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Research
Time-ResNeXt for epilepsy recognition based on EEG signals
in wireless networks
shudong wang
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at EURASIP Journal on Wireless Communications and Networking.
To automatically detect dynamic EEG signals to reduce the time cost of epilepsy diagnosis. In the signal recognition of electroencephalogram (EEG)of epilepsy, traditional machine learning and statistical methods require manual feature labeling engineering in order to show excellent results on a single data set. And the artificially selected features may carry a bias, and cannot guarantee the validity and expansibility in real-world data. In practical applications, deep learning methods can release people from feature engineering to a certain extent. As long as the focus is on the expansion of data quality and quantity, the algorithm model can learn automatically to get better improvements. In addition, the deep learning method can also extract many features that are difficult for humans to perceive, thereby making the algorithm more robust. Based on the design idea of ResNeXt deep neural network, this paper designs a Time-ResNeXt network structure suitable for time series EEG epilepsy detection to identify EEG signals. The accuracy rate of Time-ResNeXt in the detection of EEG epilepsy can reach 91.50%. The Time-ResNeXt network structure produces extremely advanced performance on the benchmark dataset (Berne-Barcelona dataset), and has great potential for improving clinical practice.
Keywords
artificial intelligence, deep learning, epilepsy detection, Time-ResNeXt
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CURRENT STATUS: Published
Version 2
Posted 12 May, 2020
Published
On 07 Oct, 2020
No community comments so far
Editorial decision: Accept
On 26 May, 2020
Review #2 received
Received 04 May, 2020
Review #1 received
Received 04 May, 2020
Review #3 received
Received 03 May, 2020
Reviewer #3 agreed
On 01 May, 2020
Reviewers invited
Invitations sent on 30 Apr, 2020
Reviewer #1 agreed
On 30 Apr, 2020
Reviewer #2 agreed
On 30 Apr, 2020
Editor assigned
On 27 Apr, 2020
Submission checks complete
On 26 Apr, 2020
Editor invited
On 26 Apr, 2020
No comments provided
Editorial decision: Major revision
On 02 Apr, 2020
Review #3 received
Received 27 Mar, 2020
Review #2 received
Received 27 Mar, 2020
Review #1 received
Received 27 Mar, 2020
Reviewers invited
Invitations sent on 24 Mar, 2020
Reviewer #1 agreed
On 24 Mar, 2020
Reviewer #2 agreed
On 24 Mar, 2020
Reviewer #3 agreed
On 24 Mar, 2020
Editor assigned
On 26 Feb, 2020
Editor invited
On 25 Feb, 2020
Submission checks complete
On 18 Feb, 2020
First submitted
On 14 Feb, 2020
Metrics
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Subject Areas
Artificial Intelligence and Machine Learning
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This preprint is under consideration at EURASIP Journal on Wireless Communications and Networking. A preprint is 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
Time-ResNeXt for epilepsy recognition based on EEG signals
in wireless networks
shudong wang
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
Version 2
Posted 12 May, 2020
Published
On 07 Oct, 2020
No community comments so far
Editorial decision: Accept
On 26 May, 2020
Review #2 received
Received 04 May, 2020
Review #1 received
Received 04 May, 2020
Review #3 received
Received 03 May, 2020
Reviewer #3 agreed
On 01 May, 2020
Reviewers invited
Invitations sent on 30 Apr, 2020
Reviewer #1 agreed
On 30 Apr, 2020
Reviewer #2 agreed
On 30 Apr, 2020
Editor assigned
On 27 Apr, 2020
Submission checks complete
On 26 Apr, 2020
Editor invited
On 26 Apr, 2020
No comments provided
Editorial decision: Major revision
On 02 Apr, 2020
Review #3 received
Received 27 Mar, 2020
Review #2 received
Received 27 Mar, 2020
Review #1 received
Received 27 Mar, 2020
Reviewers invited
Invitations sent on 24 Mar, 2020
Reviewer #1 agreed
On 24 Mar, 2020
Reviewer #2 agreed
On 24 Mar, 2020
Reviewer #3 agreed
On 24 Mar, 2020
Editor assigned
On 26 Feb, 2020
Editor invited
On 25 Feb, 2020
Submission checks complete
On 18 Feb, 2020
First submitted
On 14 Feb, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Artificial Intelligence and Machine Learning
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at EURASIP Journal on Wireless Communications and Networking.
To automatically detect dynamic EEG signals to reduce the time cost of epilepsy diagnosis. In the signal recognition of electroencephalogram (EEG)of epilepsy, traditional machine learning and statistical methods require manual feature labeling engineering in order to show excellent results on a single data set. And the artificially selected features may carry a bias, and cannot guarantee the validity and expansibility in real-world data. In practical applications, deep learning methods can release people from feature engineering to a certain extent. As long as the focus is on the expansion of data quality and quantity, the algorithm model can learn automatically to get better improvements. In addition, the deep learning method can also extract many features that are difficult for humans to perceive, thereby making the algorithm more robust. Based on the design idea of ResNeXt deep neural network, this paper designs a Time-ResNeXt network structure suitable for time series EEG epilepsy detection to identify EEG signals. The accuracy rate of Time-ResNeXt in the detection of EEG epilepsy can reach 91.50%. The Time-ResNeXt network structure produces extremely advanced performance on the benchmark dataset (Berne-Barcelona dataset), and has great potential for improving clinical practice.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10