Compendium Juxtapose of Algorithmic Ingress to Evaluate Performance of Brain Signals in Seizure Detection
Keywords:
EEG Signal Processing, Seizure Detection, Machine Learning Algorithms, Deep Learning Algorithms, Signal Processing, Preprocessing, Signal Data Analytics.Abstract
Brain signals are imperative for the human body's regular neuronal functioning. Epilepsy is a neural detriment that can cause severe morbidity and paralytic seizures that injure an individual's efficient quotidian operability. The Electroencephalogram (EEG) indicators have been proven to corroborate the disorders in the human body through the signal fluctuations and volatilities that indicate the damages that may likely be persistent or risks that may be triggered in the future. The method of EEG analysis is most successful in neoteric times due to its property of non-invasiveness that aids in increasing traffic amongst users to take up the tests. This paper proposed a novel framework comprising various methods to process the EEG input signal, from preprocessing to predicting seizure availability. Initially, The Linear Filter was employed in the previous system. In terms of accuracy and detecting morbidity, this filtering technique is inefficient. As a result, the distinctive research of Least Square Generative Adversarial Network techniques can be used to denoise the input signal, improving its quality. The Multilevel decomposition method is used to breakdown the input signal to speed up the process and increase the accuracy of signal processing to the next level. The Higuchi Fractal Dimension model is used from the decomposed signal to extract the features and cluster using the K-Means clustering method. Finally, the cluster data is analyzed and classified as seizure and non-seizure using SVM, ANN, CNN, and E-CNN models. These algorithms are implemented, experimented with, and the results are verified. The results are compared with the other algorithms, and it is found that the proposed framework outperforms earlier methods in classification.
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