Intelligent differentiation between Parkinson's disease and essential tremor using wearable sensors and machine...

Parkinson's disease (PD) and essential tremor (ET) are prevalent movement disorders with overlapping clinical presentations but divergent progression and treatment, underscoring the importance of accurate differential diagnosis. Current diagnosis mainly depends on neurologists' clinical rating scales, which is subjective, time-consuming and prone to high misdiagnosis rates. This study integrates wearable sensor technology with machine learning methodologies to facilitate more objective and precise discrimination between PD and ET.

Using standardized upper-limb motor tasks and wearable nine-axis inertial measurement units (IMU), kinematic data were collected from a total of 118 participants (93 in the development dataset and 25 in the temporally independent validation dataset) during task execution. After multi-dimensional feature extraction, feature selection and model training were performed within a 5 × 5 nested cross-validation framework. Temporal validation was conducted to simulate real-world clinical consultations and evaluate model generalizability over time. SHapley Additive exPlanations (SHAP) was used for model interpretation.

The XGBoost model with integrated tremor features outperformed all other tested models. In temporal validation, it achieved patient-level 88.00% accuracy (AUC = 0.8635) for resting tremor task and 92.00% accuracy (AUC = 0.9035) for postural tremor task, respectively. Patient-level SHAP analysis identified acceleration peak frequency and inter-axis correlation as core discriminative features consistent with clinical consensus, demonstrating favorable preliminary generalizability.

This wearable-based machine learning approach with temporal validation shows encouraging performance and good interpretability in differentiating PD and ET. It is a promising single-center temporal validation study requiring further patient-level prospective multicenter validation.