**5. Discussion**

There is a need for finding objective methods for Parkinson's disease for improving the diagnostic accuracy, for enabling earlier diagnosis, and for quantifying the disease progression and the efficacy of treatment [2, 11, 24]. Surface EMG and the kinematic measurements may be potentially useful methods for quantifying the motor impairment in PD and the effects of


**Table 3.** Total UPDRS -motor scores and the distances from the center of healthy subjects with DBS on and off.

treatment. However, the EMG signals of PD patients are characterized by spikes and bursts that are not effectively captured with conventional amplitude- and spectral-based parameters of EMG. Therefore, more novel methods of EMG analysis are needed for PD.

#### **5.1. Discrimination between patients and healthy subjects**

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detailed analysis that the method is most sensitive to PD with associated tremor. In Figure 11, one patient is farther from the healthy subjects with DBS on than with DBS off. This patient has higher tremor (acceleration signal) amplitude and regularity and less complex EMG recordings (higher %REC and lower *D*2) with DBS on than with DBS off. For that patient,

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θ<sup>j</sup> (3)

**Figure 11.** The third PCs *θj*(3) with respect to the first PCs *θj*(1) of 13 healthy subjects (+) and 13 PD patients with DBS on (◦) and off (�). The patients are divided into two figures, but the healthy subjects are the same in both figures. The DBS on- and off-states of each patient are connected with a line.

In [31], the first PC worked best in characterizing the effects of medication. The first PCs and the total UPDRS -motor scores in each medication condition for each patient are presented in Figure 12. One can observe that the total UPDRS -motor scores decrease (motor symptoms are relieved) with medication for all patients. Correspondingly the first PCs decrease with medication for eight out of nine patients. By examining the first eigenvector in Figure 12 one can realize that the reduction in the first PC indicates reduction in the parameters *k* (less spiky EMG), %REC (less recurring patterns) and RMS (lower tremor amplitude), and increase in the parameter SampEn (more complex tremor). The severity of motor symptoms (UPDRS -motor score) starts to increase 2–3 hours after medication for all patients, which indicates that the efficacy of medication starts to weaken 2–3 hours after medication. Correspondingly, the first PCs start to increase 2–3 hours after medication for seven out of nine patients. The UPDRS -motor scores and the first PCs do not start to increase at the same time for all patients, which

There is a need for finding objective methods for Parkinson's disease for improving the diagnostic accuracy, for enabling earlier diagnosis, and for quantifying the disease progression and the efficacy of treatment [2, 11, 24]. Surface EMG and the kinematic measurements may be potentially useful methods for quantifying the motor impairment in PD and the effects of

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Other PD patients and healthy sub jects

the measurement results contradict the subjective clinical scores.

Two PD patients and healthy sub jects

Computational Intelligence in Electromyography Analysis –

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θ<sup>j</sup> (1)

indicates that these scores do not measure exactly the same thing.

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**5. Discussion**

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We have developed methods for discriminating between PD patients and healthy subjects on the basis of surface EMG and kinematic measurements and analysis in [28, 29, 32]. One developed approach was based on analyzing the surface EMG signal morphology [32]. One approach was based on analyzing isometric [28] and one approach on analyzing dynamic muscle contractions [29]. Principal components were used in each approach for discrimination between subjects. All methods were tested with the measured data. The obtained discrimination rates were 72 % for patients and 86 % for healthy subjects on the basis of surface EMG signal morphology, 76 % for patients and 90 % for healthy subjects on the basis of isometric EMG and acceleration recordings, 73 % for patients and 82 % for healthy subjects on the basis of elbow flexion movements, and 80 % for patients and 87 % for healthy subjects on the basis of elbow extension movements. These percentages predict the sensitivities and specificities of the methods in the subject groups that were studied.

The best discrimination rates between patients and healthy subjects were obtained by analyzing the EMG and acceleration signals measured during the isometric contraction and elbow extension movements [28, 29]. In fact, it has been observed previously, that the elbow extension movements are more impaired than the flexion movements of PD patients [33]. The isometric approach was most sensitive to patients with associated tremor [28] and the dynamic approach to patients with various motor symptoms (rigidity, bradykinesia and tremor) and

20 Will-be-set-by-IN-TECH 240 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>21</sup>

medication-induced changes in the neuromuscular and motor function of PD patients by

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Feature Extraction Methods for Studying Surface

Electromyography and Kinematic Measurements in Parkinson's Disease

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,

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

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

a better placement of recording electrodes would be advisable.

filtering in the same way for all subjects in order to get comparable results.

using the developed methods.

**5.4. Conclusions**

**5.3. Methods of signal analysis**

**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 PD.

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

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

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 medication-induced changes in the neuromuscular and motor function of PD patients by using the developed methods.
