Abdulnasir Hossen

Department of Electrical and Computer Engineering Sultan Qaboos University, Oman

P.O.Box 33, Al-Khoud 123 Muscat, oman

abhossen@squ.edu.om

Abstract – A novel discrimination method of Parkinsonian tremor from essential tremor is presented in this paper. The method

uses the approximate power spectral density of specific sub-bands, which is estimated using a soft-decision wavelet-based decomposition of EMG and accelerometer signals. Selection of specific sub-bands of the spectrum of two EMG signals and accelerometer signal has been implemented to provide the neural network with its proper inputs. Two sets of data, training set and test set, which are recorded in the department of Neurology of the University of Kiel-Germany, are used in this work. The training set, which consists of 21 essential tremor subjects and 19 Parkinson disease subjects, is used to train the neural network of type feed-forward back-propagation. The test set, which consists of 20 essential tremor subjects and 20 Parkinson disease subjects are used to test the performance of the discrimination system. A best discrimination efficiency of 87.5% has been obtained in this work.

Keywords – Parkinsonian Tremor, Essential Tremor, Discrimination, Wavelet-Decomposition, Power Spectral Density, EMG, Accelerometer, Artificial Neural networks

I. INTRODUCTION Essential tremor (ET) is a disease with the tremor being

the main symptom, while Parkisonian Disease (PD) is a

neuro-degenerative disorder caused by the loss of

dopamine receptors which control the movement of the

body. The available clinical diagnosis methods have

difficulties to discriminate between Essential tremor (ET)

and the tremor in Parkinson’s disease (PD) [1] especially

at early stages of the diseases. Clinically, dopamine-

transporter imaging is used which identifies the

asymmetric loss of dopaminergic neurons in the PD [2-3].

However, this requires SPECT (Single Photon Emission

Computer Tomography) technology, with injection of a

radioactivity-labeled tracers, and it needs a considerable

amount of time and can be performed only in very few

centers. Therefore, simple signal processing techniques

are very useful to discriminate between the two tremors.

The tremor time-series, that can be recorded in laboratory

by accelerometry and surface EMG, is mostly used in a

clinical setting. The spectral analysis of these signals has

proven useful to distinguish between these two tremors

[4].

The soft-decision power spectral estimation technique

based on sub-band decomposition and wavelet-

decomposition [5] was implemented successfully as a

non-invasive tool for identification of patients with

obstructive sleep apnea and congestive heart failure [6-8].

In [9], the wavelet-based soft-decision technique succeeds

in obtaining 85% accuracy of discrimination of ET from

PD using both accelerometer and two surface EMG

signals EMG1 and EMG2 recording the extensor and

flexor carpi-ulnaris muscles, respectively. The 85%

accuracy was obtained as a voting between three results

taken from the three different signals of the same data

used in this work.

In [10], a neural network of 16 inputs carrying the

power spectral density of the first 16 bands out of 256

Selection of Wavelet-Bands for Neural Network Discrimination of Parkinsonian Tremor from

Essential Tremor

978-1-4673-1260-8/12/$31.00 ©2012 IEEE 37

bands of the accelerometer and EMG signals, used to

discriminate between ET and PD using a simulated set of

2000 subjects. The efficiency of the neural network was

91.6%.

In this work, the same principle of [10] is to be

used, but on the actual data on the aim to raise the 85%

efficiency obtained using voting of results in [9]. Specific

bands are to be used with each one of the three different

signals.

The organization of the paper is as follows:

In section 2, both the trial data and test data are described.

Section 3 contains the main idea of the soft-decision

wavelet-based technique. The results of implementation

of the ANN on test data and discussion of the results are

given in section 4. Section 5 contains conclusions of the

presented work.

II. DATA In this study, 39 PD and 41 ET subjects were analyzed,

respectively. All patients are suffering from a moderate to

severe postural tremor. The data, which is collected in the

department of Neurology of the University of Kiel-

Germany, is divided into two sets and to be used for

training (trial set) and for testing (test set), respectively.

The mean age, sex and disease duration of the PD patients

were compared with the ET patients for the trial and test

data in Tables 1 & 2. All patients gave informed consent,

and the study was approved by the local ethics

committees at the University of Kiel.

III. ESTIMATION OF POWER SPECTRAL DENSITY USING WAVELET-BASED SOFT-DECISION

TECHNIQUE

The soft-decision wavelet-based algorithm [5] can be

used to estimate the power spectral density as below:

1) The wavelet-decomposition is performed on the

signal and repeated with all branches up to a certain

stage (in our case up to stage 8 to have 256 stages).

2) All estimator results up to this stage are stored, and

their outputs are given a probabilistic interpretation

by assigning a probability measure to each path (i.e.,

frequency band) to bear the primary information.

