Empirical enhancement of EEG classification models for epileptic seizures using a fuzzy statistical perspective

Abstract

The signals produced by an electrocardiogram (ECG) are made up of intricate pattern se-quences that have a periodic structure. These pattern sequences contain an initial P-wave, which denotes the beginning of an ECG wave, a QRS sequence, which denotes the intensi-ty of the pulse, and a T segment, which denotes the conclusion of the wave. Characteris-tics such as PR interval, QRS interval, QT interval, ST interval, R to R interval, etc. are employed to recognize chronic, ischemic, and other cardiac illnesses. These wave patterns need the simultaneous execution of many high-complexity signal processing operations to be classified into cardiac disorders. Signal pre-processing, feature extraction, feature se-lection, classification into epileptic and non-epileptic seizures, and post-processing are some of these operations. For each of these operations, researchers create a wide range of algorithms. These algorithms' performance differs significantly in terms of the quantity of leads utilized for ECG collection, filtering effectiveness, feature extraction & selection ef-fectiveness, and classifier effectiveness. Thus, researchers and system designers have be-come unclear when choosing the optimum algorithm set for an application. This text of-fers a thorough analysis & design of fuzzy CNN model of a broad range of epileptic & non-epileptic seizure classification techniques to lessen ambiguity. Convolutional neural network (CNN) based models outperform other models in terms of general-purpose per-formance, whereas application-specific deployments need the employment of customized fuzzy CNN models. A significant area of research & development is presented by the ob-servation that fuzzy logic techniques are not observed while constructing ECG classifica-tion models. Based on these findings, a new fuzzy logic-based classification method is provided in this text that employs quantization techniques to transform input ECG signals into fuzzy values. With these parameters and a specially created CNN model, it was pos-sible to achieve an accuracy of 99.5% for diverse ECG datasets. This accuracy was com-pared to a number of cutting-edge models, and it was observed that the suggested model is quite good at categorizing ECG signals. The suggested technique was observed to be quicker than traditional procedures due to the usage of a fuzzy logic model, enhancing its scalability for a broad range of clinical applications.