Recognition of Parkinson's Disease Based on Residual Neural Network and Voice Diagnosis

In order to reduce the clinical diagnosis of Parkinson's disease on the scale and wearable equipment and doctors' clinical experience of excessive dependence, provide new ideas for PD patients in the diagnosis method. In this paper, signal processing method is used to extract 12 complex speech features from MDVR-KCL dataset, including periodic change, peak change and harmonic signal-to-noise ratio. Traditional decision tree and residual neural network are used for training and testing. Through comparative experiments, it is found that residual neural network, which can effectively solve the problem of neural network deepening and accuracy decreasing, can effectively distinguish PD patients and healthy people, and the accuracy rate is up to 97.3%.