7th Dutch Bio-Medical Engineering Conference

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11:00
15 mins

An Improved Deep Convolutional Neural Network for Sleep Stage Classification in Preterm and Term Neonates
Amir Hossein Ansari, Kirubin Pillay, Ofelie De Wel, Anneleen Dereymaeker, Katrien Jansen, Alexander Caicedo, Jan Vervisch, Sabine Van Huffel, Gunnar Naulaers, Maarten De Vos

Session: Sleep
Session starts: Thursday 24 January, 10:30
Presentation starts: 11:00
Room: Lecture room 558

Amir Hossein Ansari (KU Leuven, imec)
Kirubin Pillay (University of Oxford)
Ofelie De Wel (KU Leuven, imec)
Anneleen Dereymaeker (KU Leuven)
Katrien Jansen (KU Leuven)
Alexander Caicedo (Universidad del Rosario, Bogotá, Colombia)
Jan Vervisch (KU Leuven)
Sabine Van Huffel (KU Leuven, imec)
Gunnar Naulaers (KU Leuven)
Maarten De Vos (University of Oxford)

Abstract:
Sleep stage analysis provides valuable information about the maturation progress and brain development in preterm and term babies [1], [2]. Accordingly, in neonatal brain monitors, an automated sleep stage classifier can play an important role to improve potentially the quality of monitoring and consequently the treatment. Recently, we introduced a deep neural network for EEG quiet sleep detection in preterm newborns [3]. In the current research, we redesign the architecture of that network in order to be able to classify the two sleep stages of preterm babies with higher accuracy, as well as the four sleep stages of term neonates. Our previously developed deep convolutional neural network has been improved using more filters in the convolutional layers, as well as employing dropout and batch-normalization layers. Then, this deep 12-layer neural network was trained by an advanced back-propagation optimizer, Adadelta [4], with 70 multi-channel EEG recordings of preterm and term neonates. As a result, in addition to the detection of quiet sleep periods in preterm and term babies, this network is able to classify the four term neonates’ sleep stages: 1) trace alternante, 2) high-voltage slow-wave, 3) active sleep type I, and 4) low-voltage irregular. The proposed algorithm has significantly better performance with respect to different metrics including sensitivity, specificity, and kappa compared to our previously developed algorithms, as well as considered state-of-the-art methods, which have been trained and tested on common datasets. The proposed method is also suitable for real-time monitoring.