Classification of Parkinson’s disease severity using gait stance signals in a spatiotemporal deep learning classifier

• Published in: Medical & biological engineering & computing Vol. 62; no. 11; pp. 3493 - 3506
• Main Authors: Muñoz-Mata, Brenda G., Dorantes-Méndez, Guadalupe, Piña-Ramírez, Omar
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Balances (scales); Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Classification systems; Computer Applications; Deep learning; Diagnosis; disease severity; Gait; Human Physiology; Imaging; Machine learning; Motor skill learning; Movement disorders; Nervous system; nervous system diseases; Neural networks; Neurodegenerative diseases; Original Article; Parkinson's disease; Radiology; Signal classification; Vertical forces; Stance gait cycle; Convolutional long short-term deep neural network; Movement disorders; Vertical ground reaction force
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-024-03148-2

Parkinson’s disease (PD) is a degenerative nervous system disorder involving motor disturbances. Motor alterations affect the gait according to the progression of PD and can be used by experts in movement disorders to rate the severity of the disease. However, this rating depends on the expertise of the clinical specialist. Therefore, the diagnosis may be inaccurate, particularly in the early stages of PD where abnormal gait patterns can result from normal aging or other medical conditions. Consequently, several classification systems have been developed to enhance PD diagnosis. In this paper, a PD gait severity classification algorithm was developed using vertical ground reaction force (VGRF) signals. The VGRF records used are from a public database that includes 93 PD patients and 72 healthy controls adults. The work presented here focuses on modeling each foot’s gait stance phase signals using a modified convolutional long deep neural network (CLDNN) architecture. Subsequently, the results of each model are combined to predict PD severity. The classifier performance was evaluated using ten-fold cross-validation. The best-weighted accuracies obtained were 99.296(0.128)% and 99.343(0.182)%, with the Hoehn-Yahr and UPDRS scales, respectively, outperforming previous results presented in the literature. The classifier proposed here can effectively differentiate gait patterns of different PD severity levels based on gait signals of the stance phase. Graphical abstract