**2. Analysis EMG signal morphology in PD**

2 Will-be-set-by-IN-TECH

However, none of the proposed methods is widely used for PD. The validation of new methods for clinical use takes time. In order to be more sensitive than the traditional methods

Surface electromyography (EMG) and kinematic measurements are non-invasive and relatively simple and cost-effective methods for quantifying neuromuscular function and movement. Therefore, these methods may be suitable for quantifying objectively the motor impairment in PD and the effects of treatment. A few new technologies based on kinematic sensors have been recently commercialized for measuring motor symptoms of PD. The kinematic measurements provide information about human movements. However, it is possible that surface EMG provides earlier or more direct information about PD than the sole

Several studies have analyzed the surface EMG and kinematic signals of PD patients in comparison to the signals of healthy subjects and aimed to correlate the most significant findings with the clinical rating scales. Differences between patients and healthy subjects have been observed in the tremor-EMG coherence [50], in the cortico-muscular coherence [37] and in the muscle activation patterns during limb movements [13, 26, 35]. In the gait characteristics, differences have been observed in the gait speed and stride length, in the arm

Several studies have evaluated effects of PD treatment (medication and DBS) on the basis of EMG and kinematic measurements. It has been observed that the medication and DBS may modify the tremor amplitude, regularity and frequency [4, 41, 42], movement speed [3, 8, 34, 40, 44, 49, 51, 52], joint kinetics and muscle activation during movements [55], EMG burst patterns during movement [34, 51, 52] and the cortico-muscular coherence [25, 37]. There is currently a lot of interest for characterizing EMG and kinematic signals of PD patients. However, many studies have analyzed the EMG signals of PD patients by using conventional amplitude- and spectral based methods. More information about PD could be extracted from the EMG signals by using also more modern methods of signal analysis, by analyzing sets of

EMG signals are impulse-like waveforms because they consist of motor unit (MU) action potentials. The level of MU synchronization is increased in PD [14, 50], which appears as an increased number of recurring spikes and bursts in the EMG signals. Therefore, there is important information about PD in the morphology of the EMG signal and in the recurring signal patterns. It has been observed that the conventional EMG signal parameters (amplitudes and the mean and median frequencies) are not effective in capturing impulse-like structures [23]. Therefore, more modern methods of signal analysis are needed for analyzing

**1.3. Our approach for studying surface EMG and kinematic measurements in PD** In order to extract PD-related information from the surface EMG signals effectively, we proposed specific methods based on signal morphology, nonlinear dynamics and wavelets for analyzing the EMG signals of PD patients in [28–32]. One aim of those studies was to develop

signal features and by analyzing the signal characteristics also on individual level.

it is probable that a combination of several methods will be needed for PD. [2, 11, 24]

**1.2. Surface electromyography and kinematic measurements in PD**

and leg swing and in the muscle activation patterns of gait [5–7, 36, 43].

kinematic measures based on movement.

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the EMG signals of PD patients.

EMG signal is a sum of MU action potentials at a given location and therefore it is an impulse-like waveform. The EMG signals of PD patients are characterized by recurring spikes and bursts (see Figure 1) that are likely caused by the increased level of MU synchronization. Important information about PD is in the EMG signal morphology and in the recurring signal patters.

In [32], the EMG signal morphology of 25 PD patients and 22 healthy subjects was analyzed by using sample histograms and crossing rate (CR) expansions. The analyzed EMG signals were measured during the isometric contraction of biceps brachii (BB) muscles. During the task, subjects were asked to hold their elbows at a 90° angle with their palms up. The measurements were performed by using the ME6000 -biosignal monitor (Mega Electronics Ltd., Kuopio, Finland) and disposable Ag/AgCl electrodes (Medicotest, model M-00-S, Ølstykke, Denmark) in bipolar connection. The sampling rate was 1000 Hz.

Typical EMG signals of one healthy subject and one PD patient are presented in Figure 1. One can observe that the EMG signal of the patient contains recurring EMG bursts while the EMG signal of the healthy subject does not.

#### **2.1. Feature extraction by using sample histograms and CR expansions**

Sample histograms were extracted from the scaled (between -1 and 1) EMG signals with 200 bins and the CR expansions from the scaled EMGs as the number of crossings at given threshold levels (201 threshold levels). An example of the sample histogram and the CR expansion for the healthy subject and for the PD patient are presented in Figure 1. One can observe that the sample histogram of the patient is sharper and the CR expansion narrower than those of the healthy subject.

