Please use this identifier to cite or link to this item:
Title: Improving deep learning parkinsons disease detection through data augmentation training
Authors: Taleb, Catherine
Likforman-Sulem, Laurence
Mokbel, Chafic 
Affiliations: Department of Electrical Engineering 
Keywords: PDMultiMC dataset
Parkinsons disease (PD)
Data augmentation
Transfer learning
Subjects: Handwriting
Issue Date: 2020
Part of: Communications in Computer and Information Science (CCIS)
Volume: 1144
Start page: 79
End page: 93
Conference: Mediterranean Conference on Pattern Recognition and Artificial Intelligence (3rd : 22-23 Dec, 2019 : Istanbul, Turkey) 
Deep learning has been successfully applied to different classification applications where large data are available. However, the lack of data makes it more difficult to predict Parkinsons disease (PD) with the deep models, which requires enough number of training data. Online handwriting dynamic signals can provide more detailed and complex information for PD detection task. In our previous work [1], two different deep models were studied for time series classification; the convolutional neural network (CNN) and the convolutional neural network- bidirectional long short term memory network (CNN-BLSTM). Different approaches were applied to encode pen-based signals into images for the CNN model while the raw time series are used directly with the CNN-BLSTM model. We have showed that both CNN model with spectrogram images as input and CNN-BLSTM model, improve the performance of time series classification applied for early PD stage detection. However, these approaches did not outperform classical support vector machine (SVM) classification applied on pre-engineered features. In this paper we investigate transfer learning and data augmentation approaches in order to train these models for PD detection on large-scale data. Various data augmentation methods for pen-based signals are proposed. Our experimental results show that the CNN-BLSTM model used with the combination of Jittering and Synthetic data augmentation methods provides promising results in the context of early PD detection, with accuracy reaching 97.62%. We have illustrated that deep architecture can surpass the models trained on pre-engineered features even though the available data is small.
Ezproxy URL: Link to full text
Type: Conference Paper
Appears in Collections:Department of Electrical Engineering

Show full item record

Record view(s)

checked on Aug 12, 2022

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.