Epileptic Seizure Prediction Using Deep Transformer Model

被引:55
作者
Bhattacharya, Abhijeet [1 ]
Baweja, Tanmay [1 ]
Karri, S. P. K. [2 ]
机构
[1] Bharati Vidyapeeths Coll Engn, Elect & Elect Engn, A-4 Block,Baba Ramdev Marg, New Delhi 110063, India
[2] Natl Inst Technol, Dept Elect Engn, Tadepalligudem 534101, Andhra Pradesh, India
关键词
Machine learning; deep learning; seizure prediction; epilepsy; transformer model; intracranial electroencephalogram; scalp electroencephalogram; EEG;
D O I
10.1142/S0129065721500581
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The electroencephalogram (EEG) is the most promising and efficient technique to study epilepsy and record all the electrical activity going in our brain. Automated screening of epilepsy through data-driven algorithms reduces the manual workload of doctors to diagnose epilepsy. New algorithms are biased either towards signal processing or deep learning, which holds subjective advantages and disadvantages. The proposed pipeline is an end-to-end automated seizure prediction framework with a Fourier transform feature extraction and deep learning-based transformer model, a blend of signal processing and deep learning - this imbibes the potential features to automatically identify the attentive regions in EEG signals for effective screening. The proposed pipeline has demonstrated superior performance on the benchmark dataset with average sensitivity and false-positive rate per hour (FPR/h) as 98.46%, 94.83% and 0.12439, 0, respectively. The proposed work shows great results on the benchmark datasets and a big potential for clinics as a support system with medical experts monitoring the patients.
引用
收藏
页数:16
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