Research on Parkinson's Disease Detection Based on Deep Residual Shrinkage Network

Abstract

Parkinson’s disease (Parkinson’s disease, PD) is a common neurodegenerative disorder of the nervous system. Beyond severely compromising patients’ quality of daily life, it also induces non-motor symptoms such as cognitive impairment and depression, thereby imposing a heavy burden on patients’ families and society as a whole. Conventional PD detection methods, however, are constrained by their dependence on manual feature extraction and susceptibility to noisy data. These methods suffer from limitations including relatively low detection accuracy and insufficient feature extraction capability, making them barely able to meet the requirements for precision and stability in clinical diagnosis.To address the limitations of conventional methods, this study proposes a Deep Residual Shrinking Network (Deep Residual Shrinking Network, DRSN) based on pooling fusion for efficient detection of Parkinson’s disease. First, by introducing residual connections into the network structure, the risks of gradient vanishing or gradient exploding caused by excessive network depth are effectively mitigated, ensuring the effective learning of deep-level features. Second, a multi-scale pooling feature fusion strategy is adopted to extract richer local features from Electroencephalogram (Electroencephalogram, EEG) signals. Meanwhile, an attention mechanism is integrated to automatically derive the optimal threshold of the soft-thresholding function, enabling adaptive removal of noise in the signals and enhancing the representational capability of effective features.The proposed method was validated on the Parkinson’s disease neurophysiological activity dataset and the New Mexico dataset. The results demonstrate that the DRSN method achieved an accuracy of 92.10%, precision of 91.27%, recall of 90.72%, and F1-score of 90.99% on the PD neurophysiological activity dataset. On the New Mexico dataset, it further yielded an accuracy of 98.15%, precision of 97.63%, recall of 96.12%, and F1-score of 96.87%. In comparison with traditional methods—including convolutional neural networks (accuracy: 88.00%) and deep residual networks (accuracy: 92.03%)—the proposed method exhibited an accuracy improvement of 10.15 percentage points and 6.12 percentage points, respectively.Leveraging the residual connections that ensure the stability of deep networks, the multi-scale pooling fusion that enables the extraction of rich features, and the attention mechanism-driven adaptive denoising capability, this method demonstrates distinct advantages in Parkinson’s disease detection. It effectively enhances detection accuracy and robustness, thereby providing more reliable technical support for the auxiliary diagnosis of Parkinson’s disease.