Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/466960
Title: Prediction of epileptic seizure using regression model
Researcher: Ganapriya, K
Guide(s): Uma Maheswari, N
Keywords: Engineering and Technology
Computer Science
Computer Science Information Systems
Deep Learning
Seizure Detection
Genetic Algorithm
University: Anna University
Completed Date: 2022
Abstract: Epilepsy seizure detection by means of machine learning and deep newlinelearning models is the emerging research field. There are certain works that newlineemploys machine learning algorithm for seizure detection. Thus the first phase newlineof this work concentrates on using the hyper parameter tuning to achieve better newlineresults. For enabling the best classifier, nine different classifiers have been newlineexperimented and compared with one another in terms of accuracy and other newlineperformance parameters. The classifiers experimented with are K-Nearest newlineNeighbor, Decision Tree, Naïve Bayes, Random forest, Stochastic Gradient newlineDescent, Gradient Boosting, XGBoost, Extra Tree Classifier, and Logistic newlineregression.The best performing machine learning classifier is identified and fine newlinetune for increasing the performance. From the obtained results, it is noticed that newlinethe Extra Tree classifier performs better than the other models. The accuracy newlineobtained with it is 96.5%. Modification in several features and Hyperparameter newlinetuning is done to increase the classifier performance using a genetic algorithm newlineapproach. When experimented with the Extra tree classifier s max features newlineproperty, the best accuracy obtained is 98%. The performance of the model increases considerably when the genetic algorithm based AutoML approach is used for hyperparameter tuning. In addition to this a convolutional neural network model is also designed for newlineseizure detection and the results are compared. The CNN model is found to newlineperform better than the extra tree classifier and the result obtained is 99.6%, newlinewhich is higher than the other state of art models.Prediction of occurrence of a seizure would be of greater help to make necessary precaution for taking care of the patient. A Deep learning model, Recurrent Neural Network (RNN), is designed for predicting the upcoming newlinevalues in the EEG values. A deep data analysis is made to find the parameter that newlinecould best differentiate the normal values and seizure values. newline newline
Pagination: xvi,114p.
URI: http://hdl.handle.net/10603/466960
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File27.56 kBAdobe PDFView/Open
02_prelim pages.pdf947.82 kBAdobe PDFView/Open
03_content.pdf7.66 kBAdobe PDFView/Open
04_abstract.pdf4.4 kBAdobe PDFView/Open
05_chapter 1.pdf451.03 kBAdobe PDFView/Open
06_chapter 2.pdf279.59 kBAdobe PDFView/Open
07_chapter 3.pdf190.18 kBAdobe PDFView/Open
08_chapter 4.pdf159.63 kBAdobe PDFView/Open
09_chapter 5.pdf918.92 kBAdobe PDFView/Open
10_chapte r6.pdf236.52 kBAdobe PDFView/Open
11_annexures.pdf135.72 kBAdobe PDFView/Open
80_recommendation.pdf99.08 kBAdobe PDFView/Open
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