Abstract
Parkinson’s disease (PD) is a progressive condition that affects dopaminergic neurons, causing motor alterations. Motor disturbances, such as gait impairment, can be used to assess the disease. Unfortunately, gait disturbances, such as decreased walking speed and step variability, can also occur due to aging, affecting the identification of abnormal PD gait. Therefore, developing an adequate tool to evaluate PD patients’ gait is essential. This paper proposes a deep learning algorithm to differentiate between PD gaits and normal walking using vertical ground reaction force (VGRF) signals. CLDNN is a single framework composed of a convolutional neural network, a long-short term memory network, and a deep neural network. To train and validate a CLDNN classifier gait cycles were obtained from VGRF signals. The VGRF signals were from a public database with recordings from 93 PD patients and 73 healthy adult controls. The CLDNN performance was evaluated by five-fold cross-validation. The combined spatial and temporal methods in CLDNN enabled the effective identification of PD gait with less complex architecture. The best weighted accuracy was 98.28 ± 0.38. Thus, our model is compact and efficient for future embedded or portable implementations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
El Maachi, I., Bilodeau, G.A., Bouachir, W.: Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait. Expert Syst. Appl. 143, 113,075 (2020). https://doi.org/10.1016/j.eswa.2019.113075
Goldberger, A.L., et al.: Physiobank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000). https://doi.org/10.1161/01.CIR.101.23.e215
Hoang, N.S., Cai, Y., Lee, C.W., Yang, Y.O., Chui, C.K., Chua, M.C.H.: Gait classification for Parkinson’s disease using stacked 2D and 1D convolutional neural network. In: 2019 International Conference on Advanced Technologies for Communications (ATC), pp. 44–49. IEEE (2019). 10.1109/ATC.2019.8924567
Jankovic, J.: Parkinson’s disease: clinical features and diagnosis. J. Neurol. Neurosurg. Psychiatry 79(4), 368–376 (2008). https://doi.org/10.1136/jnnp.2007.131045
Prabhu, P., Pradhan, N.: Recurrence quantification analysis of human gait in neurological movement disorders. IJERT 5(03), 447–452 (2016)
Reeve, A., Simcox, E., Turnbull, D.: Ageing and Parkinson’s disease: why is advancing age the biggest risk factor? Ageing Res. Rev. 14, 19–30 (2014). https://doi.org/10.1016/j.arr.2014.01.004
Sainath, T.N., Vinyals, O., Senior, A., Sak, H.: Convolutional, long short-term memory, fully connected deep neural networks. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4580–4584. IEEE (2015). https://doi.org/10.1109/ICASSP.2015.7178838
Standaert, D.G., Saint-Hilaire, M.H., Thomas, C.A.: Parkinson’s Disease Handbook. American Parkinson’s Disease Association, Staten Island (2019)
Tysnes, O.-B., Storstein, A.: Epidemiology of Parkinson’s disease. J. Neural Transm. 124(8), 901–905 (2017). https://doi.org/10.1007/s00702-017-1686-y
Veeraragavan, S., Gopalai, A.A., Gouwanda, D., Ahmad, S.A.: Parkinson’s disease diagnosis and severity assessment using ground reaction forces and neural networks. Front. Physiol. 11, 587,057 (2020). https://doi.org/10.3389/fphys.2020.587057
Xia, Y., Yao, Z., Ye, Q., Cheng, N.: A dual-modal attention-enhanced deep learning network for quantification of Parkinson’s disease characteristics. IEEE Trans. Neural Syst. Rehabil. Eng. 28(1), 42–51 (2019). https://doi.org/10.1109/TNSRE.2019.2946194
Zeng, W., Yuan, C., Wang, Q., Liu, F., Wang, Y.: Classification of gait patterns between patients with Parkinson’s disease and healthy controls using phase space reconstruction (PSR), empirical mode decomposition (EMD) and neural networks. Neural Netw. 111, 64–76 (2019). https://doi.org/10.1016/j.neunet.2018.12.012
Zhao, A., Qi, L., Li, J., Dong, J., Yu, H.: A hybrid spatio-temporal model for detection and severity rating of Parkinson’s disease from gait data. Neurocomputing 315, 1–8 (2018). https://doi.org/10.1016/j.neucom.2018.03.032
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Muñoz-Mata, B.G., Dorantes-Méndez, G., Piña-Ramírez, O. (2023). Stacked Spatial and Temporal Deep Learning Methods for Identification of Parkinson’s Disease Using Gait Signals. In: Trujillo-Romero, C.J., et al. XLV Mexican Conference on Biomedical Engineering. CNIB 2022. IFMBE Proceedings, vol 86. Springer, Cham. https://doi.org/10.1007/978-3-031-18256-3_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-18256-3_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-18255-6
Online ISBN: 978-3-031-18256-3
eBook Packages: EngineeringEngineering (R0)