scholarly journals Epileptic seizure detection from EEG signals using logistic model trees

2016 ◽  
Vol 3 (2) ◽  
pp. 93-100 ◽  
Author(s):  
Enamul Kabir ◽  
Siuly ◽  
Yanchun Zhang
Keyword(s):  
Epileptic Seizure ◽  
Logistic Model ◽  
Seizure Detection ◽  
Eeg Signals ◽  
Model Trees ◽  
Epileptic Seizure Detection

Epileptic seizure detection on EEG signals using machine learning techniques and advanced preprocessing methods

2020 ◽  
Vol 65 (1) ◽  
pp. 33-50 ◽  
Author(s):  
Chahira Mahjoub ◽  
Régine Le Bouquin Jeannès ◽  
Tarek Lajnef ◽  
Abdennaceur Kachouri
Keyword(s):  
Epileptic Seizure ◽  
High Performance ◽  
Visual Analysis ◽  
Binary Classification ◽  
Seizure Detection ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Epileptic Seizure Detection

AbstractElectroencephalography (EEG) is a common tool used for the detection of epileptic seizures. However, the visual analysis of long-term EEG recordings is characterized by its subjectivity, time-consuming procedure and its erroneous detection. Various epileptic seizure detection algorithms have been proposed to deal with such issues. In this study, a novel automatic seizure-detection approach is proposed. Three different strategies are suggested to the user whereby he/she could choose the appropriate one for a given classification problem. Indeed, the feature extraction step, including both linear and nonlinear measures, is performed either directly from the EEG signals, or from the derived sub-bands of tunable-Q wavelet transform (TQWT), or even from the intrinsic mode functions (IMFs) of multivariate empirical mode decomposition (MEMD). The classification procedure is executed using a support vector machine (SVM). The performance of the proposed method is evaluated through a publicly available database from which six binary classification cases are formulated to discriminate between healthy, seizure and non-seizure EEG signals. Our results show high performance in terms of accuracy (ACC), sensitivity (SEN) and specificity (SPE) compared to the state-of-the-art approaches. Thus, the proposed approach for automatic seizure detection can be considered as a valuable alternative to existing methods, able to alleviate the overload of visual analysis and accelerate the seizure detection.

Epileptic Seizure Detection From EEG Signals Using Bagged Ensemble Approach

2020 ◽  
pp. 67-79
Author(s):  
Pradeep Singh ◽  
Sujith Kumar Appikatla
Keyword(s):  
Epileptic Seizure ◽  
Seizure Detection ◽  
Ensemble Classifier ◽  
High Accuracy ◽  
Machine Learning Algorithms ◽  
Eeg Signals ◽  
Epileptic Seizure Detection ◽  
Electrical Pulses ◽  
Two Phases ◽  
The Brain

Seizures are caused by irregular electrical pulses in the brain. Epileptic seizure detection on EEG signals is a long process, which is done manually by epileptologists. The aim of the study is automatically detecting the seizures of the brain, given the electroencephalogram signals by feature extraction and processing through different machine learning algorithms. Machines can be trained to do this type of observation and predict the output with high accuracy. In this chapter, the classification study of individual and ensemble classifier is performed for epileptic seizure detection. The proposed method consists of two phases: extraction of data from EEG signals and development of an individual and ensemble models. Bagging ensemble is developed to achieve better results. The development of the ensemble using various classification algorithms contributes towards increasing the diversity of the ensemble. An extensive comparative study with existing benchmark algorithm is performed for epileptic seizure detection.

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