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
收藏
页码：85 / 96
页数：12
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