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Volume 6 Issue 6
Nov.  2019

IEEE/CAA Journal of Automatica Sinica

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Tuan D. Pham, Karin Wårdell, Anders Eklund and Göran Salerud, "Classification of Short Time Series in Early Parkinson’s Disease With Deep Learning of Fuzzy Recurrence Plots," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1306-1317, Nov. 2019. doi: 10.1109/JAS.2019.1911774
Citation: Tuan D. Pham, Karin Wårdell, Anders Eklund and Göran Salerud, "Classification of Short Time Series in Early Parkinson’s Disease With Deep Learning of Fuzzy Recurrence Plots," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1306-1317, Nov. 2019. doi: 10.1109/JAS.2019.1911774

Classification of Short Time Series in Early Parkinson’s Disease With Deep Learning of Fuzzy Recurrence Plots

doi: 10.1109/JAS.2019.1911774
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  • 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.

     

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  • [1]
    S. J. Chinta and J. K. Andersen, " Dopaminergic neurons,” Int. J. Biochem. Cell. Biol., vol. 37, no. 5, pp. 942–946, May 2005. doi: 10.1016/j.biocel.2004.09.009
    [2]
    C. Marras, et al., " Prevalence of Parkinson’s disease across North America,” NPJ Parkinsons Disease, vol. 4, no. 21, 2018. doi: 10.1038/s41531-018-0058-0
    [3]
    P. Rizek, N. Kumar, and M. S. Jog, " An update on the diagnosis and treatment of Parkinson disease,” CMAJ, vol. 188, no. 16, pp. 1157–1165, Nov. 2016. doi: 10.1503/cmaj.151179
    [4]
    A. Elkouzi, " What is Parkinson’s?”[Online]. Parkinson’s Foundation. Available: https://parkinson.org/understanding-parkinsons/what-is-parkinsons, 2019.
    [5]
    G. DeMaagd and A. Philip, " Parkinson’s disease and its management: Part 1: disease entity, risk factors, pathophysiology, clinical presentation, and diagnosis,” P&T, vol. 40, no. 8, pp. 504–532, 2015.
    [6]
    M. Hariz, P. Blomstedt, and L. Zrinzo, " Future of brain stimulation: new targets, new indications, new technology,” Mov. Disord., vol. 28, no. 13, pp. 1784–1792, Nov. 2013. doi: 10.1002/mds.25665
    [7]
    M. Hariz, " Deep brain stimulation: new techniques,” Parkinsonism. Relat. Disord., vol. 20, no. S1, pp. S192–S196, Jan. 2014.
    [8]
    F. N. Emamzadeh and A. Surguchov, " Parkinson’s disease: biomarkers, treatment, and risk factors,” Front. Neurosci., vol. 12, pp. 612, Aug. 2018. doi: 10.3389/fnins.2018.00612
    [9]
    W. H. Oertel, " Recent advances in treating Parkinson’s disease [version 1; peer review: 2 approved],” F1000Research, vol. 6, pp. 260, Mar. 2017. doi: 10.12688/f1000research.10100.1
    [10]
    J. M. Hausdorff, " Gait dynamics in Parkinson’s disease: common and distinct behavior among stride length, gait variability, and fractal-like scaling,” Chaos, vol. 19, no. 2, pp. 026113, Jun. 2009. doi: 10.1063/1.3147408
    [11]
    P. H. Chen, R. L. Wang, D. J. Liou, and J. S. Shaw, " Gait disorders in Parkinson’s disease: assessment and management,” Int. J. Gerontol., vol. 7, no. 4, pp. 189–193, Dec. 2013. doi: 10.1016/j.ijge.2013.03.005
    [12]
    W. Zeng, F. L. Liu, Q. H. Wang, Y. Wang, L. Ma, and Y. Zhang, " Parkinson’s disease classification using gait analysis via deterministic learning,” Neurosci. Lett., vol. 633, pp. 268–278, Oct. 2016. doi: 10.1016/j.neulet.2016.09.043
    [13]
    P. Ren, S. J. Tang, F. Fang, L. Z. Luo, L. Xu, M. L. Bringas-Vega, D. Z. Yao, K. M. Kendrick, and P. A. Valdes-Sosa, " Gait rhythm fluctuation analysis for neurodegenerative diseases by empirical mode decomposition,” IEEE Trans. Biomed. Eng., vol. 64, no. 1, pp. 52–60, Jan. 2017. doi: 10.1109/TBME.2016.2536438
    [14]
    T. D. Pham, " Texture classification and visualization of time series of gait dynamics in patients with neuro-degenerative diseases,” IEEE Trans. Neural. Syst. Rehabil. Eng., vol. 26, no. 1, pp. 188–196, Jan. 2018. doi: 10.1109/TNSRE.2017.2732448
    [15]
    S. Hemm, D. Pison, F. Alonso, A. Shah, J. Coste, J. J. Lemaire, and K. Wardell, " Patient-specific electric field simulations and acceleration measurements for objective analysis of intraoperative stimulation tests in the thalamus,” Front. Hum. Neurosci., vol. 10, pp. 577, Nov. 2016.
    [16]
