Adaptive Seizure Onset Detection Framework Using a Hybrid PCA–CSP Approach

Abstract

Epilepsy is one of the most common neurological disorders in the world. Prompt detection of seizure onset from electroencephalogram (EEG) signals can improve the treatment of epileptic patients. This paper presents a new adaptive patient-specific seizure onset detection framework that dynamically selects a feature from enhanced EEG signals to discriminate seizures from normal brain activity. The proposed framework employs principal component analysis and common spatial patterns to enhance the EEG signals and uses the extracted discriminative feature as an input for adaptive distance-based change point detector to identify the seizure onsets. Experimental results from the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) dataset show the computational efficiency of the proposed method (analyzing EEG signals in a time window of 3 s within 0.1 s using a Core i7 PC) while providing comparable results to the existing methods in terms of average sensitivity, latency, and false detection rate. The proposed method is advantageous for real-time monitoring of epileptic patients and could be used to improve early diagnosis and treatment of patients suffering from recurrent seizures.

A Unified Framework and Method for EEG-Based Early Epileptic Seizure Detection and Epilepsy Diagnosis

“…In [49], they first calculated the fuzzy entropy of EEG signals from different states, then a feature selection method has been used, and finally based on the optimal features, the support vector machine (SVM) was employed to make classifications. In [50], they presented a framework that employs principle component analysis and common spatial patterns to enhance the EEG signals and uses the extracted discriminative feature as an input for adaptive distance-based change point detector to make the final decision. And in [51], a novel framework was proposed, the morphological features were extracted based on the local binary pattern operator, and K-nearest neighbor classifier was used for classification.…”

Review of Methods for EEG Signal Classification and Development of New Fuzzy Classification-Based Approach

“…This example shows that the development of approach for automatic EEG signal analysis can be useful in diagnostics because it allows deciding whether a person has epilepsy without the occurrence of epileptic seizure. Investigation of automatic EEG signal is thus useful in the development of decision support systems for early diagnosis of epilepsy [11], [13], [14].…”

“…Data mining methods for signal classification, and EEG signal in particular, include two steps [14]. The first of them is signal preprocessing and the second is classification itself.…”

A framework for seizure detection using effective connectivity, graph theory, and multi-level modular network

“…This work is very time-consuming and error-prone [1]. Therefore, in recent years, automatic seizure detection has attracted a lot of attention and various algorithms are presented based on frequency analysis [2][3][4][5], time analysis [4][5][6], time-frequency analysis [7][8][9][10][11], and nonlinear analysis [12,13].…”