Table 1. Trial data-size, age, gender, and disease duration

distribution of both PD and ET subjects

PD ET

Number of Patients 19 21

Mean Age (Range) 64.54 (40-90)

Years

63.24 (27-

94) Years

Gender

(Male/Female) 11/8 12/9

Mean Disease

Duration 16.4 Years 34 Years

Table 2. Test data-size, age, gender, and disease duration

distribution of both PD and ET subjects

PD ET

Number of Patients 20 20

Mean Age (Range) 68.22 (52-85)

Years

64.52 (32-86)

Years

Gender

(Male/Female) 12/8 11/9

Mean Disease

Duration 15.3 Years 29 Years

3) If J(L) is the assigned probability of the input signal

being primarily low-pass, the number J(H) = 1- J(L)

is the probability that the signal is primarily high-

pass. One simple way to make the probability

assignments is to use the ratio of the number of

positive comparisons between |g(n)| and |h(n)| to the

total number of comparisons for a given stage, where

g(n) and h(n) are the low-pass and high-pass filtered

sequences, respectively.

4) At the following stage, the resulting estimate can be

interpreted as the conditional probability of the new

input sequence containing primarily low (high)

frequency components, given that the previous

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branch was predominantly low (high)-pass. Using

this reasoning and laws of probability, the

assignments for the probability measure of the

resulting sub-bands should be made equal to the

product of the previous branch probability and the

conditional probability estimated at a given stage.

The higher the probability value of some band, the

higher is its power-spectral content! So, after m-stage

decomposition, a staircase approximation of the PSD

is obtained, if the 2m probabilities are plotted. For

m=8 and with a sampling frequency of 800 Hz, each

of the resulted 256 sub-bands covers 400/256 Hz.

IV. THE ARTIFICIAL NEURAL NETWORK

A neural network of the type feed-forward back-

propagation (referred to as a multi-layer perceptron) is

used in [10] to discriminate between ET and PD. This

network consists of three layers. The first layer (input

layer) accepts 16 input signals (B1 to B16) (power

spectral densities of the first 16 different bands of either

(EMG1 or EMG2 or Accelerometer) and redistributes

these signals to all neurons in the second layer. Actually,

the input layer does not include computing neurons. The

second layer (hidden layer) has three hyperbolic tangent

sigmoid "tansig" neurons. Neurons in the hidden layer

detect the features; the weights of the neurons represent

the features hidden in the input patterns. These features

are then used by the third layer (output layer) in

determining the output pattern. This third layer has one

linear "purelin" neuron in our approach. The back

propagation network training function is "trainbar".

Number of epochs is 1000 and the training rate is 0.001.

The whole network has 16 input nodes corresponding

to the 16 key features and a single binary output that

corresponds to one out of the two types under

classification (ET or PD). Figure 1 shows the three-layer

back-propagation neural network used in the training

stage [10]. The network is trained with the training data

(1000 ET and 1000 PD) obtained from the original trial

data (21 ET) and (19PD). The network is then tested with

the test data (1000 ET and 1000 PD ) obtained from the

original test data (20 ET and 20 PD). The accuracy of the

network was found to be 91.6% on the simulated data.

In this paper, the ANN is trained with the actual trial

small size of data, that consists of 21 ET subjects and 19

PD subjects. The network is then tested with the actual

test small size of data, which consists of 20 ET and 20 PD

subjects. The accuracy (ability to identify both ET and PD

subjects), the specificity (ability to identify ET subjects),

and the sensitivity (ability to identify PD subjects) are

listed in Table 3, for each of the three signals (with a 16

input neural network) and for combination of all signals

(with a 48 input neural network).

Table 3. Results obtained from original test data, using

first 16 bands

Signal Specificity Sensitivity Accuracy

Acc. 55% 55% 55%

EMG1 60% 80% 70%

EMG2 95% 15% 55%

All Signals 75% 60% 67.5%

In this section, four specific bands are to be used with

each one of the three signals (Acc., EMG1, EMG2). The

network is trained with 4 inputs at each case, and with 12

inputs in case of using all signals in the training phase.

Then, the network is tested with the actual test small size

of data, which consists of 20 ET and 20 PD subjects. The

specificity, sensitivity, and accuracy of this network are

listed in Table 2. It can be concluded from the table, that

if we use 12 inputs neural network with specific four

bands for each signal, the discrimination accuracy is

found to be 85%. If we do another modification in the

network by increasing the number of neurons of the

second layer to 5, the efficiency will be improved to

87.5%. It is very important to mention that the selection

of the 4 bands associated with each signal is determined

from discriminating the training data according to the

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power spectral density of each band and then selecting the

best four bands to be used with each signal.

Table 4. Results obtained from original test data, using

selective bands

Signal & bands Specificity Sensitivity Accuracy

Acc.

[2, 3, 14, 16]

70%

55%

62.5%

EMG1

[6, 8, 11, 15]

85%

75%

80%

EMG2

[3, 6, 8, 9]

70%

60%

65%

All Signals

3 neurons

85%

85%

85%

All Signals

5 neurons

90%

85%

87.5%

V. CONCLUSIONS A new improvement in the neural network approach for

discriminating ET and PD subjects is obtained by using

specific bands with each of the three signals

(Accelerometer, EMG1, EMG2) as inputs to the neural

network. A neural network with 12 inputs, 5 neurons, and

one binary output, trained with the original small size of

train data and tested with the original size of test data

yields 87.5% accuracy of discrimination between ET and

PD.

ACKNOWLEDGEMENTS

The author would like to thank the department of

Neurology of the University of Kiel-Germany for

providing the data used in the work. Many thanks for

Sultan Qaboos University for the support in a form of

internal research grant provided for the author for doing

the research and attending the conference.

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