Chronological sewing training optimization enabled deep learning for autism spectrum disorder using EEG signal

Autism spectrum disorder (ASD) is a disorder in neurological growth, which includes cognitive and behavioral impairment and it starts from infancy. However, the reason for ASD is still vague and no effective medical ways are used for its discovery. Earlier discovery of ASD is extensively beneficial for children’s health sustainability. The classical detection models rely on expertise analysis which tends to be expensive and inaccurate. Thus, this paper presents an effectual autism diagnostic model with electroencephalogram (EEG) signals that are produced through the electrical activities of the brain for detecting ASD. The Gaussian filter is employed to abandon the noise. Various statistical features signal and spectral-based features are extracted and provided to DRN for enhanced efficiency. The ASD detection is undergone using Chronological Sewing Training Optimization-Deep Residual Network (CSTO-DRN) wherein DRN is pre-trained using CSTO algorithm by tuning finest weights. The CSTO is built by incorporating the Chronological concept with Sewing Training-Based Optimization (STBO). The CSTO-DRN provided finest accuracy of 88.6%, Negative Predictive Value (NPV) of 87.8%, Positive Predictive Value (PPV) of 89.4%, True negative rate (TNR) of 85%, True positive rate (TPR) of 88.9%, and F-Measure of 87.5%. Its execution can enhance the efficiency of detection and minimize cost and human intervention.