Fuzzy Optimization with Modified Adaboost Classifier for Epilepsy Classification from EEG Signals

Rajaguru, Harikumar; Prabhakar, Sunil

doi:10.1007/978-3-319-71767-8_52

Cited by 3 publications

(1 citation statement)

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“…A fuzzy rule-based system was utilized for detecting and visualizing stress during commuter driving by Dobbins and Fairclough [ 28 ]. A fuzzy optimization concept was utilized by Prabhakar and Rajaguru along with Modified Adaboost Classifier for epilepsy classification [ 29 ]. Fuzzy techniques were utilized by Prabhakar and Rajaguru to analyze the cerebral blood flow for epileptic and diabetic patients [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Inspired Photoplethysmography Signal Classification with Bio-Inspired Optimization for Analyzing Cardiovascular Disorders

Prabhakar

Rajaguru²,

Kim

2020

Diagnostics

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The main aim of this paper is to optimize the output of diagnosis of Cardiovascular Disorders (CVD) in Photoplethysmography (PPG) signals by utilizing a fuzzy-based approach with classification. The extracted parameters such as Energy, Variance, Approximate Entropy (ApEn), Mean, Standard Deviation (STD), Skewness, Kurtosis, and Peak Maximum are obtained initially from the PPG signals, and based on these extracted parameters, the fuzzy techniques are incorporated to model the Cardiovascular Disorder(CVD) risk levels from PPG signals. Optimization algorithms such as Differential Search (DS), Shuffled Frog Leaping Algorithm (SFLA), Wolf Search (WS), and Animal Migration Optimization (AMO) are implemented to the fuzzy modeled levels to optimize them further so that the PPG cardiovascular classification can be characterized well. This kind of approach is totally new in PPG signal classification, and the results show that when fuzzy-inspired modeling is implemented with WS optimization and classified with the Radial Basis Function (RBF) classifier, a classification accuracy of 94.79% is obtained for normal cases. When fuzzy-inspired modeling is implemented with AMO and classified with the Support Vector Machine–Radial Basis Function (SVM–RBF) classifier, a classification accuracy of 95.05% is obtained for CVD cases.

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Section: Introductionmentioning

confidence: 99%