A Multimodal Model-Fusion Approach for Improved Prediction of Freezing of Gait in Parkinson's Disease

Freezing of gait (FoG) is a widely observed movement disorder in patients with Parkinson's disease (PD). Its prediction is crucial for effectively giving the cue to avoid FoG occurrence. However, present methods of prediction of FoG are inaccurate for large but practical prediction horizons (PHs). Therefore, this work presents a comprehensive analysis of the electroencephalography (EEG) and inertial measurement units (IMUs) to predict FoG advance in time. An ensemble model consisting of two neural networks (NNs), EEGFoGNet and IMUFoGNet, was developed and tested at different PHs and ensemble weights. Moreover, the model is tested for two practical scenarios: clinical or research applications and personal uses. For clinical or research applications, stratified fivefold cross-validation was used. For personal uses, a transfer learning technique was used for learning user-specific FoG-related features. The model obtained the best accuracy of 92.1% at 1-s PH and the least accuracy of 86.2% at 5-s PH. The presented results are encouraging and show the proposed model's clinical applicability. This study will also help practitioners in comparing the efficacy of different cueing methods.

Automated summary

This study presents a comprehensive analysis of EEG and IMUs to predict freezing of gait in patients with Parkinson's disease. An ensemble model consisting of two neural networks was developed and tested at different time ranges and weights, obtaining high accuracy for both clinical and personal uses.