Use of technological tools for Parkinson's disease early ... - IEEE Xplore

2014 IEEE International Conference on Control System, Computing and Engineering, 28 - 30 November 2014, Penang, Malaysia

Use of Technological Tools for Parkinson's disease Early Detection: A Review * Q.W.Oung , M.Hariharan, S.N. Basah, S. Yaacob, M.Sarillee, H.L.Lee School of Mechatronic Engineering Universiti Malaysia Perlis (UniMAP), Campus Pauh Putra, 02600, Arau, Perlis, Malaysia *[email protected]

levodopa or other drugs that activate dopamine receptors of PD. Levodopa that has been the most successful medication for reducing symptoms in patient with Parkinson's (PWP), is prescribed in order to eliminate the typical symptoms at the early stages of PD. However, during late-stage PD, patients start to develop motor complications, namely the abrupt loss of efficacy of medications at the end of a dosing interval, wearing off and involuntary hyperkinetic movements referred to as dyskinesia [6]. Clinicians refer to such variations as motor fluctuations as shown in Figure 1. Many of the PWP start to fluctuate between the "off' state (re-emergence of PD symptoms due to the effect of levodopa wears off a few hours after levodopa intake) and the "on" state (i.e. levodopa is active and improves the patients' motor performance). However, in the "on" state, patients suffer from dyskinesia. The presence of dyskinesia is a side effect of levodopa therapy and therefore referred to as levodopa-induced dyskinesia [7, 8].

Abstract- Over the past fifteen years, quantitative monitoring of human motor control and movement disorders has been an emerging field of research. Recent studies state the fact that Malaysia has been experiencing improved health, longer life expectancy, and low mortality as well as declining fertility like other developing countries. As the population grows older, the prevalence of neurodegenerative diseases also increases exponentially. Parkinson disease (PD) is one of the most common chronic progressive neurodegenerative diseases that are related to movement disorders. After years of research and development solutions for detecting and assessing the symptoms severity in PD are quite limited. With current ongoing advance development sensor technology, development of various uni-modal approaches: technological tools to quantify PD symptom severity had drawn significance attention worldwide. The objective of this review is to compare some available technological tools for monitoring the severity of motor fluctuations in patients with Parkinson (PWP). Index Terms-Parkinson's disease (PD), Technological Tools, electroencephalogram (EEG), electromyogram (EMG), wearable sensors

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I. INTRODUCTION

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For several decades, neurological disorders, that includes Parkinson's disease (PD), epilepsy and Alzheimer's, affects the lives of patients and their family members at an epidemic rate worldwide [1]. Improvement in health and longevity has brought changes in the demographic profile of its population whereby the prevalence increases exponentially with advancing age [2]. As the incidence of PD increases with age, PD is one of the most common chronic progressive neurodegenerative disease worldwide. It occurs in about 3% of the population over the age of 65, and this figure is expected to increase in the not too distance future [3]. According to the statistic conducted by the World Health Organization (WHO), it was estimated that 7 to 10 million people in the world are living with PD [4]. Several indications of PD include tremor, bradykinesia, rigidity and postural instability [5]. Currently, there is no available cure, although medication using drugs can offer significant alleviation to some of the symptoms and slowing down the progression of the disease. This clinical intervention is according to the augmentation or replacement of dopamine, by the use of biosynthetic precursor

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Figure I: Schematic representation of PO motor fluctuation cycle In order for these patients to function at their best, medications must be optimally adjusted whereby the managing clinician must have an accurate picture of the way the PD symptoms will fluctuate throughout a typical day's activities. With the current and ongoing advance development of sensor technology, quantitative methods for detecting and assessing the severity of symptoms of motor disorders in PD are quite limited. Development of various uni-modal approaches: technological tools to quantifY PD symptom severity have drawn significance interest among researchers. Although physiological signal based PD progression and monitoring

