Novel Framework: Face Feature Selection Algorithm for Neonatal Facial and Related Attributes Recognition

Awais, Muhammad; Chen, Chen; Long, Xi; Yin, Bin; Nawaz, Anum; Abbasi, Saadullah Farooq; Akbarzadeh, Saeed; Tao, Linkai; Lu, Chunmei; Wang, Laishuan; Aarts, Ronald M.; Chen, Wei

doi:10.1109/access.2020.2982865

Cited by 19 publications

(20 citation statements)

References 39 publications

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“…Video EEG (VEEG) recordings from 19 neonates were used for testing and training the network [41]. EEG recordings were obtained from a NICU at Children's hospital affiliated to Fudan University, Shanghai, China.…”

Section: A Datasetmentioning

confidence: 99%

EEG-Based Neonatal Sleep-Wake Classification Using Multilayer Perceptron Neural Network

et al. 2020

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Objective: Classification of sleep-wake states using multichannel electroencephalography (EEG) data that reliably work for neonates. Methods: A deep multilayer perceptron (MLP) neural network is developed to classify sleep-wake states using multichannel bipolar EEG signals, which takes an input vector of size 108 containing the joint features of 9 channels. The network avoids any post-processing step in order to work as a full-fledged real-time application. For training and testing the model, EEG recordings of 3525 30second segments from 19 neonates (postmenstrual age of 37 ± 05 weeks) are used. Results: For sleep-wake classification, mean Cohen's kappa between the network estimate and the ground truth annotation by human experts is 0.62. The maximum mean accuracy can reach up to 83% which, to date, is the highest accuracy for sleep-wake classification.

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Section: A Datasetmentioning

confidence: 99%

EEG-Based Neonatal Sleep-Wake Classification Using Multilayer Perceptron Neural Network

et al. 2020

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“…Video and VEEG data from seven neonates were collected retrospectively by a pediatrician at the Children’s Hospital affiliated to Fudan University, Shanghai, China [ 25 ]. The detailed descriptions of the demographics and physical conditions of the neonates are shown in Additional file 1 : Table S1.…”

Section: Main Textmentioning

confidence: 99%

“…To enable identifying sleep and wake states for neonates using video frames, it is required to have precise face detection in Fluke ® video [ 27 ]. Detail description of Intensity-based detection has been discussed in our previous paper [ 25 ]. Figure 1 shows the input video frame, and the neonatal facial region is detected using an intensity-based method.…”

Section: Main Textmentioning

confidence: 99%

Can pre-trained convolutional neural networks be directly used as a feature extractor for video-based neonatal sleep and wake classification?

et al. 2020

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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 Fluke® 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 Fluke® (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.

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“…Video and VEEG data from seven neonates were collected retrospectively by a pediatrician at the Children's Hospital a liated to Fudan University, Shanghai, China [25]. The detailed descriptions of the demographics and physical conditions of neonates is shown in Additional le 1: Table S1.…”

Section: Subject Databasementioning

confidence: 99%

“…To enable identifying sleep and wake states for neonates using video frames, it is required to have precise face detection in Fluke®video [27]. Detail description of Intensity-based detection has been discussed in our previous paper [25]. Figure 1 shows the input video frame, and neonatal facial region is detected using intensity-based method.…”

Section: Intensity-based Detectionmentioning

confidence: 99%

Can Pre-Trained Convolutional Neural Networks be used as Feature Extractors for Video-based Neonatal Sleep and Wake Classification?

Awais

Long

Yin

et al. 2020

Preprint

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Objective In this paper, we propose to evaluate the use of a pre-trained convolutional neural networks (CNN’s) 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 Fluke® facial video frames. Using pre-trained CNN’s as feature extractor would hugely reduce the effort of collecting new neonatal data for training a neural network which could be computationally very expensive. The features are extracted after fully connected layers (FCL’s), where we compare several pre-trained CNN’s, e.g., VGG16, VGG19, InceptionV3, GoogLeNet, ResNet, and AlexNet. Results From around 2-h Fluke® video recording of seven neonate, we achieved a modest classification performance with an accuracy, sensitivity, and specificity of 65.3%, 69.8%, 61.0%, respectively with AlexNet using Fluke® (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 a dedicated neural network trained on neonatal data or a transfer learning approach is required.

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Novel Framework: Face Feature Selection Algorithm for Neonatal Facial and Related Attributes Recognition

Cited by 19 publications

References 39 publications

EEG-Based Neonatal Sleep-Wake Classification Using Multilayer Perceptron Neural Network

EEG-Based Neonatal Sleep-Wake Classification Using Multilayer Perceptron Neural Network

Can pre-trained convolutional neural networks be directly used as a feature extractor for video-based neonatal sleep and wake classification?

Can Pre-Trained Convolutional Neural Networks be used as Feature Extractors for Video-based Neonatal Sleep and Wake Classification?

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