Time-ResNeXt for epilepsy recognition based on EEG signals
Abstract Objective 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.Method 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.Results The accuracy rate of Time-ResNeXt in the detection of EEG epilepsy can reach 90.50%.Conclusion The Time-ResNeXt network structure produces extremely advanced performance on the benchmark dataset (Berne-Barcelona dataset), and has great potential for improving clinical practice..
Medienart: |
Preprint |
---|
Erscheinungsjahr: |
2019 |
---|---|
Erschienen: |
2019 |
Enthalten in: |
bioRxiv.org - (2019) vom: 30. Dez. Zur Gesamtaufnahme - year:2019 |
---|
Sprache: |
Englisch |
---|
Beteiligte Personen: |
Wang, shaoqiang [VerfasserIn] |
---|
Links: |
Volltext [kostenfrei] |
---|
doi: |
10.1101/2019.12.27.889238 |
---|
funding: |
|
---|---|
Förderinstitution / Projekttitel: |
|
PPN (Katalog-ID): |
XBI000689378 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | XBI000689378 | ||
003 | DE-627 | ||
005 | 20230429100511.0 | ||
007 | cr uuu---uuuuu | ||
008 | 200313s2019 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1101/2019.12.27.889238 |2 doi | |
035 | |a (DE-627)XBI000689378 | ||
035 | |a (DE-599)biorXiv10.1101/2019.12.27.889238 | ||
035 | |a (biorXiv)10.1101/2019.12.27.889238 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | |a 570 |q DE-84 | |
100 | 1 | |a Wang, shaoqiang |e verfasserin |4 aut | |
245 | 1 | 0 | |a Time-ResNeXt for epilepsy recognition based on EEG signals |
264 | 1 | |c 2019 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a Abstract Objective 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.Method 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.Results The accuracy rate of Time-ResNeXt in the detection of EEG epilepsy can reach 90.50%.Conclusion The Time-ResNeXt network structure produces extremely advanced performance on the benchmark dataset (Berne-Barcelona dataset), and has great potential for improving clinical practice. | ||
700 | 1 | |a Wang, Yifan |e verfasserin |4 aut | |
700 | 1 | |a Wang, Shudong |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t bioRxiv.org |g (2019) vom: 30. Dez. |
773 | 1 | 8 | |g year:2019 |g day:30 |g month:12 |
856 | 4 | 0 | |u http://dx.doi.org/10.1101/2019.12.27.889238 |z kostenfrei |3 Volltext |
912 | |a GBV_XBI | ||
912 | |a SSG-OLC-PHA | ||
951 | |a AR | ||
952 | |j 2019 |b 30 |c 12 | ||
953 | |2 045F |a 570 |