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conducted. This classifier had shown promising results for identification the onset of freezing of PD patients during walking using these features with the highest accuracy of 76.6% for identifYing onset of freezing of PD patients. In the latest research conducted by Priyanka G. Bhosale et al. [9] she had applied the combination of two classifiers: Support Vector Machine (SVM) and Multilayer Perceptron (MLP) for PD detection from EEG signals. The overall methodology was presented as shown in Figure 2. Feature extraction of the signal was obtained by applying the Discrete Fourier transform (DFT) to obtain the frequency components of the signal and the unwanted frequency components are removed. Then, the statistical feature vectors were calculated from these clean frequency components using the percentage power formula in order to obtain the output of different frequency bands which are the input to the classifier. In the feature classification stage, two different classifiers were applied, support vector machine (SVM) and Multilayer perceptron-Backpropagation (MLP-BP) to obtain the output of the classifier. SVM selected a discriminate hyper plane that maximizes the margins, which is the distances from the nearest training points for class identification. This enables classification using linear decision boundaries which are known as linear SVM. In addition, there is a possibility of creating nonlinear decision boundaries by using "kernel trick" with low increase of the classifiers complexity known as Gaussian SVM or Radial Basis Function (REF) SVM. MLP is a feed forward artificial neural network model that maps sets of input data into a set of appropriate output utilizing a supervised learning technique called backpropagation to train the network. These two classifiers was combined instead of using only one classification algorithm because SVM has best training accuracy while the MLP has best testing accuracy than others. The proposed classifier shows the promising classification accuracy that had capability to identifY any input combination as belonging to either one of these two classes: Normal or Parkinson's [9].

system had been developed by many researchers, system based on electroencephalogram (EEG) [9, 14], electromyogram (EMG) [lO, 17-22], brain imaging (BI) [11, 24-25], wearable sensors and audio sensor has increased the interest of many researchers in recent years. Such sensor systems allow the possibility to envision an unobtrusive system for monitoring PD early symptom severity on a more continuous basis. This is a promising tool that can enable long-term monitoring in the home, having the potential to improve the standards of heaIthcare delivery while making it an efficient and cost effective process to PWP. In this review, our focus will be on the state of art in early detection of PD symptom severity performing through some technological tools. Section 2 covers some of the previous research conducted using EEG, Section 3 contains previous research on monitoring PD using EMG while previous work on monitoring PD using BI are presented in Section 4. Section 5 provides the overall discussion and concludes the paper. II. MONITORING PD USING ELECTROENCEPHALOG�(EEG) Freezing of gait (FOG) is grouped as one of the common disabling gait symptoms of advancing PD that cause an increased risk of falling in PWP. Patients typically experience a sudden inability to perform walking, despite the intension to move forward, that has been defined as a "brief, episodic absence or marked reduction of forward progression of the feet despite the intention to walk" [12, 13]. In research conducted by A.M.Ardi et al. [14], he had presented a methodology for FOG detection by using EEG signals based on wavelet decomposition and pattern recognition techniques. As to allow detection of changes in a short segment of EEG signals, two features, EEG subband wavelet energy (WE) and total wavelet entropy (TWE) had been extracted using the multi resolution decomposition of EEG signal based on Discrete Wavelet Transform (DWT) as shown in Eq. 1:

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=

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f"" x(t)

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( - k) t

2j

2j

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DISCRETE FOURIER TRANSFORM (DFT)

INPUT SIGNAL

(1)

where 2i and 2i k are the scale (reciprocal of frequency) and translation (time localization) respectively. This wavelet transform will provide an excellent feature extraction from the EEG non-stationary signals where its advantages include multirate filtering, time-frequency localization, and multiscale zooming in order to detect and characterize transients since its building block functions had capability in adapting and can be adjusted [15]. Three layers Back Propagation Neural Network (BP-NN) classifier had been chosen as the classifier where 56% of the data were trained while 25% of the data used as validation and 19% of the data used as testing by the Levenberg Marquardt algorithm. The activation function used was tangent sigmoid and the number of hidden nodes was selected based on the number of training pairs and number of inputs dimension. For each selected features, 20 separated training and testing were

FREQUENCY COMPONENTS

REMOVE UNWANTED NOISE CLEAN FREQUENCIES

OVERALL COLLECTIVE OUTPUTS

OUTPUT

MLP-BP + SVM

SIGNAL COMPONENT

% POWER

Figure 2: Overall system methodology from [9]