#### **2.2. Discrimination analysis between subjects**

The calculated sample histograms and CR expansions of PD patients (with medication on) and healthy subjects were used as high-dimensional feature vectors for discrimination analysis between subjects. The PC-based approach was used for decreasing the dimensionality of the

4 Will-be-set-by-IN-TECH 224 Computational Intelligence in Electromyography Analysis – A Perspective on Current Applications and Future Challenges Feature Extraction Methods for Studying Surface Electromyography and Kinematic Measurements in Parkinson's Disease <sup>5</sup>

**Figure 1.** EMG signals of one healthy subject (top) and one PD patient (bottom). The sample histograms and crossing rate expansions of the healthy subject and the PD patient.

feature vectors and the discriminant analysis of subjects was performed in a two-dimensional feature space.

In the PC-based approach [19], each feature vector *zj* <sup>∈</sup> **<sup>R</sup>***Np* is modeled with a linear model

$$z\_j = H\theta\_j + v\_j. \tag{1}$$

PC approach are illustrated in Figure 2. In addition, the correlation matrix and the three eigenvectors corresponding to the three largest eigenvalues are presented in the same figure.

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**Figure 2.** The feature vectors of one PD patient (black) and one healthy subject (gray) in the augmented PC approach (top left). Three eigenvectors corresponding to the three largest eigenvalues (left). The data correlation matrix (top right). The third PCs *θj*(3) with respect to the first PCs *θj*(1) of 22 healthy subjects

The correlation matrix in Figure 2 contains four white areas with high correlation. The white area in the top left corner describes correlations between the CR expansion values. The white area in the bottom right corner describes correlations between the sample histogram values. The non-diagonal white areas describe cross-correlations between the CR expansion values

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The eigenvectors in Figure 2 can be interpreted as follows:

(+) and 25 PD patients (◦) (bottom right).

and the sample histogram values.

Feature vectors

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In the linear model, *<sup>H</sup>* = [*φ*<sup>1</sup> *<sup>φ</sup>*<sup>2</sup> ... *<sup>φ</sup>K*] <sup>∈</sup> **<sup>R</sup>***Np*×*<sup>K</sup>* is the model matrix that contains the basis vectors *<sup>φ</sup><sup>k</sup>* <sup>∈</sup> **<sup>R</sup>***Np* in its columns. Vector *<sup>θ</sup><sup>j</sup>* <sup>∈</sup> **<sup>R</sup>***<sup>K</sup>* contains the model weights and *vj* <sup>∈</sup> **<sup>R</sup>***Np* the model error for the *j*'th feature vector. The basis vectors *φ<sup>k</sup>* are selected to be the eigenvectors of the data correlation matrix

$$R\_z = \frac{1}{M} \sum\_{j=1}^{M} z\_j z\_j^T \, \text{\,\,\,}\tag{2}$$

where *<sup>M</sup>* is the total number of feature vectors and (·)*<sup>T</sup>* denotes the transpose. Because the eigenvectors are orthonormal, the least squares solution for the model weights *θ<sup>j</sup>* is of the form

$$
\hat{\theta}\_{\dot{\jmath}} = (H^T H)^{-1} H^T z\_{\dot{\jmath}} = H^T z\_{\dot{\jmath}}.\tag{3}
$$

These weights are called the principal components. By choosing *K* (*K < Np*) eigenvectors corresponding to *K* largest eigenvalues for modeling, the best *K*-dimensional orthogonal approximation for the data set is obtained. The PCs are the new uncorrelated features and they can be used for discriminating between subjects in a low-dimensional feature space.

In [32], three feature vectors were formed for each subject: one containing the EMG sample histogram, one containing the CR expansion and one containing both of them (augmented PC approach). Thus, the original dimensionality of the feature vectors was reasonably high (*Np* ≥ 200). The feature vectors of one PD patient and one healthy subject in the augmented PC approach are illustrated in Figure 2. In addition, the correlation matrix and the three eigenvectors corresponding to the three largest eigenvalues are presented in the same figure.

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**Figure 1.** EMG signals of one healthy subject (top) and one PD patient (bottom). The sample histograms

feature vectors and the discriminant analysis of subjects was performed in a two-dimensional

In the PC-based approach [19], each feature vector *zj* <sup>∈</sup> **<sup>R</sup>***Np* is modeled with a linear model

In the linear model, *<sup>H</sup>* = [*φ*<sup>1</sup> *<sup>φ</sup>*<sup>2</sup> ... *<sup>φ</sup>K*] <sup>∈</sup> **<sup>R</sup>***Np*×*<sup>K</sup>* is the model matrix that contains the basis vectors *<sup>φ</sup><sup>k</sup>* <sup>∈</sup> **<sup>R</sup>***Np* in its columns. Vector *<sup>θ</sup><sup>j</sup>* <sup>∈</sup> **<sup>R</sup>***<sup>K</sup>* contains the model weights and *vj* <sup>∈</sup> **<sup>R</sup>***Np* the model error for the *j*'th feature vector. The basis vectors *φ<sup>k</sup>* are selected to be the eigenvectors

