Does CortiMoS-Net with S3C-Tune optimization improve the accuracy of Parkinson's disease diagnosis from EEG signals compared to state-of-the-art deep learning approaches?
The CortiMoS-Net model with S3C-Tune optimization demonstrates highly accurate classification of Parkinson's disease from EEG signals, outperforming existing deep learning approaches.
Parkinson's disease (PD) is a neurodegenerative disease that causes extensive impacts on cognitive and motor function, hence making correct and early diagnosis essential for efficient clinical management and enhancement of the quality of life. Analysis using EEG has now become a safe and possible method for the identification of neural abnormalities in PD. Nevertheless, current models must struggle with several constraints: high false detection rates, poor generalizability across subjects, sensitivity to EEG noise pollution, and the inability to extract deep cortical representations, which have the capability to distinguish between healthy and Parkinson patterns. To alleviate these issues, the current paper proposes a novel CortiMoS-Net (Cortical Modeling with Stacked Autoencoder and MobileNet) capable of accurately detecting Parkinson's disease from EEG signals. CortiMoS-Net architecture combines deep stacked autoencoders with low-computation MobileNet convolution blocks such that low-complexity learning of complex cortical activity patterns is supplemented with computational scalability. To achieve further enhanced model convergence and optimization of learnable parameters, the present work also proposes an enhanced hybrid optimization technique named Snow Shepherd Stride Configuration Tuning (S3C-Tune). The proposed pipeline is initiated with raw EEG signal recording, preprocessing, and peak picking for the intent of artifact removal and detection of neurologically intriguing events. Model parameters are tuned by the S3C-Tune algorithm to realize maximal training accuracy. Such a pipeline hybrid enables extensive cortical modeling as well as efficient optimization and results in correct PD vs. healthy subject classification. Experimental results confirm the effectiveness of the suggested approach with better accuracy, precision, recall, and F1-score of 0.99 and minimum error rate and minimum loss of 0.01 and 0.05, respectively. The suggested model also indicates maximum prediction correctness of 0.99 and mean efficiency measure of 0.95 as compared to a large number of state-of-the-art hybrid deep learning approaches.
Kolla et al. (Fri,) studied this question.