Real-Time Epileptic Seizure Prediction Using AR Models and Support Vector Machines

2010 ◽  
Vol 57 (5) ◽  
pp. 1124-1132 ◽  
Author(s):  
Luigi Chisci ◽  
Antonio Mavino ◽  
Guido Perferi ◽  
Marco Sciandrone ◽  
Carmelo Anile ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Real Time ◽  
Epileptic Seizure ◽  
Support Vector ◽  
Seizure Prediction ◽  
Vector Machines ◽  
Epileptic Seizure Prediction ◽  
Ar Models

scholarly journals Epileptic Seizure Prediction over EEG Data using Hybrid CNN-SVM Model with Edge Computing Services

2018 ◽  
Vol 210 ◽  
pp. 03016 ◽  
Author(s):  
Punjal Agarwal ◽  
Hwang-Cheng Wang ◽  
Kathiravan Srinivasan
Keyword(s):  
Epileptic Seizure ◽  
Life Support ◽  
Real Life ◽  
Prediction Method ◽  
Edge Computing ◽  
Support Vector ◽  
Seizure Prediction ◽  
Computing Services ◽  
Svm Model ◽  
Epileptic Seizure Prediction

Epilepsy is one of the most common neurological disorders, which is characterized by unpredictable brain seizure. About 30% of the patients are not even aware that they have epilepsy and many have to undergo surgeries to relieve the pain. Therefore, developing a robust brain-computer interface for seizure prediction can help epileptic patients significantly. In this paper, we propose a hybrid CNN-SVM model for better epileptic seizure prediction. A convolutional neural network (CNN) consists of a multilayer structure, which can be adapted and modified according to the requirement of different applications. A support vector machine is a discriminative classifier which can be described by a separating optimal hyperplane used for categorizing new samples. The combination of CNN and SVM is found to provide an effective way for epileptic prediction. Furthermore, the resulting model is made autonomous using edge computing services and is shown to be a viable seizure prediction method. The results can be beneficial in real-life support of epilepsy patients.

scholarly journals A Novel Permutation Entropy-Based EEG Channel Selection for Improving Epileptic Seizure Prediction

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7972
Author(s):  
Jee S. Ra ◽  
Tianning Li ◽  
Yan Li
Keyword(s):  
Sensitivity And Specificity ◽  
Epileptic Seizure ◽  
Epileptic Seizures ◽  
Optimization Method ◽  
Channel Selection ◽  
Permutation Entropy ◽  
Patient Specific ◽  
Support Vector ◽  
Seizure Prediction ◽  
Epileptic Seizure Prediction

The key research aspects of detecting and predicting epileptic seizures using electroencephalography (EEG) signals are feature extraction and classification. This paper aims to develop a highly effective and accurate algorithm for seizure prediction. Efficient channel selection could be one of the solutions as it can decrease the computational loading significantly. In this research, we present a patient-specific optimization method for EEG channel selection based on permutation entropy (PE) values, employing K nearest neighbors (KNNs) combined with a genetic algorithm (GA) for epileptic seizure prediction. The classifier is the well-known support vector machine (SVM), and the CHB-MIT Scalp EEG Database is used in this research. The classification results from 22 patients using the channels selected to the patient show a high prediction rate (average 92.42%) compared to the SVM testing results with all channels (71.13%). On average, the accuracy, sensitivity, and specificity with selected channels are improved by 10.58%, 23.57%, and 5.56%, respectively. In addition, four patient cases validate over 90% accuracy, sensitivity, and specificity rates with just a few selected channels. The corresponding standard deviations are also smaller than those used by all channels, demonstrating that tailored channels are a robust way to optimize the seizure prediction.

Seizure prediction with spectral power of EEG using cost-sensitive support vector machines

Epilepsia ◽  
2011 ◽  
Vol 52 (10) ◽  
pp. 1761-1770 ◽  
Author(s):  
Yun Park ◽  
Lan Luo ◽  
Keshab K. Parhi ◽  
Theoden Netoff
Keyword(s):  
Support Vector Machines ◽  
Spectral Power ◽  
Support Vector ◽  
Seizure Prediction ◽  
Vector Machines
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