    L. Kribus-Shmiel, G. Zeilig, B. Sokolovski, and M. Plotnik, " How many strides are required for a reliable estimation of temporal gait parameters? Implementation of a new algorithm on the phase coordination index” PLoS One, vol. 13, no. 2, pp. e0192049, Feb. 2018. doi: 10.1371/journal.pone.0192049
    [17]
    L. Giancardo, A. Sanchez-Ferro, T. Arroyo-Gallego, I. Butterworth, C. S. Mendoza, P. Montero, M. Matarazzo, J. A. Obeso, M. L. Gray, and R. San Jose Estepar, " Computer keyboard interaction as an indicator of early Parkinson’s disease,” Sci. Rep., vol. 6, pp. 34468, Oct. 2016. doi: 10.1038/srep34468
    [18]
    A. L T. Tavares, G. S. X. E. Jefferis, M. Koop, B. C. Hill, T. Hastie, G. Heit, and H. M. Bronte-Stewart, " Quantitative measurements of alternating finger tapping in Parkinson’s disease correlate with UPDRS motor disability and reveal the improvement in fine motor control from medication and deep brain stimulation,” Mov. Disord., vol. 20, no. 10, pp. 1286–1298, Oct. 2005. doi: 10.1002/mds.20556
    [19]
    T. D. Pham, " Fuzzy recurrence plots,” EPL (Europhys. Lett.), vol. 116, no. 5, pp. 50008, Dec. 2016. doi: 10.1209/0295-5075/116/50008
    [20]
    Z. P. Che, S. Purushotham, K. Cho, D. Sontag, and Y. Liu, " Recurrent neural networks for multivariate time series with missing values,” Sci. Rep., vol. 8, no. 1, pp. 6085, Apr. 2018. doi: 10.1038/s41598-018-24271-9
    [21]
    G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. R. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, " Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups,” IEEE Signal Process. Mag., vol. 29, no. 6, pp. 82–97, Nov. 2012. doi: 10.1109/MSP.2012.2205597
    [22]
    I. Sutskever, O. Vinyals, and Q. V. Le, " Sequence to sequence learning with neural networks,” in Proc. 27th Int. Conf. Neural Information Processing Systems, Montreal, Canada, 2014, pp. 3104–3112.
    [23]
    D. Bahdanau, K. Cho, Y. Bengio, " Neural machine translation by jointly learning to align and translate,” Eprint arXiv:1409.0473, 2014.
    [24]
    P. Malhotra, T. V. Vishnu, L. Vig, P. Agarwal, and G. Shroff, " TimeNet: Pre-trained deep recurrent neural network for time series classification,” in Proc. 25th European Symp. Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 2017, pp. 607–612.
    [25]
    N. Mehdiyev, J. Lahann, A. Emrich, D. Enke, P. Fettke, and P. Loos, " Time series classification using deep learning for process planning: a case from the process industry,” Procedia Comput. Sci., vol. 114, pp. 242–249, Dec. 2017. doi: 10.1016/j.procs.2017.09.066
    [26]
    X. J. Shi, Z. R. Chen, H. Wang, D. Y. Yeung, W. K. Wong, and W. C. Woo, " Convolutional LSTM network: a machine learning approach for precipitation nowcasting,” in Proc. 28th Int. Conf. Neural Information Processing Systems, Montreal, Canada, 2015, pp. 802–810.
    [27]
    Z. C. Cui, W. L. Chen, and Y. X. Chen, " Multi-scale convolutional neural networks for time series classification,” Eprint arXiv:1409.0473, 2016.
    [28]
    Z. G. Wang, W. Z. Yan, and T. Oates, " Time series classification from scratch with deep neural networks: a strong baseline,” in Proc. 2017 Int. Joint Conf. Neural Networks, Anchorage, USA, 2017, pp. 1578–1585.
    [29]
    H. I. Fawaz, G. Forestier, J. Weber, L. Idoumghar, and P. A. Muller, " Deep learning for time series classification: a review,” Data Min. Knowl. Discov., vol. 33, no. 4, pp. 917–963, Jul. 2019. doi: 10.1007/s10618-019-00619-1
    [30]
    J. P. Eckmann, S. O. Kamphorst, and D. Ruelle, " Recurrence plots of dynamical systems,” EPL (Europhys. Lett.), vol. 4, no. 9, pp. 973–977, Nov. 1987. doi: 10.1209/0295-5075/4/9/004
    [31]
    F. Takens, " Detecting strange attractors in turbulence,” in Dynamical Systems and Turbulence, Warwick 1980: Proceedings of a Symposium Held at the University of Warwick 1979/80, D. Rand, and L. S. Young, Eds. Berlin, Heidelberg: Springer, 1981, pp. 366–381.
    [32]
    N. Marwan, M. C. Romano, M. Thiel, and J. Kurths, " Recurrence plots for the analysis of complex systems,” Phys. Rep., vol. 438, no. 5-6, pp. 237–329, Jan. 2007. doi: 10.1016/j.physrep.2006.11.001
    [33]
    L. A. Zadeh, " Similarity relations and fuzzy orderings,” Inf. Sci., vol. 3, no. 2, pp. 177–200, Apr. 1971. doi: 10.1016/S0020-0255(71)80005-1
