Time-ResNeXt for epilepsy recognition based on EEG signals
in wireless networks
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.
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Posted 12 May, 2020
On 07 Oct, 2020
On 26 May, 2020
Received 04 May, 2020
Received 04 May, 2020
Received 03 May, 2020
On 01 May, 2020
Invitations sent on 30 Apr, 2020
On 30 Apr, 2020
On 30 Apr, 2020
On 27 Apr, 2020
On 26 Apr, 2020
On 26 Apr, 2020
On 02 Apr, 2020
Received 27 Mar, 2020
Received 27 Mar, 2020
Received 27 Mar, 2020
Invitations sent on 24 Mar, 2020
On 24 Mar, 2020
On 24 Mar, 2020
On 24 Mar, 2020
On 26 Feb, 2020
On 25 Feb, 2020
On 18 Feb, 2020
On 14 Feb, 2020
Time-ResNeXt for epilepsy recognition based on EEG signals
in wireless networks
Posted 12 May, 2020
On 07 Oct, 2020
On 26 May, 2020
Received 04 May, 2020
Received 04 May, 2020
Received 03 May, 2020
On 01 May, 2020
Invitations sent on 30 Apr, 2020
On 30 Apr, 2020
On 30 Apr, 2020
On 27 Apr, 2020
On 26 Apr, 2020
On 26 Apr, 2020
On 02 Apr, 2020
Received 27 Mar, 2020
Received 27 Mar, 2020
Received 27 Mar, 2020
Invitations sent on 24 Mar, 2020
On 24 Mar, 2020
On 24 Mar, 2020
On 24 Mar, 2020
On 26 Feb, 2020
On 25 Feb, 2020
On 18 Feb, 2020
On 14 Feb, 2020
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
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Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10