Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals.

Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classificationfrom EEG Signals -- ABSTRACT -- TABLE OF CONTENTS -- LIST OF TABLES -- LIST OF FIGURES -- LIST OF ABBREVIATIONS -- CHAPTER 1: INTRODUCTION -- 1.1 GENESIS OF EEG SIGNALS -- 1.2 EPILEPSY DETECTION AND EEG SIGNALS -- 1.3 EPILEPSY CLASSIFICATION SYSTEM -- 1.4 DATA COLLECTION -- 1.5 FEATURE EXTRACTION -- 1.5.1 ALGORITHM -- 1.6 ORGANISATION OF THE STUDY -- CHAPTER 2: CODE CONVERTER AS A PRE CLASSIFIER FOR CLASSIFICATION OFEPILEPSY RISK LEVEL -- 2.1 INTRODUCTION -- 2.2 EEG SIGNAL PARAMETERS -- 2.3 WAVELET TRANSFORM -- 2.4 THRESHOLDING -- 2.5 METHODOLOGY -- 2.6 SUMMARY -- CHAPTER 3: PSO AND BAYESIAN CLASSIFIER AS POST CLASSIFIER FOR CLASSIFICATION OF EPILEPSY RISK LEVEL -- 3.1 INTRODUCTION -- 3.2 PARTICLE SWARM OPTIMIZATION -- 3.2.1 PSO PARAMETER CONTROL -- 3.2.2 PSO ALGORITHM -- 3.3 HYBRID PSO ALGORITHM -- 3.3.1 STEPS OF HYBRID PSO -- 3.4 BAYESIAN CLASSIFIER -- 3.5 CONCLUSION -- CHAPTER 4: PERFORMANCE ANALYSIS AND DISCUSSION -- 4.1 PERFORMANCE INDEX -- 4.2 QUALITY VALUE -- 4.3 COMPARISON OF OPTIMIZATION RESULTS -- CHAPTER 5: CONCLUSION -- REFERENCES.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.