Automated Patient-Specific seizure detection system with Self-Parameters adaptation

被引：0

作者：

Ammar S. ^{[1
]}

Trigui O. ^{[2
]}

Senouci M. ^{[3
]}

机构：

[1] Laboratory of Informatics and Mathematics, Ibn Khaldoun University of Tiaret, Tiaret

[2] Advanced Technologies for Medicine and Signals, Sfax University, Sfax

[3] Laboratory of Parallel and Embedded Architectures and Intensive Computing, Oran University Ahmed Ben Bella, Oran

来源：

| 1600年 / Acta Press, Building B6, Suite 101, 2509 Dieppe Avenue S.W., Calgary, AB, T3E 7J9, Canada卷 / 45期

关键词：

Empirical Mode Decomposition; Epilepsy; Genetic Algorithm; Linear Discriminant Analysis; Seizures Detection;

D O I：

10.2316/Journal.201.2017.4.201-2853

中图分类号：

学科分类号：

摘要：

This paper presents a novel fully generic automated patient-specific seizures detection system. The aim is the detection of the seizure epochs with a high precision. For this end, the empirical mode decomposition is used to overcome the system limitations caused by the non-linear and non-stationary characteristics of the electroencephalography (EEG) signal. The genetic algorithm allows selecting the best parameters combination of each patient without the need of any prior information. For instance, it can estimate the relevant features for each subject from the list of 14 features extracted from the intrinsic mode functions. Thus, the proposed system is able to automatically self-adapt to increase its accuracy rate. The experimental results found using the benchmark CHB-MIT scalp long-term EEG database prove the effectiveness and the reliability of the proposed system with an average sensitivity of about 93.4% and an average specificity of about 99.9%.

引用

页码：181 / 191

页数：10

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