Classification of Kinematic and Electromyographic Signals Associated with Pathological Tremor Using Machine and Deep Learning

Peripheral Electrical Stimulation (PES) of afferent pathways has received increased interest as a solution to reduce pathological tremors with minimal side effects. Closed-loop PES systems might present some advantages in reducing tremors, but further developments are required in order to reliably detect pathological tremors to accurately enable the stimulation only if a tremor is present. This study explores different machine learning (K-Nearest Neighbors, Random Forest and Support Vector Machines) and deep learning (Long Short-Term Memory neural networks) models in order to provide a binary (Tremor; No Tremor) classification of kinematic (angle displacement) and electromyography (EMG) signals recorded from patients diagnosed with essential tremors and healthy subjects. Three types of signal sequences without any feature extraction were used as inputs for the classifiers: kinematics (wrist flexion–extension angle), raw EMG and EMG envelopes from wrist flexor and extensor muscles. All the models showed high classification scores (Tremor vs. No Tremor) for the different input data modalities, ranging from 0.8 to 0.99 for the f score. The LSTM models achieved 0.98 f scores for the classification of raw EMG signals, showing high potential to detect tremors without any processed features or preliminary information. These models may be explored in real-time closed-loop PES strategies to detect tremors and enable stimulation with minimal signal processing steps.This study was funded by the European Union’s Horizon2020 research and innovation program (Project EXTEND—Bidirectional Hyper-Connected Neural System) under grant agreement No 779982.This work was also funded by the Spanish Ministry of Science and Innovation (Project NETremor: Development of a digital platform for the remote data management of patients with movement disorders), Project TED2021-130174B-C32, funded by MCIN/AEI/10.13039/501100011033 and the European Union Next Generation EU/PRTR. This work was also partially funded by the Spanish MCIN/AEI/10.13039/501100011033 and by the “European Union Next Generation EU/PRTR” under Grant agreement IJC2020-044467-I