Diagnosing autism in children using nonlinear dynamics of EEG signals and fuzzy extreme learning machines with...

Autism is a complex psychiatric condition that needs to be diagnosed early using more objective techniques. Hence, many researchers have turned to diagnosing autism by analyzing EEG signals. However, a comprehensive framework for this has not yet been introduced, and there is room for improvement. In this study, to increase the precision of autism diagnosis from EEG signals, a new framework is introduced that includes the steps of data preprocessing, extraction of nonlinear features from EEG time series, optimization of extracted features using an innovative technique based on GA and DBSCAN algorithms, feature reduction using the LASSO technique, and classification using a fuzzy ELM classifier. This study presents a feature optimization technique that leverages a genetic algorithm informed by clustering principles. Rather than relying on random selection for forming the new generation, the approach incorporates clustering during the fitness evaluation phase to identify and exclude outliers from advancing to the next generation. The recommended scheme was examined on two EEG databases. Using only 14-channel EEG data, it was able to achieve 96.81% accuracy, 95.16% sensitivity, 97.73% specificity, and a 96.42% F1-score for autism detection using database A, as well as 97.64% accuracy, 96.55% sensitivity, 98.49% specificity, and a 97.51% F1-score using database B. This framework outperformed existing methods on two EEG databases. The practical use of low-channel systems suggests potential for real-world clinical deployment, enabling scalable and cost-effective screening. This study underscores the potential of using nonlinear dynamics of EEG signals alongside fuzzy ELM for diagnosing autism in children.