Bispectrum and Recurrent Neural Networks : Improved Classification of Interictal and Preictal States
This work proposes a novel approach for the classification of interictal and preictal brain states based on bispectrum analysis and recurrent Long Short-Term Memory (LSTM) neural networks. Two features were first extracted from bilateral intracranial electroencephalography (iEEG) recordings of dogs with naturally occurring focal epilepsy. Single-layer LSTM networks were trained to classify 5-min long feature vectors as preictal or interictal. Classification performances were compared to previous work involving multilayer perceptron networks and higher-order spectral (HOS) features on the same dataset. The proposed LSTM network proved superior to the multilayer perceptron network and achieved an average classification accuracy of 86.29% on held-out data. Results imply the possibility of forecasting epileptic seizures using recurrent neural networks, with minimal feature extraction.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2019 |
|---|---|
| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:9 |
|---|---|
| Enthalten in: |
Scientific reports - 9(2019), 1 vom: 30. Okt., Seite 15649 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Gagliano, Laura [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Completed 06.11.2020 Date Revised 10.01.2021 published: Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1038/s41598-019-52152-2 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM302711171 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM302711171 | ||
| 003 | DE-627 | ||
| 005 | 20231226200723.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2019 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1038/s41598-019-52152-2 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1009.xml |
| 035 | |a (DE-627)NLM302711171 | ||
| 035 | |a (NLM)31666621 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Gagliano, Laura |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Bispectrum and Recurrent Neural Networks |b Improved Classification of Interictal and Preictal States |
| 264 | 1 | |c 2019 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 06.11.2020 | ||
| 500 | |a Date Revised 10.01.2021 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a This work proposes a novel approach for the classification of interictal and preictal brain states based on bispectrum analysis and recurrent Long Short-Term Memory (LSTM) neural networks. Two features were first extracted from bilateral intracranial electroencephalography (iEEG) recordings of dogs with naturally occurring focal epilepsy. Single-layer LSTM networks were trained to classify 5-min long feature vectors as preictal or interictal. Classification performances were compared to previous work involving multilayer perceptron networks and higher-order spectral (HOS) features on the same dataset. The proposed LSTM network proved superior to the multilayer perceptron network and achieved an average classification accuracy of 86.29% on held-out data. Results imply the possibility of forecasting epileptic seizures using recurrent neural networks, with minimal feature extraction | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a Bou Assi, Elie |e verfasserin |4 aut | |
| 700 | 1 | |a Nguyen, Dang K |e verfasserin |4 aut | |
| 700 | 1 | |a Sawan, Mohamad |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Scientific reports |d 2011 |g 9(2019), 1 vom: 30. Okt., Seite 15649 |w (DE-627)NLM215703936 |x 2045-2322 |7 nnns |
| 773 | 1 | 8 | |g volume:9 |g year:2019 |g number:1 |g day:30 |g month:10 |g pages:15649 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1038/s41598-019-52152-2 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 9 |j 2019 |e 1 |b 30 |c 10 |h 15649 | ||