The time-frequency synergy network (TFSNet) achieved an average accuracy of 98.68% and 97.14% on two datasets for ASD classification using EEG signals, outperforming existing methods.
Does TFSNet improve the accuracy of ASD classification using EEG signals compared to existing methods?
TFSNet demonstrates high accuracy in classifying Autism Spectrum Disorder using EEG signals, outperforming existing machine learning and deep learning methods.
Autism Spectrum Disorder (ASD) seriously affects social, communication, and behavioral functions, and early accurate diagnosis is crucial to improve the prognosis of patients. Traditional diagnosis methods rely on professional doctors to make subjective diagnosis through scales, the feature extraction of existing machine learning methods is inefficient, and existing deep learning methods have limitations in capturing time-varying features and the joint expression of time-frequency features. To this end, this study proposes a time-frequency synergy network (TFSNet) to improve the accuracy of ASD EEG signal classification. The proposed Dynamic Residual Block (TDRB) was used to enhance time-domain feature extraction; Short-Time Fourier Transform (STFT), convolutional attention mechanism, and transformation technology were combined to capture frequency-domain information; and an adaptive cross-domain attention mechanism (ACDA) was designed to realize efficient fusion of time-frequency features. The experimental results show that the average accuracy of TFSNet on the University of Sheffield (containing 28 ASD patients and 28 healthy controls) and KAU dataset (containing 12 ASD patients and five healthy controls) reaches 98.68%and 97.14%, respectively, yielding significantly better results than the existing machine learning and deep learning methods. In addition, the analysis of model decisions through interpretability analysis techniques enhances its transparency and reliability.
Shi et al. (Wed,) conducted a other in Autism Spectrum Disorder (ASD) (n=73). Time-frequency synergy network (TFSNet) vs. Existing machine learning and deep learning methods was evaluated on Average accuracy of ASD EEG signal classification. The time-frequency synergy network (TFSNet) achieved an average accuracy of 98.68% and 97.14% on two datasets for ASD classification using EEG signals, outperforming existing methods.
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