural network is developed to perform 2- and 4-class sleep classification in neonates. The network takes as input an 8-channel 30 s EEG segment and outputs the sleep state probabilities. Apart from simple downsampling of the input and smoothing of the output, the suggested network is an end-to-end algorithm that avoids the need for hand-crafted feature selection or complex pre/post processing steps. To train and test this method, 113 EEG recordings from 42 infants are used. MAINRESULTS:
For quiet sleep detection (the 2-class problem), mean kappa between the network estimate and the ground truth annotated by EEG human experts is 0.76. The sensitivity and specificity are 90% and 88%, respectively. For 4-class classification, mean kappa is 0.64. The averaged sensitivity and specificity (1 versus all) respectively equal 72% and 91%. The results outperform current state-of-the-art methods for which kappa ranges from 0.66 to 0.70 in preterm and from 0.51 to 0.61 in term infants, based on training and testing using the same database.SIGNIFICANCE:
The proposed method has the highest reported accuracy for EEG sleep state classification for both preterm and term age neonates.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Fases del Sueño
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Algoritmos
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Encéfalo
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Recien Nacido Prematuro
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Redes Neurales de la Computación
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Electroencefalografía
Tipo de estudio:
Health_economic_evaluation
Límite:
Humans
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Newborn
Idioma:
En
Revista:
J Neural Eng
Asunto de la revista:
NEUROLOGIA
Año:
2020
Tipo del documento:
Article
País de afiliación:
Bélgica