Automated detection of PD resting tremor using PSD with recurrent neural network classifier

a,bR Arvind, a,bB Karthik, b*N Sriraam and cJ Kamala Kannan a,bUndergraduate Student, Biomedical Engineering,SSN College of Engineering,Chennai

bCenter for Biomedical Informatics and Signal Processing, Dept of Bio- Medical Engineering, SSN College of Engineering, Chennai, India

cNeurology Dept,Sri Ramachandra University,Chennai,India *sriraamn@ssn.edu.in

Abstract Diagnosis of Parkinsons disease (PD) is a challenging problem for medical community. Typically characterized by tremor, PD occurs due to the loss of dopamine in the brains thalamic region that results in involuntary or oscillatory movement in the body. The early stage of the PD is referred as resting tremors, which appears when the muscles are relaxed. It is well known that surface EMG recording provides clinical information on the neuro-physiological characteristics of the tremors. This paper discusses the detection of resting tremors by extracting power spectral density (PSD) features from EMGs. Two methods namely, PSD by Welch and Burgs are applied by configuring the order of the predictors and are then classified using a recurrent neural network model, Elman Neural Network (REN). Experiments are performed using EMG patterns and statistical measures such as mean and maximum of PSD are used to classify the normal and abnormal PD subjects. It is found from the experimental results that the mean value of power spectral density by Burg with recurrent neural network classifier yields a classification accuracy of 95.6%. The proposed work need to be validated with larger datasets for real -time clinical application. Keywords Parkinsons Disease; EMG tremors; power spectral density; recurrent neural network

I. INTRODUCTION Electromyography (EMG), the measure of electrical

activity produced by skeletal muscles, is one of the major diagnostic parameter for detection of Parkinsons disease (PD) [1-3] . The brain is the master controller of the body activities and that includes the motor activities as well. The degeneration of the hypothalamus in the brain leads to very severe complications of which Parkinson's disease(PD) is the most widespread. PD is characterized by muscle rigidity, resting muscular tremor that is very rare for normal subjects, a slowing of motor action (bradykinesia) and a loss of muscular contraction that leads to loss of the entire motor activity (akinesia)[2-4] in extreme cases. Reduced motor activity in this disease makes it detectable with the help of EMG measurements from the patient.

The early indication of the PD is the resting tremor with a trembling or shaking in one of the hands. This is due to the involuntary action of the muscles. This muscle activation is well exploited by investigating the EMGs and several work have been reported in the literature for the diagnosis of PD [6-13]. The alternating properties of the EMG can be exploited

properly, provided appropriate features are extracted. Time and frequency domain features such as frequency spectrum estimation, amplitude and the frequency band in which maximum signal contribution have already been reported [6-13]. In this research study the importance of power spectral information is investigated by using two methods, namely, PSD by Welch and PSD by Burgs. Fig.1 shows the proposed schematic for detection of PD. The optimal features based on statistical means are then classified using Recurrent Neural network model, Elman network. Then the classifier accuracy is estimated based on the network performance in recognizing the true-false positive and negative patterns respectively.

Fig.1 Proposed work for the detection of PD

II. MATERIALS AND METHODS

A. Data Source For experimental study, EMGs are obtained from the

Neurology Department, Sri Ramachandra University, Chennai, India. The subjects under the age group of 20-30 years are selected and recorded under rest and activated motion from the extensor carpi radialis muscle. Resting tremors are recorded from subjects diagnosed by physicians as PD and induced muscular contractions are recorded from normal subjects. All the EMG data are free from artifacts and external power line interference. The EMG recordings are considered for a restricted duration of 30 min with sampling frequency of 100 Hz. Fig.2 shows the sample recordings.

B. Feature Extraction In order to characterize the tremor accurately, the entire

EMG time series is segmented into 1s, say patterns. Furthermore this ensures stationarity of the signal and frequency domain parameters can be extracted subsequently. The PSD of the EMG signal is evaluated through autoregressive Burgs method and Welch method [14-16].

FEATURE SELECTION

BASED ON PSD WELCH / PSD

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PSD based Features

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Input layer Hidden layers Output layer

Context UnitsRecurrent Links

1) PSD using AR Burg: The fundamental method to estimate the distribution of power density across the frequency bands is the Burg method which calculates the PSD by estimating the reflection co-efficients. The major errors that might arise during the evaluation of PSD with this method is the prediction errors. The prediction errors may be forward or backward prediction errors. The application of autoregressive model reduces these errors, while reducing the number of AR parameters to be in accordance with the Levinson-Durbin recursion and producing an optimal combination of both these errors. The input is the sampled EMG data sampled at 1s duration.

