The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
간질 발작 예측은 임상 간질 치료에서 중요한 연구 주제로, 간질 환자와 의료진에게 예방 조치를 취할 수 있는 기회를 제공할 수 있습니다. EEG는 뇌 활동을 연구하는 데 일반적으로 사용되는 도구로, 뇌의 전기적 방전을 기록합니다. 머신 러닝 알고리즘을 기반으로 한 많은 연구가 EEG 신호를 사용하여 과제를 해결하기 위해 제안되었습니다. 이 연구에서는 발작 전 상태와 발작 간 상태 간의 이진 분류를 위해 합성 신경망과 두피 EEG를 기반으로 하는 새로운 발작 예측 모델을 제안합니다. 단시간 푸리에 변환을 사용하여 원시 EEG 신호를 STFT 격막으로 변환하여 모델의 입력으로 적용했습니다. 융합 특징은 측면 출력 구성을 통해 얻었으며 모델을 훈련하고 테스트하는 데 사용했습니다. 테스트 결과에 따르면 모델은 융합 특징에 따라 민감도와 FPR 모두에서 비슷한 결과를 얻을 수 있습니다. 제안된 환자별 모델은 EEG 분류를 위한 발작 예측 시스템에 사용할 수 있습니다.
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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},}
부
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 -