Classification of Parkinson's Disease Patients-A Deep Learning Strategy

Carvajal-Castano HA, Pérez Toro PA, Orozco Arroyave JR (2022)

Publication Type: Journal article, Original article

Publication year: 2022

Journal

Electronics MDPI

Book Volume: 11

Article Number: 2684

Journal Issue: 17

DOI: 10.3390/electronics11172684

Abstract

(1) Background and objectives: Parkinson's disease (PD) is one of the most prevalent neurodegenerative diseases whose typical symptoms include bradykinesia, abnormal gait and posture, shortened strides, and other movement disorders. In this study, we present a novel framework to evaluate PD gait patterns using state of the art deep learning algorithms. A comparative analysis with three different approaches is presented and evaluated upon three groups of subjects: PD patients, Young Healthy Controls (YHC), and Elderly Healthy Controls (EHC). (2) Methods: The three approaches used in the study include: (i) The energy content of the gait signals in the frequency domain is captured with spectrograms that are used to feed a CNN model, (ii) Temporal information is incorporated by creating GRU networks, (iii) Temporal and spectral information is simultaneously captured by creating a new architecture based on CNNs and GRUs. (3) Results: Accuracies of up to 83.7% and 92.7% are found for the classification between PD vs. EHC and PD vs. YHC, respectively. According to our observations, the proposed approach based on the combination of temporal and spectral information, yields better results than others reported in the state of the art. (4) Conclusions: The results obtained in this study suggest that the combination of temporal and spectral information is more accurate than individual approaches used to classify and evaluate gait patterns in PD patients. To the best of our knowledge, this is the first study in gait analysis where temporal and spectral information is combined in an architecture of deep learning.

Authors with CRIS profile

Paula Andrea Perez Toro Lehrstuhl für Informatik 5 (Mustererkennung) Juan Rafael Orozco Arroyave Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) (LME)

Involved external institutions

Universidad de Antioquía (UDEA) CO Colombia (CO)

How to cite

APA:

Carvajal-Castano, H.A., Pérez Toro, P.A., & Orozco Arroyave, J.R. (2022). Classification of Parkinson's Disease Patients-A Deep Learning Strategy. Electronics, 11(17). https://doi.org/10.3390/electronics11172684

MLA:

Carvajal-Castano, Helber Andres, Paula Andrea Pérez Toro, and Juan Rafael Orozco Arroyave. "Classification of Parkinson's Disease Patients-A Deep Learning Strategy." Electronics 11.17 (2022).

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