State identification of Parkinson's disease based on transfer learning

The local field potential (LFP) signals are a vital signal for studying the mechanisms of deep brain stimulation (DBS) and constructing adaptive DBS containing information related to the motor symptoms of Parkinson's disease (PD).

A Parkinson's disease state identification algorithm based on the feature extraction strategy of transfer learning was proposed.

The algorithm uses continuous wavelet transform (CWT) to convert one-dimensional LFP signals into two-dimensional gray-scalogram images and color images respectively, and designs a Bayesian optimized random forest (RF) classifier to replace the three fully connected layers for the classification task in the VGG16 model, to realize automatic identification of the pathological state of PD patients.

It was found that consistently superior performance of gray-scalogram images over color images. The proposed algorithm achieved an accuracy of 97.76%, precision of 99.01%, recall of 96.47%, and F1-score of 97.73%, outperforming feature extractors such as VGG19, InceptionV3, ResNet50, and the lightweight network MobileNet.

This algorithm has high accuracy and can distinguish the disease states of PD patients without manual feature extraction, effectively assisting the working of doctors.