RT Journal Article
T1 Classification of kinematic and electromyographic signals associated with pathological tremor using machine and deep learning
A1 Pascual Valdunciel, Alejandro
A1 Lopo Martinez, Victor
A1 Beltran Carrero, Alberto J.
A1 Sendra Arranz, Rafael
A1 González Sánchez, Miguel
A1 Perez Sanchez, Javier Ricardo
A1 Grandas Pérez, Francisco Javier
A1 Farina, Dario
A1 Pons, José L.
A1 Oliveira Barroso, Filipe
A1 Gutierrez, Álvaro
AB 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(1) score. The LSTM models achieved 0.98 f(1) 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.
PB MDPI
YR 2023
FD 2023-01-05
LK https://hdl.handle.net/20.500.14352/103528
UL https://hdl.handle.net/20.500.14352/103528
LA eng
NO Pascual-Valdunciel, A.; Lopo-Martínez, V.; Beltrán-Carrero, A.J.; Sendra-Arranz, R.; González-Sánchez, M.; Pérez-Sánchez, J.R.; Grandas, F.; Farina, D.; Pons, J.L.; Oliveira Barroso, F.; et al. Classification of Kinematic and Electromyographic Signals Associated with Pathological Tremor Using Machine and Deep Learning. Entropy 2023, 25, 114.
NO 2023 Descuento MDPI
NO European Commission
NO Ministerio de Ciencia e Innovación (España)
DS Docta Complutense
RD 7 abr 2025