scholarly journals Epileptic Seizure Detection in EEG using Support Vector Machines and Statistical Analysis

2017 ◽  
Vol 9 (2) ◽  
pp. 26-33
Author(s):  
Ahmad M. Sarhan
Keyword(s):  
Statistical Analysis ◽  
Support Vector Machines ◽  
Epileptic Seizure ◽  
Seizure Detection ◽  
Support Vector ◽  
Epileptic Seizure Detection ◽  
Vector Machines

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.

Decision Support with Support Vector Machinesin R & D Project Termination Decision

2011 ◽  
Vol 403-408 ◽  
pp. 4098-4102
Author(s):  
Jing Rong Dong ◽  
Yu Ke Chen
Keyword(s):  
Reinforcement Learning ◽  
Decision Support ◽  
Project Management ◽  
Statistical Analysis ◽  
Support Vector Machines ◽  
Evaluation Criteria ◽  
Support Vector ◽  
Selection Decisions ◽  
Vector Machines ◽  
Termination Decisions

Research and development (R&D) project termination decision is an important and challenging task for organizations with R&D project management .Current research on R&D project management mainly focuses on project selection decisions. Very little research has been done on the termination decision of R&D projects .In this paper a support vector machines classifer for assisting managers in deciding whether to abandon an ongoing R&D project at various stages of R&D is presented. It has also shown by the modeling and pattern recognizing results in terms of termination decisions of fifty R&D projects that the method possesses reinforcement learning properties and universalized capabilities. With respect to modeling and termination decision of R&D project, which has the fact that the evaluation criteria are hardly ever determined by conventional approaches such as statistical analysis, the method is available.

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