| Title | A convolutional neural network outperforming state-of-the-art sleep staging algorithms for both preterm and term infants. |
| Author(s) | Ansari AH, De Wel O, Pillay K, Dereymaeker A, Jansen K, Van Huffel S, Naulaers G, De Vos M. |
| Source | J Neural Eng., Vol. 17, No. 1 |
| Publication Date | 1/1/2014 |
| 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. Main results: 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. |