IEICE TRANSACTIONS on Information
Epileptic Seizure Prediction Using Convolutional Neural Networks and Fusion Features on Scalp EEG Signals
Summary :
Epileptic seizure prediction is an important research topic in the clinical epilepsy treatment, which can provide opportunities to take precautionary measures for epilepsy patients and medical staff. EEG is an commonly used tool for studying brain activity, which records the electrical discharge of brain. Many studies based on machine learning algorithms have been proposed to solve the task using EEG signal. In this study, we propose a novel seizure prediction models based on convolutional neural networks and scalp EEG for a binary classification between preictal and interictal states. The short-time Fourier transform has been used to translate raw EEG signals into STFT sepctrums, which is applied as input of the models. The fusion features have been obtained through the side-output constructions and used to train and test our models. The test results show that our models can achieve comparable results in both sensitivity and FPR upon fusion features. The proposed patient-specific model can be used in seizure prediction system for EEG classification.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.5 pp.821-823
- Publication Date
- 2023/05/01
- Publicized
- 2022/05/27
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022DLL0002
- Type of Manuscript
- Special Section LETTER (Special Section on Deep Learning Technologies: Architecture, Optimization, Techniques, and Applications)
- Category
- Smart Healthcare
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Qixin LAN, Bin YAO, Tao QING, "Epileptic Seizure Prediction Using Convolutional Neural Networks and Fusion Features on Scalp EEG Signals" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 5, pp. 821-823, May 2023, doi: 10.1587/transinf.2022DLL0002.
Abstract: Epileptic seizure prediction is an important research topic in the clinical epilepsy treatment, which can provide opportunities to take precautionary measures for epilepsy patients and medical staff. EEG is an commonly used tool for studying brain activity, which records the electrical discharge of brain. Many studies based on machine learning algorithms have been proposed to solve the task using EEG signal. In this study, we propose a novel seizure prediction models based on convolutional neural networks and scalp EEG for a binary classification between preictal and interictal states. The short-time Fourier transform has been used to translate raw EEG signals into STFT sepctrums, which is applied as input of the models. The fusion features have been obtained through the side-output constructions and used to train and test our models. The test results show that our models can achieve comparable results in both sensitivity and FPR upon fusion features. The proposed patient-specific model can be used in seizure prediction system for EEG classification.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022DLL0002/_p
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@ARTICLE{e106-d_5_821,
author={Qixin LAN, Bin YAO, Tao QING, },
journal={IEICE TRANSACTIONS on Information},
title={Epileptic Seizure Prediction Using Convolutional Neural Networks and Fusion Features on Scalp EEG Signals},
year={2023},
volume={E106-D},
number={5},
pages={821-823},
abstract={Epileptic seizure prediction is an important research topic in the clinical epilepsy treatment, which can provide opportunities to take precautionary measures for epilepsy patients and medical staff. EEG is an commonly used tool for studying brain activity, which records the electrical discharge of brain. Many studies based on machine learning algorithms have been proposed to solve the task using EEG signal. In this study, we propose a novel seizure prediction models based on convolutional neural networks and scalp EEG for a binary classification between preictal and interictal states. The short-time Fourier transform has been used to translate raw EEG signals into STFT sepctrums, which is applied as input of the models. The fusion features have been obtained through the side-output constructions and used to train and test our models. The test results show that our models can achieve comparable results in both sensitivity and FPR upon fusion features. The proposed patient-specific model can be used in seizure prediction system for EEG classification.},
keywords={},
doi={10.1587/transinf.2022DLL0002},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Epileptic Seizure Prediction Using Convolutional Neural Networks and Fusion Features on Scalp EEG Signals
T2 - IEICE TRANSACTIONS on Information
SP - 821
EP - 823
AU - Qixin LAN
AU - Bin YAO
AU - Tao QING
PY - 2023
DO - 10.1587/transinf.2022DLL0002
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2023
AB - Epileptic seizure prediction is an important research topic in the clinical epilepsy treatment, which can provide opportunities to take precautionary measures for epilepsy patients and medical staff. EEG is an commonly used tool for studying brain activity, which records the electrical discharge of brain. Many studies based on machine learning algorithms have been proposed to solve the task using EEG signal. In this study, we propose a novel seizure prediction models based on convolutional neural networks and scalp EEG for a binary classification between preictal and interictal states. The short-time Fourier transform has been used to translate raw EEG signals into STFT sepctrums, which is applied as input of the models. The fusion features have been obtained through the side-output constructions and used to train and test our models. The test results show that our models can achieve comparable results in both sensitivity and FPR upon fusion features. The proposed patient-specific model can be used in seizure prediction system for EEG classification.
ER -
English