> *M* ∑ *j*=1 *zjz<sup>T</sup>*

where *<sup>M</sup>* is the total number of feature vectors and (·)*<sup>T</sup>* denotes the transpose. Because the eigenvectors are orthonormal, the least squares solution for the model weights *θ<sup>j</sup>* is of the form

These weights are called the principal components. By choosing *K* (*K < Np*) eigenvectors corresponding to *K* largest eigenvalues for modeling, the best *K*-dimensional orthogonal approximation for the data set is obtained. The PCs are the new uncorrelated features and they can be used for discriminating between subjects in a low-dimensional feature space.

In [32], three feature vectors were formed for each subject: one containing the EMG sample histogram, one containing the CR expansion and one containing both of them (augmented PC approach). Thus, the original dimensionality of the feature vectors was reasonably high (*Np* ≥ 200). The feature vectors of one PD patient and one healthy subject in the augmented

*Rz* <sup>=</sup> <sup>1</sup> *M*

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and crossing rate expansions of the healthy subject and the PD patient.

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*zj* = *Hθ<sup>j</sup>* + *vj*. (1)

*θ<sup>j</sup>* = (*HTH*)−1*HTzj* = *HTzj*. (3)

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**Figure 2.** The feature vectors of one PD patient (black) and one healthy subject (gray) in the augmented PC approach (top left). Three eigenvectors corresponding to the three largest eigenvalues (left). The data correlation matrix (top right). The third PCs *θj*(3) with respect to the first PCs *θj*(1) of 22 healthy subjects (+) and 25 PD patients (◦) (bottom right).

The correlation matrix in Figure 2 contains four white areas with high correlation. The white area in the top left corner describes correlations between the CR expansion values. The white area in the bottom right corner describes correlations between the sample histogram values. The non-diagonal white areas describe cross-correlations between the CR expansion values and the sample histogram values.

The eigenvectors in Figure 2 can be interpreted as follows:

Computational Intelligence in Electromyography Analysis –

	- The first eigenvector is the best mean-square fit for the feature vectors of all subjects. Thus, it is similar to the mean of all feature vectors. Therefore, the first PC describes the amplitude of the histogram and the CR expansion with respect to the mean of all subjects.
	- The second eigenvector is the best mean-square fit for the residual of the first fit. The second eigenvector describes variations in the peaks (modes) of the histograms and CR expansions of all subjects.

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**Figure 3.** EMG and acceleration recordings of one PD patient (left) and one healthy subject (right)

the differences between the patient and the healthy subject are not as pronounced.

**3.2. Feature extraction from EMG and acceleration signals**

recording, that the EMG signal of the PD patient differs from the EMG signal of the healthy subject by containing recurring EMG bursts and the acceleration signal by containing regular high-amplitude oscillation. This oscillation is likely due to the resting and postural tremor. It is observed in the dynamic recording, that the EMG signal of the PD patient is characterized by recurring spikes and the acceleration recording by containing high-amplitude oscillation during the extension phases of the movement. The oscillation in the acceleration signal (which was high-pass-filtered with 2 Hz as cut-off frequency) is likely due to muscle rigidity and kinetic tremor (tremor that occurs during movement). In the flexion phases of the movement,

It was observed in [23] and [28, 29] that the conventional amplitude- and spectral-based EMG parameters (root mean square value and median frequency) are not effective in characterizing the EMG signals of PD patients in comparison to the signals of the healthy subjects. Therefore, we extracted a set of other PD characteristic signal features from the isometric [28] and dynamic EMG and acceleration recordings [29]. These parameters are detailed in Table 1 and they were calculated as epoch averages from the isometric EMG and acceleration signals and

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as time-varying from the dynamic signals.

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• The third eigenvector models variations in the heights and widths of the histograms and CR expansions in the whole data set.

The rest of the eigenvectors contain information about higher frequencies of the data and do not interest us in this case. The biggest differences between patients and healthy subjects were found in the third PC and some differences were observed in the first PC. Therefore, the discrimination between subjects was performed with respect to the third and the first PC.

#### **2.3. Results**

A linear discriminant was used in [32] for discriminating between the subjects in the two-dimensional feature space that was spanned by the third and the first PCs. The best discrimination results were obtained by using the augmented PC approach (see results in Figure 2). According to the results, 72 % of PD patients can be discriminated from 86 % of healthy subjects on the basis of EMG signal morphology.