III. MONITORING PD USING ELECTROMYOGRAPHY (EMG) Methods previously used in the research of EMG signal for distinguishing between the PD patients and healthy controls

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can be divided into: morphology analysis approach [16, 21], spectral based analysis approach [17-19], non-linear analysis approach [18, 20] and approach from the above three categories [10]. Table 1 gives a sunun ary of the research completed on PD detection using EMG signals. Saara Rissanen et al. [16] presented a novel study for analysis of surface EMG morphology in PD. This new approach was developed due to the difficulty in analyzing spiky impulse-like EMG waveform and the information about brain disorder is in the morphology of an impulse chain. The proposed method was based on integration of histogram and crossing rate (CR) analysis of EMG signal which are used as high dimensional feature vectors. Then Karhunen-Loeve transform (KLT) was used to reduce the dimensionality of the feature vectors. Finally, the discriminant analysis of feature vectors will be performed in a low dimensional eigenspace. Histograms and CR values were selected because Parkinsonian EMG signals typically involve bursting and spiky patterns. Traditional Fourier-based spectral analysis are not effective in analyzing impulse-like signals where the analysis only conducts decomposition of a signal into harmonic basic functions of different frequencies. Besides that, this traditional method of amplitude analysis is not effective in signal morphology analysis, although they had the ability in detecting increased level of muscle activity. The ratio of correct discrimination by using augmented KLT was 86 % for the control group and 72 % of the PD patient group. Based on V.Ruonalla et al. [21] research, essential tremor (ET) and PD tremor can be difficult to differentiate as both may occur under the same circumstances. This research objective was to develop a methodology aiming to differentiate patients with ET from patients with PWP using EMG measurements. The obtained EMG signal was high pass filtered using the smoothness priors detrending method to remove the low frequency trends from the signal and were divided into smaller segments with length 2048ms and 75% overlapping. Similar to research proposed in [16], morphology analysis using sample histogram (as shown in Figure 3) that was calculated with 200 bins for each epoch in one segment were implemented due to the spiky nature of the EMG signal.

was found out that the best discriminators were the height of the histogram and the side differences between left hand and right hand. Results have shown that it was possible to discriminate 13/17 (76%) patients with essential tremor and 26/35 (74%) patients with PD [21]. Research conducted by Bryan et al. [22] described a two stage Freezing of Gait (FOG) detection algorithm while the PD performs some daily unscripted and unconstrained activities. The output from both wireless, wearable, miniaturized triaxial accelerometer and EMG sensors' worn by PD patients were derived as input features of the dynamic neural network (DNN) for detecting FOG instances. Three triaxial accelerometer sensors were placed on one forearm, thigh, and shin of the subject, while an additional surface EMG sensor was placed on the shin as shown in Figure 4. In this paper the researchers had

designed and presented a two-stage algorithm for FOG detection that consists of: 1) Linear classifier for detection of the subject when they are upright either to be standing or walking and 2) DNN that was designed for FOG detection given that the subject is upright. Figure 4: Placement of the accelerometer and surface EMG sensor of the PO patient and the example of signals collected from the sensor placed on the shin of PO patient [22]

Detection of FOG can only be conducted when the subject is either attempting to initiate walking while in the standing position or attempting to continue walking. Therefore, the first stage of the developmental algorithm was applying the linear classifier for upright state detection using the triaxial accelerometers placed on the forearm, thigh and shin. Once declaration of the subject to be upright for more than 4 consecutive seconds was completed using the first stage classifier, application of DNN over the interval in which the patient is upright was conducted to determine if FOG is present. Upon assessing the effectiveness of this system on experimentally collected datasets, the FOG detector expressed 83% sensitivity and 97% specificity on a per-second basis. Thus, this algorithm is a practical solution to the problem of detecting FOG in PWP during activities of daily living, and can unobtrusively detect symptoms of PD in patients in their home environments [22].

