Performance Evaluation of Neural Network Models for Autism Detection Using EEG Data
DOI:
https://doi.org/10.46604/aiti.2024.13951Keywords:
Autism, detection, EEG, machine learning, neural networkAbstract
This study aims to leverage a promising avenue for the precise and early detection of Autism. Autism is a multifaceted neurodevelopmental condition marked by challenges in social interaction, communication, and repetitive behaviors. Traditional diagnosis relies on time-consuming behavioral assessments, necessitating reliable and non-intrusive biomarkers for early and accurate detection. This paper analyzes eleven linear and non-linear features across time and frequency domains from an EEG dataset. Four neural network models, such as convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTM), and a custom neural network are employed for classification. The CNN achieves the lowest accuracy at 89.02%, while the custom neural network reaches the highest accuracy at 94.02%, and the DNN and LSTM achieve 91.98% and 93.83% accuracy, respectively. Other metrics such as precision, recall, specificity, and F1-score, are also evaluated. This research underscores the efficacy of neural network in detecting Autism, advancing diagnostic tools.
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