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Classification of Short Time Series in Early

Parkinson’s Disease With Deep Learning

of Fuzzy Recurrence Plots

Tuan D. Pham, Senior Member, IEEE, Karin Wårdell, Member, IEEE, Anders Eklund, and Göran Salerud

 Abstract — There are many techniques using sensors and wear-

able devices for detecting and monitoring patients with Parkin-

son's disease (PD). A recent development is the utilization of hu-

man 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 dur-

ations 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 recur-

rence 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 classifica-

tion of healthy control and early PD subjects.

IndexTerms—Deep learning, early Parkinson’s disease (PD), fuzzy

recurrence plots, long short-term memory (LSTM) neural networks,

pattern classification, short time series.

I. Introduction

ARKINSON’S disease (PD) is a neurodegenerative dis-

order that affects dopaminergic neurons [1]. Statistics on

PD have reported it affects approximately 10 million people

worldwide, and about 4% of them before the age of 50 [2].

Symptoms of PD slowly develop over years, and the progres-

sion of PD can be different among individuals because of the

diversity of the disease. People with PD can be observed with

tremor, bradykinesia (slowness of movement), limb rigidity,

and gait and balance problems. The cause of PD remains un-

known [3]. Because a significant amount of the substantia

nigra neurons have already been lost or impaired before the

onset of motor features, people with PD may first start experi-

encing symptoms later in the course of the disease [4], [5].

Treatment options for PD can vary and include medications

and sometimes deep brain stimulation [6], [7]. While Parkin-

son’s itself is not fatal, disease complications can be serious [4].

Many scientific efforts have been spent on exploring

methods for identifying biomarkers for PD [8] with the hope

that these markers can lead to earlier diagnosis and targeted

treatments of the disease. However, present therapies used for

PD cannot slow or stop the disease in a prodromal stage [9].

There are many techniques using sensors for detecting and

monitoring movement patterns on patients with PD. Most

techniques using sensor-induced data focus on studying gait

dynamics and temporal gait parameters[10]–[14], and others

use wrist accelerometers for intraoperative measurements of

tremor during surgery [15]. One issue of using sensor data is

that gait measurements are to be obtained during relatively

long walking periods, causing discomfort to the participants or

impracticability of performance in clinical settings. Therefore,

research into the minimum strides required for a reliable

estimation of temporal gait parameters has recently been

carried out with the purpose of avoiding or minimizing

discomfort to participants in gait experiments [16].

Apart from gait and balance data, the measurement of

computer keystroke time series that contain information of the

hold time occurring between pressing and releasing a key

collected during the sessions of typing activity using a

standard word processor on a personal computer has been

proposed for detecting early stages of PD [17]. Being similar

to the motivation for determining the minimum number of

strides for the analysis of gait dynamics, this study is

interested in answering the question if there are methods that

can process very short time series and achieve good results for

differentiating healthy controls from subjects with early PD. If

being successful, the use of computer keystroke time series

can be equivalent on a practical basis to the use of mobile

sensor data for evaluating upper limbs motor functions by

finger tapping [18] that is typically used in clinical trials. The

finger tapping test requires a participant to press one or two

Manuscript received September 23, 2019; accepted October 24, 2019. Re-

commended by Associate Editor Patrick Schäfer. (Corresponding author: Tu-

an D. Pham.)

Citation: T. D. Pham, K. Wårdell, A. Eklund, and G. Salerud, “Classifica-

tion 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.

Tuan D. Pham is with the Department of Biomedical Engineering, the Cen-

ter for Medical Image Science and Visualization, Linköping University,

Linköping, Sweden; the Center for Artificial Intelligence, Prince Mohammad

Bin Fahd University, Al Khobar, Kingdom of Saudi Arabia (e-mail:

tuan.pham@liu.se).

Karin Wårdell is with the Department of Biomedical Engineering,

Linköping University, Linköping, Sweden (e-mail: karin.wardell@liu.se).

Anders Eklund is with the Department of Biomedical Engineering, the De-

partment of Computer and Information Science, the Center for Medical Im-

age Science and Visualization, Linköping University, Linköping, Sweden (e-

mail: anders.eklund@liu.se).

Göran Salerud is with the Department of Biomedical Engineering,

Linköping University, Linköping, Sweden (e-mail: goran.salerud@liu.se).

Color versions of one or more of the figures in this paper are available on-

line at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JAS.2019.1911774

1306

IEEE/CAA JOURNAL OF AUTOMATICA SINICA, VOL. 6, NO. 6, NOVEMBER 2019

buttons on a device such as an iPhone as fast as possible for a

short period of time.

The method of fuzzy recurrence plots [19] developed for

studying nonlinear dynamics in time series data can be useful

for creating feature dimensions of short time series. Therefore,

the deep learning of fuzzy recurrence plots is proposed in this

study for the classification of short time series of computer-

key hold time recorded from two cohorts of healthy control

and early PD.