Figure 3: EMG histogram [21] In order to visualize the histogram with only few useful components, principle component analysis (PCA) was applied whereby these components act as the new parameters for signal classification. Analysis was performed with the comparison of every combination of two basic vectors (height of the histogram, sharpness of the peak and side differences) and evaluation of their ability was conducted in order to discriminate between PD and ET groups. From the analysis it

IV. MONITORING PD USING 3D MOTION ANALYSIS OR IMAGING MODALITIES Research had been conducted for analyzing the occurrence of resting tremor which is one of the obvious symptoms of PD by Magdalena et al. [23] based on kinematic measurements using multimodal motion capture (MOCAP) system

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Table I: Summary of previous work for PO detection using EMG signals

AUTHORS

DATABASE

METHODS

PERFORMANCE MEASURE

Saara Rissanen et al. [161

22 healthy su bjects and 26 patients with PO

Histogram and crossing rate (CR) values selected as feature vectors while Korhunen-Loeve transform (KLT) used for high dimensionality reduction

Correct discrimination for control: 86% and PD: 72%

Saara Rissanen et al.[101

59 healthy subjects and 42 patients with PO

Selected 12 variables (six right and six left side variables): Kurtosis, Crossing rate, Correlation dimension, Recurrence rate, Sample entropy of acceleration and Coherence variable of acceleration

Clustering analysis using iterative k-means algorithms into 3 clusters: One cluster contained 90% of the healthy controls and two other clusters 76% of the PD patients

Gennaro De Michele et al. [171

10 male patients with PO and 6 healthy male subjects

Wavelet correlation analysis with Global wavelet power (PCQ) parameters extracted from local wavelet power spectra

Accurately classify the subjects into PD from healthy control

V.Ruonala et al.[211

35 patients with PD and 17 patients with essential tremor (ET)

Sample histograms during isometric contraction of biceps brachii muscle with varying loads and Principle Component Analysis (PCA) for feature dimension reduction

Discriminate 13/17 (76%) patients with ET and 26/35 (74 %) patients with PD

A.1. Meigal et al. [20)

19 patients with PO and 20 control subjects

Nonlinear SEMG parameters (% Recurrence, %Determinism and SEMG distribution kurtosis, correlation dimension and sample entropy

Differentiate the patients with PD from healthy control

Bryan T.Cole et al.[22)

2 healthy subjects and 4 patients with PD

Linear classifier to detect when the subject is upright and Dynamic Neural Network (DNN) to detect FoG given that the subject is upright

Sensitivity (82.9%) Specificity (97.3%)

and

unagmg. Data collectIOn was obtamed from the restmg-state functional magnetic resonance imaging (rsfMRI) and voxel based morphometry (structural images). The combination of this both imaging modalities had shown good correlation between brain changes in PWP with the severity of PD. After data pre-preprocessing, feature extraction was conducted using template based approach whereby for the rsfMRI images, feature characteristic was extracted at three different levels: Amplitude of low frequency fluctuations (ALFF), Regional homogeneity (ReHo) and Regional functional connectivity strength (RFCS). While, for structural images, volume characteristic was extracted from the gray matter (GM), the white matter (WM) and the cerebro-spinal fluid (CSF). Then feature selection was conducted due to decreasing of classification accuracy of certain extracted features and the generalization of noise. In this research, two sample t-tests were conducted for comparing the feature values of various brain regions for both PWP and normal subjects where features with significant difference (P< 0.05, uncorrected) between the two groups were selected. Lastly, classification was conducted from supervised machine-learning algorithm, Support Vector machine (SVM) using the leave-one-out cross validation method that produce overall accuracy of 86.96%.

for regIstratIon of 3D positions of body markers, ground reaction forces and EMG signals. In this research, PD patients that were involved had undergone deep brain stimulation surgical treatment while the data collection was taken under four conditions where stimulator was turned ON/OFF and medication was ON/OFF. The initial step of the overall analysis was the removal of the constant and trend component from the triaxial coordinates of each signal from left and right markers. This step was accomplished using the recursive histogram algorithm that aims to retrieve the mean of the noise distribution. Then, the calculation of the resulting 3D spatial signal across triaxial axis was performed to obtain two 3D tremor signals that correspond to the left and right markers. Besides that, frequency analysis was conducted using Fast Fourier Transform (FFT) whereby amplitude spectra was calculated and based on this, the maximal amplitude, mean amplitude and area under the curve of spectrum in the range of {37Hz} and {4-6Hz} were calculated. Lastly, statistical analysis was conducted based on the t-test performed to obtain results whereby the obtained results had shown the occurrence of statistically significance differences of certain tremor parameters between different tremor conditions. Objective methods for diagnosing PD using non-invasive neuroimaging modality was conducted by Dan Long et al. [24] by integrating multi-modal Magnetic Resonance (MR)

