Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/626
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dc.contributor.authorTaleb, Catherineen_US
dc.contributor.authorLikforman-Sulem, Laurenceen_US
dc.contributor.authorMokbel, Chaficen_US
dc.date.accessioned2020-12-23T08:33:52Z-
dc.date.available2020-12-23T08:33:52Z-
dc.date.issued2019-
dc.identifier.urihttps://scholarhub.balamand.edu.lb/handle/uob/626-
dc.description.abstractDeep 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.en_US
dc.format.extent15 p.en_US
dc.language.isoengen_US
dc.subjectPDMultiMC dataseten_US
dc.subjectParkinsons disease (PD)en_US
dc.subjectCNNen_US
dc.subjectCNN-BLSTMen_US
dc.subjectData augmentationen_US
dc.subjectTransfer learningen_US
dc.subject.lcshHandwritingen_US
dc.titleImproving deep learning parkinsons disease detection through data augmentation trainingen_US
dc.typeConference Paperen_US
dc.relation.conferenceMediterranean Conference on Pattern Recognition and Artificial Intelligence (3rd : 22-23 Dec, 2019 : Istanbul, Turkey)en_US
dc.contributor.affiliationDepartment of Electrical Engineeringen_US
dc.description.volume1144en_US
dc.description.startpage79en_US
dc.description.endpage93en_US
dc.date.catalogued2020-02-13-
dc.description.statusPublisheden_US
dc.identifier.ezproxyURLhttp://ezsecureaccess.balamand.edu.lb/login?url=https://link.springer.com/chapter/10.1007/978-3-030-37548-5_7en_US
dc.identifier.OlibID252319-
dc.relation.ispartoftextCommunications in Computer and Information Science (CCIS)en_US
dc.provenance.recordsourceOliben_US
Appears in Collections:Department of Electrical Engineering
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