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Article

Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time–Frequency Spectrogram and Deep Learning Neural Network Features

by 1 and 1,2,*
1
Department of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan 701, Taiwan
2
Medical Device Innovation Center, National Cheng Kung University, Tainan 701, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editors: Dimiter Prodanov and Newton Howard
Brain Sci. 2021, 11(7), 902; https://doi.org/10.3390/brainsci11070902
Received: 16 June 2021 / Revised: 1 July 2021 / Accepted: 5 July 2021 / Published: 8 July 2021
(This article belongs to the Special Issue Neuroinformatics and Signal Processing)
A novel identification algorithm using a deep learning approach was developed in this study to classify neurodegenerative diseases (NDDs) based on the vertical ground reaction force (vGRF) signal. The irregularity of NDD vGRF signals caused by gait abnormalities can indicate different force pattern variations compared to a healthy control (HC). The main purpose of this research is to help physicians in the early detection of NDDs, efficient treatment planning, and monitoring of disease progression. The detection algorithm comprises a preprocessing process, a feature transformation process, and a classification process. In the preprocessing process, the five-minute vertical ground reaction force signal was divided into 10, 30, and 60 s successive time windows. In the feature transformation process, the time–domain vGRF signal was modified into a time–frequency spectrogram using a continuous wavelet transform (CWT). Then, feature enhancement with principal component analysis (PCA) was utilized. Finally, a convolutional neural network, as a deep learning classifier, was employed in the classification process of the proposed detection algorithm and evaluated using leave-one-out cross-validation (LOOCV) and k-fold cross-validation (k-fold CV, k = 5). The proposed detection algorithm can effectively differentiate gait patterns based on a time–frequency spectrogram of a vGRF signal between HC subjects and patients with neurodegenerative diseases. View Full-Text
Keywords: gait analysis; neuro-degenerative diseases; time–frequency spectrogram; deep learning; vertical ground reaction force signal gait analysis; neuro-degenerative diseases; time–frequency spectrogram; deep learning; vertical ground reaction force signal
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MDPI and ACS Style

Setiawan, F.; Lin, C.-W. Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time–Frequency Spectrogram and Deep Learning Neural Network Features. Brain Sci. 2021, 11, 902. https://doi.org/10.3390/brainsci11070902

AMA Style

Setiawan F, Lin C-W. Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time–Frequency Spectrogram and Deep Learning Neural Network Features. Brain Sciences. 2021; 11(7):902. https://doi.org/10.3390/brainsci11070902

Chicago/Turabian Style

Setiawan, Febryan, and Che-Wei Lin. 2021. "Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time–Frequency Spectrogram and Deep Learning Neural Network Features" Brain Sciences 11, no. 7: 902. https://doi.org/10.3390/brainsci11070902

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