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The ultimate goal of these collaborative efforts between the heaIthcare and engineering communities is to enable unobtrusive autonomous monitoring of the patients' state and generate valuable clinical feedback [34]. Due to the disadvantages brought by this few technological tools in making prediction on PD, a more readily and easier diagnostic methods are desirable. We proposed to develop a multimodal system using wearable sensors and speech signal for detecting and assessing the symptoms severity level of motor disorders in PD. Wearable sensor and microphones are cheaper, smaller, more robust, and it's a promising tool that can enable long term monitoring in the home. Home monitoring has the potential to improve the standards of healthcare delivery while making it an efficient and cost effective process of rehabilitation. Wearable sensor technology is totally unobtrusive and does not interfere with the PWP's normal behavior. It allows physicians to overcome the limitations of ambulatory technology and provide a response to the need for monitoring individuals over weeks or even months. They typically rely on wireless, miniature sensors enclosed in patches or bandages, or in items that can be worn, such as a ring or a shirt. Study of progression and severity of PD using speech signals is a non-invasive method, easy to obtain from PWP who are not required or expected to perform any special kinds of actions. In future, this new scenario is expected to provide a remarkable improvement in the patients' management as well as a substantial cutting-off of the economic burden caused by PD [35, 36].

V. DISCUSSION AND CONCLUSION This review paper had been focused on the discussion of the capabilities of several type of assessment of PD through technological tools: EMG, EEG and imaging modalities. As the most common onset are slowness (82.4%), difficulty in walking (77.1%), and difficulty in writing (53.6%), stiffuess (50%), tremor (82%) and speech difficulty (34%) [26], all these approaches have their own advantages and disadvantages in terms of accuracy, user-acceptance and applicability. However, this approach brings some disadvantages on recording all the above said common symptoms that often not able to meet the desired performance requirements. In EEG based methods, brain responses of PWP were recorded with or without visual cues, which is difficult for patients and their caregivers, especially in the later stage of PD. The electrical activity recorded by electrodes placed on the scalp or the surface of the brain only reflects the summation of excitory and inhibitory postsynaptic potentials in apical dendrites of pyramidal neurons in the more superficial layers of the cortex. The location of the source of the electrical activity may sometimes give confusing impressions due to the propagation of electrical activity along the physiological pathway or through volume conduction in extracellular spaces. Moreover, the placement of an EEG cap gives discomfort and data collection procedure is tedious for PWP [27, 28]. While, for methods using EMG signals, the physicians must have a very good understanding of the anatomy of the human body as the electrode location and placement is very important. [29, 30] In addition, EMG will not work effectively if the patient is taking medication that affects the nervous system. Moreover, EMG is more sensitive to dynamic movements; hence this will not be useful to record the static movements of PWP [31]. Methods using imaging modalities such as MRI scan also bring some disadvantages whereby the machine makes a tremendous amount of noise like a continual, rapid hammering during a scan. The combination of PD patient being put in an enclosed space and the loud noises that are made with the magnets can make some patients feel claustrophobic while they are having an MRI scan. It requires PWP to hold still for extended periods of time. Even very slight movement of the part being scanned can cause distorted images which means the scanning will need to be repeated [32]. Although CT scan has advantages of precise, painless and highly detailed compared to other imaging modalities, it insert a high dose of radiation in the PD patient. It sometimes will give misinterpretations to doctors whereby the scan can report wrong problems in the patients' body where PWP will have to undergo unnecessary treatments which exposed them to more radiation. Besides that, the imaging modalities produce remarkable and exquisite anatomic pictures, but the brain changes that create neurodegenerative disease such as PD are microscopic, on a chemical level and are not revealed by these scans [33].

ACKNOWLEDGMENT

All the authors would like to acknowledge the journal incentive research grants received from Universiti Malaysia Pedis (UniMAP) [Grant No: 9007-00071 and 9007-00117]. REFERENCES [I]

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