A Convolutional Gated Recurrent Neural Network for Epileptic Seizure Prediction

被引：26

作者：

Affes, Abir ^{[1
]}

Mdhaffar, Afef ^{[1
,2
]}

Triki, Chahnez ^{[3
]}

Jmaiel, Mohamed ^{[1
,2
]}

Freisleben, Bernd ^{[4
]}

机构：

[1] Univ Sfax, ReDCAD Lab, ENIS, BP 1173, Sfax, Tunisia

[2] Digital Res Ctr Sfax, Sfax 3021, Tunisia

[3] Hosp Hedi Chaker, Dept Child Neurol, Sfax 3029, Tunisia

[4] Philipps Univ Marburg, Dept Math & Comp Sci, Marburg, Germany

来源：

HOW AI IMPACTS URBAN LIVING AND PUBLIC HEALTH, ICOST 2019 | 2019年 / 11862卷

关键词：

Epilepsy; Elecroencephalogram; Spectrogram; STFT; CNN; GRU; Seizure prediction; INTERNATIONAL-LEAGUE; ILAE;

D O I：

10.1007/978-3-030-32785-9_8

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we present a convolutional gated recurrent neural network (CGRNN) to predict epileptic seizures based on features extracted from EEG data that represent the temporal aspect and the frequency aspect of the signal. Using a dataset collected in the Children's Hospital of Boston, CGRNN can predict epileptic seizures between 35 min and 5 min in advance. Our experimental results indicate that the performance of CGRNN varies between patients. We achieve an average sensitivity of 89% and a mean accuracy of 75.6% for the patients in the data set, with a mean False Positive Rate (FPR) of 1.6 per hour.

引用

页码：85 / 96

页数：12

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