**5.3. Methods of signal analysis**

20 Will-be-set-by-IN-TECH

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kr

k<sup>l</sup>

%RECr

%RECl

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Patient 4

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UPDRS

First eigenvector

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**Figure 12.** The first PCs (*θj*(1)) and UPDRS -motor scores of nine PD patients with medication off, and two and three and hour hours after taking the medication. The first eigenvector (bottom right).

especially to patients with problems in performing movement tasks [29]. Therefore, the analysis of both kind of muscle contractions is essential when quantifying motor impairment

In studies [30, 31], we developed methods for quantifying the effects of treatment in PD on the basis of surface EMG and kinematic measurements and analysis. The results of the study [30] show that the measured EMG and acceleration signals of 12 out of 13 PD patients were more similar with the signals of the healthy subjects with DBS on than with DBS off. This result indicates that it is possible to detect DBS-induced improvements in the neuromuscular

In [31], the EMG signals of eight out of nine PD patients changed into less spiky and the acceleration recordings into more complex after taking the medication. A reverse phenomenon in the signal characteristics was observed 3–4 hours after taking the medication for seven out of nine patients. This result indicates that it is possible to detect

θ<sup>j</sup> (1)

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and motor function of PD patients by using the developed analysis approach.

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Patient 1

Computational Intelligence in Electromyography Analysis –

Patient 3

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UPDRS

**5.2. Quantification of the effects of treatment**

10 20 UPDRS

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in PD.

θ<sup>j</sup> (1)

240

θ<sup>j</sup> (1)

We extracted a large number of features from the EMG and acceleration signals of PD patients and healthy subjects in [28–32] and chose the most effective features for characterizing PD and the effects of treatment into the feature vectors for deeper analysis. The chosen EMG features were not conventional EMG parameters but they were based on nonlinear dynamics, signal morphology, wavelets and EMG-acceleration coherence. Previously, there have been only one [14] or few other studies, in which a method of nonlinear dynamics has been used for studying EMGs of PD patients. Our studies [30, 31] are the only studies that have analyzed the effects of PD treatment (DBS and medication) by using methods of nonlinear dynamics for EMG.

All of the studies [28–32] were based on an innovative way of combining the PC-based approach with the selection of feature vectors instead of analyzing the statistics of single signal parameters. The PC-based approach provided a better discrimination between the subjects by capturing essential information in the combination of variables. With the PC-based approach, it was possible to examine the effects of treatment in a feature space on an individual level.

Few things about signal quality and electrode placement should be kept in mind when analyzing the EMG signals with the proposed analysis methods. First, the EMG signal amplitude is relatively low and the signal is sensitive to noise that is coming from other electrical sources. This noise may affect the calculated signal parameters. Therefore, the noise should be eliminated already during the measurements whenever it is possible. Another thing is that sometimes a large MU is firing constantly and dominantly in the proximity of the recording electrode causing recurring impulse-like patterns into the EMG signal. In that case, a better placement of recording electrodes would be advisable.

In PD patients with DBS, the stimulator causes artifacts into the EMG signal. The DBS artifact and its filtering may affect the calculated signal parameters. Previously, the DBS artifact has been removed from the EMG signal by low-pass filtering the rectified signal with a low (20–60 Hz) cut-off frequency [21, 41, 42, 51, 52]. In our study [30], we low-pass filtered the EMG signal with the 110 Hz cut-off frequency. Our aim was to remove the DBS artifact from the EMG as effectively as possible without removing important information and to perform the filtering in the same way for all subjects in order to get comparable results.

#### **5.4. Conclusions**

In this chapter, we presented several approaches for feature extraction from surface EMG and acceleration signals and for discrimination between PD patients and healthy subjects on the basis of the extracted signal features. The presented discrimination approaches were developed in our studies [28, 29, 32]. By using the developed approaches, we could discriminate 72-80 % of PD patients from 82-90 % of healthy subjects depending on the analyzed signal features and the muscle contraction type. These percentages can be regarded as promising because it is known that the PD diagnostics can be difficult. Clinicopathological studies from the UK and Canada have shown that the disease is diagnosed incorrectly in about 25 % of cases [48]. On the basis of our discrimination results, further research and clinical studies are suggested for evaluating the sensitivity of the developed approaches in patients with different types of PD and in patients with early stages of PD. In addition, the ability of EMG and acceleration signal features in discriminating between PD patients and other patients with similar symptoms should be studied.

**6. References**

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In this chapter, we presented two approaches for quantifying the effects of PD treatment (medication and DBS) on the basis of the extracted EMG and acceleration signal features. The presented approaches were developed in our studies [30, 31]. By using the developed approaches, we could detect DBS- and medication-induced improvements in the neuromuscular and motor function of PD patients. This result is encouraging because the widely used method for evaluating the efficacy of PD treatment is subjective. However, the sensitivity of the developed approaches should be quantified with a larger number of PD patients.

The need for finding objective methods for PD diagnosis and for quantifying the disease progression and the efficacy of treatment is well known [2, 11, 24]. We hope that our results [28–32] can help in creating a practical method for quantifying motor impairment in PD and the effects of treatment on individual PD patients. However, in order to be more sensitive than the traditional methods, it is probable that a combination of several objective methods will be needed for PD.

#### **5.5. Future directions**

There is currently a lot of effort for determining objective methods and characteristics for PD [2, 11, 24]. One important goal of current research is to determine criteria for the pre-motor and pre-clinical phases of PD [39]. In surface EMG studies, the sensitivity of surface EMG signal features in detecting PD patients before the actual diagnosis of PD should be studied. It will be important to analyze differences in the signal characteristics between PD patients and other patients with similar symptoms. These other similar diseases form currently a significant reason for the wrong diagnosis of PD [17]. It has been observed that surface EMG and kinematic measurements can provide information about the effects of PD treatments (medication and DBS). The ability of these measurements in helping the optimal adjustment of these treatments should be evaluated.
