Study on the recognition of early-stage Parkinson’s disease patients using functional near-infrared spectroscopy signals based on machine learning

Objective This study aims to investigate the feasibility of diagnosing early-stage Parkinson’s disease (PD) patients by combining functional near-infrared spectroscopy (fNIRS) signals with machine learning algorithms. Methods Sixty PD patients as well as 60 healthy controls, diagnosed between December 2021 and August 2023 at Beijing Rehabilitation Hospital, Capital Medical University, were consecutively enrolled in this study. The ETG-4000 near-infrared brain imaging system with 22 channels (CH) was used to record changes in oxyhemoglobin and deoxyhemoglobin concentrations in the prefrontal cortex of the subjects. A general linear model was applied to calculate the activation degree (β value) for each channel. Four machine learning diagnostic models were developed: support vector machine (SVM), back-propagation (BP) neural network, random forest, and logistic regression models. The performance of the four diagnostic models was evaluated based on accuracy, sensitivity, specificity, and the area under the Receiver Operating Characteristic (ROC) curve. Additionally, SHapley Additive exPlanations (SHAP) analysis was applied to improve the interpretability of the optimal model. SHAP values for each channel were calculated, and the weighted average of the SHAP values from different channels was summarized. By combining this with the brain region distribution, the contribution of different brain regions to the model’s classification task was obtained. Results The accuracy of the four diagnostic models ranged from 81% to 90%, sensitivity from 69% to 89%, specificity from 93% to 100%, and the area under the ROC curve from 0.90 to 0.98. The SVM model outperformed the others, achieving an area under the ROC curve of 0.96, accuracy of 90%, sensitivity of 89%, and specificity of 93%. SHAP analysis revealed that the four channels contributing most to the SVM model were CH08, CH05, CH01, and CH13, with the right frontopolar cortex (FPC) region contributing the largest share (36.5% of the total). Conclusions The model based on fNIRS signals and the SVM algorithm shows great diagnostic advantages in diagnosing early-stage PD patients, with sensitivity (89%) and specificity (93%) exceeding those of most existing methods. Future research should focus on the fNIRS signal characteristics of the right frontopolar cortex and dorsolateral prefrontal cortex regions to further improve the performance of the diagnostic model.