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

• Published in: Brain sciences Vol. 11; no. 7; p. 902
• Main Authors: Setiawan, Febryan, Lin, Che-Wei
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08.07.2021; MDPI
• Subjects: Algorithms; Amyotrophic lateral sclerosis; Classification; Deep learning; Discriminant analysis; Gait; gait analysis; Genetic transformation; Motor neurone disease; Nervous system; Neural networks; neuro-degenerative diseases; Neurodegenerative diseases; Parkinson's disease; Patients; Principal components analysis; Sensors; Support vector machines; Time series; time–frequency spectrogram; vertical ground reaction force signal; Wavelet transforms; neuro-degenerative diseases; deep learning; vertical ground reaction force signal; time–frequency spectrogram; gait analysis
• ISSN: 2076-3425; 2076-3425
• DOI: 10.3390/brainsci11070902

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.