Can pre-trained convolutional neural networks be directly used as a feature extractor for video-based neonatal sleep and
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BMC Research Notes Open Access
RESEARCH NOTE
Can pre‑trained convolutional neural networks be directly used as a feature extractor for video‑based neonatal sleep and wake classification? Muhammad Awais1 , Xi Long2,7, Bin Yin3, Chen Chen1, Saeed Akbarzadeh1, Saadullah Farooq Abbasi1, Muhammad Irfan1, Chunmei Lu4*, Xinhua Wang5, Laishuan Wang4 and Wei Chen1,6*
Abstract Objective: In this paper, we propose to evaluate the use of pre-trained convolutional neural networks (CNNs) as a features extractor followed by the Principal Component Analysis (PCA) to find the best discriminant features to perform classification using support vector machine (SVM) algorithm for neonatal sleep and wake states using F luke® facial video frames. Using pre-trained CNNs as a feature extractor would hugely reduce the effort of collecting new neonatal data for training a neural network which could be computationally expensive. The features are extracted after fully connected layers (FCL’s), where we compare several pre-trained CNNs, e.g., VGG16, VGG19, InceptionV3, GoogLeNet, ResNet, and AlexNet. Results: From around 2-h Fluke® video recording of seven neonates, we achieved a modest classification performance with an accuracy, sensitivity, and specificity of 65.3%, 69.8%, 61.0%, respectively with AlexNet using F luke® (RGB) video frames. This indicates that using a pre-trained model as a feature extractor could not fully suffice for highly reliable sleep and wake classification in neonates. Therefore, in future work a dedicated neural network trained on neonatal data or a transfer learning approach is required. Keywords: Convolutional neural networks (CNNs), Video electroencephalogram (VEEG), Neonatal sleep, Sleep and wake classification, Feature extraction Introduction Sleep is an essential behavior for the development of the nervous system in neonates [1–3]. Normally, newborn babies sleep between 16 and 18 h per day. Continuous sleep tracking and assessment could potentially provide an indicator of brain development over time [4, *Correspondence: [email protected]; [email protected] 1 Center for Intelligent Medical Electronics, Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China 4 Department of Neonatology, Children’s Hospital of Fudan University, Shanghai 200032, China Full list of author information is available at the end of the article
5]. To achieve this, automatic sleep and wake analysis is required, which can offer valuable information on a neonate’s mental and physical growth, not only for healthcare professionals but also for parents [6]. Currently, Video electroencephalogram (VEEG) is considered as a gold standard for neonatal sleep monitoring, which requires a number of sensors and electrodes attached to a neonate’s skin to collect multiple-channel EEG signals [7–9]. In addition, the use of VEEG is laborintensive, where the human effort on annotating sleep states is required [10]. Therefore, one would demand a contact-free sleep monitoring
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