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Pascual-Valdunciel A, Lopo-Martínez V, Beltrán-Carrero AJ, Sendra-Arranz R, González-Sánchez M, Pérez-Sánchez JR, Grandas F, Farina D, Pons JL, Oliveira Barroso F, Gutiérrez Á. Classification of Kinematic and Electromyographic Signals Associated with Pathological Tremor Using Machine and Deep Learning. Entropy (Basel) 2023; 25:114. [PMID: 36673255 PMCID: PMC9858124 DOI: 10.3390/e25010114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 12/23/2022] [Accepted: 12/30/2022] [Indexed: 06/17/2023]
Abstract
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 f1 score. The LSTM models achieved 0.98 f1 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.
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Affiliation(s)
- Alejandro Pascual-Valdunciel
- E.T.S. Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, 28040 Madrid, Spain
- Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council (CSIC), 28002 Madrid, Spain
- Department of Bioengineering, Imperial College London, London SW7 2AZ, UK
| | - Víctor Lopo-Martínez
- E.T.S. Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | | | - Rafael Sendra-Arranz
- E.T.S. Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Miguel González-Sánchez
- Movement Disorders Unit, Department of Neurology, Hospital General Universitario Gregorio Marañón, 28007 Madrid, Spain
| | - Javier Ricardo Pérez-Sánchez
- Movement Disorders Unit, Department of Neurology, Hospital General Universitario Gregorio Marañón, 28007 Madrid, Spain
| | - Francisco Grandas
- Movement Disorders Unit, Department of Neurology, Hospital General Universitario Gregorio Marañón, 28007 Madrid, Spain
- Department of Medicine, Universidad Complutense, 28040 Madrid, Spain
| | - Dario Farina
- Department of Bioengineering, Imperial College London, London SW7 2AZ, UK
| | - José L. Pons
- Legs & Walking AbilityLab, Shirley Ryan AbilityLab, Chicago, IL 60611, USA
- Department of PM&R, Feinberg School of Medicine, Northwestern University, Evanston, IL 60208, USA
- Department of Biomedical Engineering and Mechanical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL 60208, USA
| | - Filipe Oliveira Barroso
- Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council (CSIC), 28002 Madrid, Spain
| | - Álvaro Gutiérrez
- E.T.S. Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, 28040 Madrid, Spain
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