    [34]
    J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum Press, 1981.
    [35]
    S. Hochreiter and J. Schmidhuber, " Long short-term memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, Nov. 1997. doi: 10.1162/neco.1997.9.8.1735
    [36]
    A. Graves and J. Schmidhuber, " Framewise phoneme classification with bidirectional LSTM and other neural network architectures,” Neural Netw., vol. 18, no. 5-6, pp. 602–610, Jul.–Aug. 2005. doi: 10.1016/j.neunet.2005.06.042
    [37]
    A. Graves, N. Jaitly, and A. R. Mohamed, " Hybrid speech recognition with Deep Bidirectional LSTM,” in Proc. 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, Olomouc, Czech Republic, 2013, pp. 273–278.
    [38]
    R. Zazo, A. Lozano-Diez, J. Gonzalez-Dominguez, D. T. Toledano, and J. Gonzalez-Rodriguez, " Language identification in short utterances using long short-term memory (LSTM) recurrent neural networks,” PLoS One, vol. 11, no. 1, pp. e0146917, Jan. 2016. doi: 10.1371/journal.pone.0146917
    [39]
    Z. Y. Li, K. Gavrilyuk, E. Gavves, M. Jain, and C. G. M. Snoek, " VideoLSTM convolves, attends and flows for action recognition,” Comput. Vision Image Underst., vol. 166, pp. 41–50, Jan. 2018. doi: 10.1016/j.cviu.2017.10.011
    [40]
    T. Mikolov, S. Kombrink, L. Burget, J. H. Cernocky, and S. Khudanpur, " Extensions of recurrent neural network language model,” in Proc. 2011 IEEE Int. Conf. Acoustics, Speech and Signal Processing, Prague, Czech Republic, 2011, pp. 5528–5531.
    [41]
    R. Pascanu, T. Mikolov, and Y. Bengio, " On the difficulty of training recurrent neural networks,” in Proc. 30th Int. Conf. Machine Learning, Atlanta, GA, USA, 2013, pp. III-1310-III-1318.
    [42]
    K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, and J. Schmidhuber, " LSTM: a search space odyssey,” IEEE Trans. Neural Netw. Learn. Syst., vol. 28, no. 10, pp. 2222–2232, Oct. 2017. doi: 10.1109/TNNLS.2016.2582924
    [43]
    PhysioNet, " neuroQWERTY MIT-CSXPD Dataset,” [Online]. Available: https://www.physionet.org/physiobank/database/nqmitcsxpd/. Accessed on: Dec. 20, 2016.
    [44]
    P. Martinez-Martin, A. Gil-Nagel, L. M. Gracia, and J. B. Gomez, J. Martinez-Sarries, F. Bermejo, and The Cooperative Multicentric Group, " Unified Parkinson’s disease rating scale characteristics and structure,” Mov. Disord., vol. 9, no. 1, pp. 76–83, Jan. 1994. doi: 10.1002/mds.870090112
    [45]
    T. D. Pham, " Pattern analysis of computer keystroke time series in healthy control and early-stage Parkinson’s disease subjects using fuzzy recurrence and scalable recurrence network features,” J. Neurosci. Methods, vol. 307, pp. 194–202, Sep. 2018. doi: 10.1016/j.jneumeth.2018.05.019
    [46]
    H. Kantz and T. Schreiber, Nonlinear Time Series Analysis. 2nd ed. Cambridge: Cambridge University Press, 2004.
    [47]
    C. Szegedy, W. Liu, Y. Q. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, " Going deeper with convolutions,” in Proc. 2015 IEEE Conf. Computer Vision and Pattern Recognition, Boston, MA, USA, 2015, pp. 1–9.
    [48]
    A. Krizhevsky, I. Sutskever, and G. E. Hinton, " ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, Jun. 2017. doi: 10.1145/3065386
    [49]
    MathWork, " Classify time series using wavelet analysis and deep learning,” [Online]. Available: https://mathworks.com/help/deeplearning/examples/classify-time-series-using-wavelet-analysis-and-deep-learning.html. Accessed on: Apr. 25, 2019.
    [50]
    S. Karimi-Bidhendi, F. Munshi, and A. Munshi, " Scalable classification of univariate and multivariate time series,” in Proc. IEEE Int. Conf. Big Data, Seattle, WA, USA, 2019, pp. 1598–1605.
    [51]
    H. I. Fawaz, G. Forestier, J. Weber, L. Idoumghar, and P. A. Muller, " Transfer learning for time series classification,” in Proc. IEEE Int. Conf. Big Data, Seattle, WA, USA, 2018, pp. 1367–1376.
    [52]
    M. Schuster and K. K. Paliwal, " Bidirectional recurrent neural networks,” IEEE Trans. Signal Process., vol. 45, no. 11, pp. 2673–2681, Nov. 1997. doi: 10.1109/78.650093

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    • Classification of short time series.
    • Deep learning.
    • Fuzzy recurrence plots.
    • Neurodegenerative disorder.

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