The rest of this paper is organized as follows. Section II

includes a survey of literature relating to the present study.

Section III outlines the concept and mathematical formulation

of fuzzy recurrence plots. Section IV presents the

implementation of LSTM neural networks with fuzzy

recurrence plots. The public database for testing the proposed

classification approach is described in Section V. Section VI

shows the experimental results together with discussion.

Finally, Section VII consists of the concluding remarks of the

research finding and suggestion of issues for future work.

II. Related Work

Having highlighted earlier, the concept of using fuzzy

recurrence plots of short time series for classification using

LSTM networks is original in that it contributes to the

increase of the feature dimensions of short raw time series,

which, in turn, can improve the classification. No prior work

of similar concepts exists in literature. A survey of recent

reports that applied LSTM and convolutional neural networks

for time-series or sequential data classification is addressed

herein.

The deep-learning method of LSTM neural networks has

been adopted for the classification of time-series or sequential

data [20], including speech recognition [21] and machine

translation [22], [23]. A deep recurrent neural network called

TimeNet for extracting features from time series was

developed [24]. The TimeNet was designed to generalize time

series representation across domains. A trained TimeNet can

be used as a feature extractor for time series and was reported

to be useful for time series classification by performing better

than a domain-specific recurrent neural network and dynamic

time warping [24]. Stacked LSTM autoencoder networks were

applied to extract features of time-series data, which were then

used to train deep feed-forward neural networks for

classification of multivariate time series recorded with sensors

in the steel industry to detect steel surface defects [25]. In this

work, the features extracted with LSTM autoencoders were

found to be useful, and therefore the need for domain expert

knowledge can be alleviated.

Other time-series classification using convolutional neural

networks (CNNs) have recently been introduced. A

convolutional LSTM (ConvLSTM) was introduced for a

spatiotemporal sequence forecasting problem in which both

the input and the prediction target are spatiotemporal

sequences [26]. This ConvLSTM model was constructed by

extending the fully connected LSTM to have convolutional

structures in the input-to-state and state-to-state transitions.

The ConvLSTM network was reported to perform better than

the fully connected LSTM by being able to capture the

spatiotemporal correlations of the sequential data for

precipitation nowcasting. A multi-scale convolutional neural

network [27], which extracts deep-learning features at

different scales and frequencies from three representations of

time series, including the original, down-sampled, and

smoothed data, was reported be capable of extracting effective

features for time series classification. Baseline full

convolutional networks were proposed for time series

classification [28]. The proposed baseline models were

reported to be pure end-to-end without demanding heavy pre-

processing of the raw data or feature crafting, and achieve

competitive performance to other state-of-the-art approaches,

including the multilayer perceptron, fully convolutional

network, and residual network. This network consists of a

branching structure. The first branch is the convolutional part,

whereas the second branch is an LSTM block which receives

a time series in a transposed form as multivariate time series

with a single time step. The outputs of the two branches are

concatenated and then fed to a classifier. These models were

reported to be able to enhance the performance of fully

convolutional networks with a nominal increase in model size

as well as require minimal data pre-processing.

In general, time-series classification has been recognized as

an important and challenging area of research, particularly

with respect to the demand for handling increasing availability

of new data of time series. While numerous algorithms for

time-series classification have been published in literature and

the popularity of deep learning has been pervasive in many

disciplines, only a few deep neural networks have been

applied to solving time-series classification problems. A

recent survey on promising applications of deep neural

networks for time series classification in several areas can be

found in [29],

Having reviewed recent deep neural networks for time-

series classification, the purpose of this study is not to

compare the performance of time-series classification between

LSTM and CNN models. This study introduces the usefulness

of constructing fuzzy recurrence plots of short time series that

can be incorporated into LSTM models to improve the

classification accuracy.

III. Fuzzy Recurrence Plots

The development of constructing a fuzzy recurrence plot

(FRP) of a time series was inspired with the concept of a

recurrence plot (RP). An RP [30] is a visualization method for

studying patterns of chaos in time series. An RP shows the

times at which a phase-space trajectory approximately revisits

the same area in the phase space.

X = {x}

N × N

(i, k)

i = 1,. . . , N

k = 1, . .. , N

Based on the Takens’ embedding theorem [31] in the study

of dynamical systems, a phase-space reconstruction of a time

series can be obtained using an embedding dimension and

time delay . Let be a set of phase-space vectors, in

which is the th state of a dynamical system in -

dimensional space and time delay . An RP is constructed as

an matrix in which an element , ,

, is represented with a black dot if and are

considered to be closed to each other. For a symmetrical RP, a

threshold, denoted as , is used to define the similarity of a

PHAM et al.: CLASSIFICATION OF SHORT TIME SERIES IN EARLY PD WITH DEEP LEARNING OF FRPs 1307

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