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Fig.2 Raw Data Plot for Normal and Abnormal subjects

The output is the estimate of the signal's power spectral density calculated at the signals sampling frequency Fs.

(1) The burg spectrum is advantageous as it always produces a stable model with a satisfactory contribution in reducing the prediction errors.

2) Welch Method:

It is an improved method of estimating the PSD [14-16]. This method is carried out with the sampled EMG data with a quantified amount of overlap between the consecutive segments. The input is divided into N segments each containing X samples overlapping by a fixed number of points. If the overlap is equal to X/ 2, then it is 50% overlap, if the value of X is 0 then there is no overlapping between the input data segments.

This method divides the data into eight segments by default with a maximum of 50% overlap between them and uses a Hamming window. Normally the windowing function affects the computation of the peridogram at the centre of the segment than at the edges, which results in a loss of information. To avoid this loss of information, overlapping of the segments is carried out. Discrete Fourier transform is applied to compute the periodogram of each segment. Then the estimated periodogram is time-averaged, in order to

reduce the variances of individual power estimates. This result is called the Welch estimate. The repetitive information in the output due to the overlapping of the input signals can be minimized by using a non-rectangular window for further processing. The window must be chosen in accordance with the requirement that it reduces the weights allotted to the segment exteriors than to the center.

(2)

III. PERFORMANCE EVALUATION All To detect the PD from the given EMG time series, neural network model is incorporated. A feedback Elman neural network (REN) model is used for the classification. In order to evaluate the statistical importance of the proposed power spectral density features, the predictor order for Welch and Burgs method are varied and mean as well as maximum value of PSD for each segment(1s duration) is calculated. This process will provide the abrupt variations of EMGs for the abnormal cases. The performance of the proposed scheme is evaluated in terms of sensitivity(SE) , specificity (SP) and classification accuracy (CA).

(3)

(4)

(5) where the True Positives refers to correctly detected normal EMG patterns and True Negative refers to correctly detected PDs.

Fig.3 Architecture of Elman neural network

Due to the presence of feedback connections in Elman network from the output to the context nodes, the robust changes in values are reflected and a high accuracy of

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classification can be possibly achieved. Fig.3 shows the model of recurrent Elman neural network [17 ]. The network is configured optimally with hidden neurons =60, tan sigmoid and log sigmoid activation functions for input- hidden and

hidden-output layers respectively, back propogation gradient descent momentum as learning algorithm. For training the network, 2000 EMG patterns are used and 1200 for testing the efficiency of the network. Figs 4 and 5 show the variation of PSD for normal and PD EMGs.

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Fig. 4 Variation of PSD (mean) using Welch Algorithm

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Fig. 5 Variation of PSD (max) using Welch Algorithm Table 1 shows the classifier output using the Elman neural

network. The number of misclassified patterns out of 600 applied patterns are shown.

It can be seen from Table 1 that the classifier performance varies based on the order of the predictor as well as type of PSD estimation algorithm. Amongst the different modes, PSD- Burg (mean) yields the CA of 95.66%. Fig. 6 shows the classifier results obtained using PSD-Welch with REN classifier.

TABLE I CLASSIFICATION RESULTS FOR WELCH USING ELMAN NETWORK

TABLE II CLASSIFICATION RESULTS FOR BURG USING ELMAN NETWORK

Fig.6 Classifier performance using PSD-Welch with Elman

Neural network

IV. CONCLUSIONS This paper discusses the automated detection of resting

tremor characterizing Parkinson disease using power spectral density features with recurrent Elman neural network. The muscular activations were studied using the EMG recordings and mean and maximum of power spectral densities were estimated based on the Welch and Burgs algorithms. It can be seen from the experimental results that the proposed scheme

Order of predictor PSD Welch(mean)

PSD Welch(max)

SE SP CA (%)

SE SP CA (%)

P=2 52 112 86.33 52 112 86.3

P=5 46 69 90.41 102 225 71.9

Order of Predictor

PSD Burg(mean) PSD Burg(max)

SE SP CA (%) SE SP CA (%)

P=2 58 233 81.81 177 325 68.63

P=5 18 34 95.66 66 126 84

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yields promising classification results. To validate the efficiency for clinical usage, attempts are being made to test with lager datasets.

ACKNOWLEDGMENT The research results presented here are based on data

obtained from the Neurology Department, Sri Ramachandra University, Chennai, India.

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