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Classification of Short Time Series in Early Parkinson’s Disease With Deep Learning of fuzzy recurrence plots

IEEE/CAA Journal of Automatica Sinica 2019年第6期6卷 1306-1317页

作者： Tuan D.Pham Karin Wardell Anders Eklund Goran Salerud Department of Biomedical Engineering the Center for Medical Image Science and VisualizationLinkoping UniversityLinkopingSweden the Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityAl KhobarKingdom of Saudi Arabia Department of Biomedical Engineering Linkoping UniversityLinkopingSweden Department of Biomedical Engineering the Department of Computer and Information Sciencethe Center for Medical Image Science and VisualizationLinkoping UniversityLinkopingSweden

There are many techniques using sensors and wearable devices for detecting and monitoring patients with Parkinson’s disease(PD).A recent development is the utilization of human interaction with computer keyboards for analyzing and identifying motor signs in the early stages of the *** designs for classification of time series of computer-key hold durations recorded from healthy control and PD subjects require the time series of length to be considerably *** an attempt to avoid discomfort to participants in performing long physical tasks for data recording,this paper introduces the use of fuzzy recurrence plots of very short time series as input data for the machine training and classification with long short-term memory(LSTM)neural *** an original approach that is able to both significantly increase the feature dimensions and provides the property of deterministic dynamical systems of very short time series for information processing carried out by an LSTM layer architecture,fuzzy recurrence plots provide promising results and outperform the direct input of the time series for the classification of healthy control and early PD subjects.

关键词： Deep learning early Parkinson’s disease(PD) fuzzy recurrence plots long short-term memory(LSTM) neural networks pattern classification short time series

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