A novel variational mode decomposition based convolutional neural network for the identification of freezing of gait intervals for patients with Parkinson's disease

Abstract

Freezing of gait (FoG) is a debilitating and serious motor system complication of Parkinson's disease (PD) that may expose patients to frequent falls and life-threating injuries. Several artificial and machine learning methods have been proposed for the prediction of FoG based upon a limited time-duration of sensory data, However, most of the related work has been insufficiently trained and tested on smaller datasets compromising the generalizability of the models. Further, the proposed models provided a prediction at a lower rate (e.g., every 7.8 s). In response to the above shortcomings, we propose a novel variational mode decomposition (VMD) based deep learning that is capable of efficiently inferring the occurrence of FoG at a higher time-resolution (i.e., every sampling period of 7.8 ms) and with a subject-independent accuracy up to 98.8 % outperforming the state-of-the-art architectures and the standard LSTM models. The proposed model will enable the prompt detection of FoG episodes and support PD sufferers reducing the likelihood of falls.