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Classification of Short Time Series in Early Parkinson’s Disease With Deep Learning of Fuzzy Recurrence Plots
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Center for Artificial Intelligence, Prince MohammadBin Fahd University, Al Khobar, Kingdom of Saudi Arabia.ORCID iD: 0000-0002-4255-5130
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. (MINT)ORCID iD: 0000-0002-0012-7867
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
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2019 (English)In: IEEE/CAA Journal of Automatica Sinica, ISSN 2329-9266, Vol. 6, no 6, p. 1306-1317Article in journal (Refereed) Published
Abstract [en]

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 disease. Current 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 long. With 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 networks. Being 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.

Place, publisher, year, edition, pages
2019. Vol. 6, no 6, p. 1306-1317
Keywords [en]
Deep learning, early Parkinson’s disease (PD), fuzzy recurrence plots, long short-term memory (LSTM) neural networks, pattern classification, short time series
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-161818DOI: 10.1109/JAS.2019.1911774OAI: oai:DiVA.org:liu-161818DiVA, id: diva2:1369207
Available from: 2019-11-11 Created: 2019-11-11 Last updated: 2019-11-22Bibliographically approved

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Pham, TuanWårdell, KarinEklund, AndersSalerud, GöranSalerud, Göran
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