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Research note
Chunmei Lu
Children's Hospital of Fudan University
Corresponding Author
Wei Chen
Fudan University
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
Objective: In this paper, we propose to evaluate the use of a 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 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 CNNs, 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.
Keywords
Convolutional neural networks (CNN), Video electroencephalogram (VEEG), Neonatal sleep, sleep and wake classification, feature extraction
Figure 1
Figure 2
This is a list of supplementary files associated with this preprint. Click to download.
- Additionalfile1TableS1update.pdf
Additional file 1 Table S1. Neonatal body condition before the collection of video and VEEG data, The detailed descriptions of the demographics and physical conditions of neonates.
- Additionalfile1TableS2.pdf
Additional file 1: Table S2. Overall ConvNet’s architecture, The details descriptions of all the pre-trained model has been mentioned.
- Additionalfile1TableS3.pdf
Additional file 1: Table S3. Neonatal sleep and wake states classification results using Fluke® multiple colors palattes, statistical results achieved using multiple color palettes such as amber, high contrast, red-blue, hot metal, and grayscale.
- Additionalfile1FigureS1.pdf
Additional file 1: Figure S1. Fluke® color palettes range.
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CURRENT STATUS: Published
View the published article at BMC Research Notes.
No community comments so far
Submission checks complete
On 18 Oct, 2020
Editorial decision: Accept
On 13 Oct, 2020
Version 2
Posted 22 Sep, 2020
No comments provided
Review #1 received
Received 02 Oct, 2020
Reviewer #1 agreed
On 22 Sep, 2020
Editor assigned
On 18 Sep, 2020
Reviewers invited
Invitations sent on 18 Sep, 2020
Submission checks complete
On 17 Sep, 2020
Editor invited
On 17 Sep, 2020
No comments provided
Editorial decision: Minor revision
On 11 Sep, 2020
Review #1 received
Received 08 Sep, 2020
Reviewer #1 agreed
On 29 Aug, 2020
Reviewers invited
Invitations sent on 26 Aug, 2020
Editor assigned
On 22 Aug, 2020
Submission checks complete
On 20 Aug, 2020
Editor invited
On 20 Aug, 2020
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
See the published version of this preprint at BMC Research Notes.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research note
Chunmei Lu
Children's Hospital of Fudan University
Corresponding Author
Wei Chen
Fudan University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Research Notes.
No community comments so far
Submission checks complete
On 18 Oct, 2020
Editorial decision: Accept
On 13 Oct, 2020
Version 2
Posted 22 Sep, 2020
No comments provided
Review #1 received
Received 02 Oct, 2020
Reviewer #1 agreed
On 22 Sep, 2020
Editor assigned
On 18 Sep, 2020
Reviewers invited
Invitations sent on 18 Sep, 2020
Submission checks complete
On 17 Sep, 2020
Editor invited
On 17 Sep, 2020
No comments provided
Editorial decision: Minor revision
On 11 Sep, 2020
Review #1 received
Received 08 Sep, 2020
Reviewer #1 agreed
On 29 Aug, 2020
Reviewers invited
Invitations sent on 26 Aug, 2020
Editor assigned
On 22 Aug, 2020
Submission checks complete
On 20 Aug, 2020
Editor invited
On 20 Aug, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Research Notes.
Objective: In this paper, we propose to evaluate the use of a 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 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 CNNs, 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.
Figure 1
Figure 2
This is a list of supplementary files associated with this preprint. Click to download.
- Additionalfile1TableS1update.pdf
Additional file 1 Table S1. Neonatal body condition before the collection of video and VEEG data, The detailed descriptions of the demographics and physical conditions of neonates.
- Additionalfile1TableS2.pdf
Additional file 1: Table S2. Overall ConvNet’s architecture, The details descriptions of all the pre-trained model has been mentioned.
- Additionalfile1TableS3.pdf
Additional file 1: Table S3. Neonatal sleep and wake states classification results using Fluke® multiple colors palattes, statistical results achieved using multiple color palettes such as amber, high contrast, red-blue, hot metal, and grayscale.
- Additionalfile1FigureS1.pdf
Additional file 1: Figure S1. Fluke® color palettes range.
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