**4. PC-based approaches for quantifying effects of treatment**

In addition to the discrimination analysis between subjects, the principal component -based approach can be used for quantifying the effects of treatment. In [30, 31], we aimed to develop objective methods for quantifying effects of PD treatment (DBS and medication) on the basis of surface EMG and acceleration measurements and analysis.

#### **4.1. EMG and acceleration measurements for quantifying effects of treatment**

In [30], the PC-based approach was used for quantifying the effects of DBS treatment on the basis of a set of EMG and acceleration signal features. In total, the measurement data from 13 PD patients with DBS and 13 healthy subjects were analyzed. Measurements were performed

#### A Perspective on Current Applications and Future Challenges Feature Extraction Methods for Studying Surface Electromyography and Kinematic Measurements in Parkinson's Disease <sup>15</sup> 235 Feature Extraction Methods for Studying Surface Electromyography and Kinematic Measurements in Parkinson's Disease

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Computational Intelligence in Electromyography Analysis –

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feature space (*θj*(2)+*θj*(5) with respect to *θj*(1)). The three clusters O1, O2 and O3.

not be discriminated from the healthy subjects, had mild motor symptoms of PD.

In addition to the discrimination analysis between subjects, the principal component -based approach can be used for quantifying the effects of treatment. In [30, 31], we aimed to develop objective methods for quantifying effects of PD treatment (DBS and medication) on the basis

**4.1. EMG and acceleration measurements for quantifying effects of treatment**

In [30], the PC-based approach was used for quantifying the effects of DBS treatment on the basis of a set of EMG and acceleration signal features. In total, the measurement data from 13 PD patients with DBS and 13 healthy subjects were analyzed. Measurements were performed

**4. PC-based approaches for quantifying effects of treatment**

of surface EMG and acceleration measurements and analysis.

SampEnr

SampEnl

**Figure 7.** Mean ± SD values of normalized signal features for the patient group (◦) and for the healthy subject group (+) (left). The cluster analysis of 42 PD patients (◦) and 59 healthy subjects (+) in the

in flexion and in extension are presented in Figure 8. The results show that parameters %REC and *P*acc tend to be higher and parameters SampEn and *W*max lower for patients than for healthy subjects both in flexion and in extension. That is, the EMGs of the patients tend to contain more recurring patterns than the EMGs of the healthy subjects and the EMG wavelet power tends to be more spread for patients. The acceleration signals of the patients tend to be of higher amplitude and more regular than the acceleration signals of the healthy subjects. The cluster analysis of subjects was performed in a two-dimensional feature space that was spanned by the second PC and the first PC by using the k-means algorithm. The results are presented in Figure 8. According to the results, the method can discriminate 80 ± 1 % of the patient extension movements from 87 ± 1 % of the extension movements of healthy subjects, and 73 ± 1 % of the patient flexion movements from 82 ± 1 % of the flexion movements of healthy subjects. The leave-one-out method was used for validation. The patients, that could

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**Figure 8.** Mean ± SD values of normalized signal features for the patient group (◦) and for the healthy subject group (+) in flexion and in extension (top). The cluster analysis of 49 PD patients (◦) and 59 healthy subjects (+) in the feature space (*θj*(2) with respect to *θj*(1)).

during the isometric contraction of BB muscles (see section 3.1) and they were performed once for the healthy subjects and twice for the patients: with DBS on (stimulator was turned on) and with DBS off (stimulator was turned off). Ninth order Butterworth low-pass filter with 110 Hz cutoff was used for removing the DBS artifact from the EMG signals. The low-pass filtering was performed similarly for all subjects (patients and healthy subjects). The UPDRS -motor examination was performed for each patient with DBS on and with DBS off. The measured signals of one PD patient with DBS on and off are presented in Figure 9. One can observe that the EMG signal of the patient contains recurring EMG bursts and the acceleration signal high-amplitude tremor with DBS off but not with DBS on.

In [31], the PC-based approach was used for quantifying the effects of anti-parkinsonian medication on the basis of a set of EMG and acceleration signal features. In total, the measurement data from nine PD patients were analyzed. The subjects were measured in four different medication conditions: off-medication, and two and three and four hours after taking the medication. The isometric task (described in section 3.1) was analyzed. The UPDRS -motor examination was performed for each patient in each medication condition. The EMG and acceleration signals of one PD patient in each medication condition are presented in Figure

**4.2. EMG and acceleration signal features for characterizing effects of treatment** Several EMG and acceleration signal features were observed to be effective in characterizing the effects of treatment on PD patients in [30, 31]. These features are detailed in Table 2.

> recurrence rate of EMG %RECr and %RECl root mean square amplitude of ACC RMSr and RMSl sample entropy of ACC SampEnr and SampEnl

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recurrence rate of EMG %RECr and %RECl root mean square amplitude of ACC RMSr and RMSl sample entropy of ACC SampEnr and SampEnl

coherence between EMG and ACC Cohr and Cohl

**Table 2.** PD characteristic signal features for quantifying effects of treatment. The subscripts r and l in

The parameters were calculated as described in section 3.2. The root mean square amplitude

In [30], the ten signal features (five features from each body side) in Table 2 were normalized (to zero mean and unit SD of healthy subjects) and used to form feature vectors for subjects. One feature vector was formed for each healthy subject and two feature vectors for each patient: one with DBS on and one with DBS off. The PC approach (see section 2.2) was applied once. The eigenvectors were solved by using the feature vectors of healthy subjects. In this

In [31], the eight signal parameters in Table 2 were normalized (to zero mean and unit SD of all patients) and used to form feature vectors for PD patients. Four feature vectors were formed for each patient (one feature vector in each medication condition). The PC approach

In [30], the group mean values of the parameters *D*<sup>2</sup> and SampEn increased and the group mean values of the parameters %REC, RMS and Coh decreased with DBS for the patient group. However, the SDs of the parameters were very high for the patient group because of its heterogeneity. Therefore, the patient measurements were studied individually. The first and the third PCs worked best in characterizing effects of DBS and differences between patients and healthy subjects. According to the results in Figure 11, 12 out of 13 patients are closer to the center point of healthy subjects with DBS on than with DBS off in the two-dimensional feature space (*θ*3(*j*) with respect to *θ*1(*j*)). That is, the EMG and acceleration signals of PD patients are more similar with the signals of the healthy subjects with DBS on than with DBS off. The distances of the patients from the center of healthy subjects and the clinical UPDRS -motor scores are highly individual (see Table 3). It was observed in a more

Treatment Signal features Notations DBS correlation dimension of EMG *D*2,r and *D*2,l

Medication sample kurtosis of EMG *kr* and *kl*

**4.3. Principal components in quantifying the effects of treatment**

way, the healthy subject group formed the normal group for later comparison.

of acceleration was calculated for quantifying tremor amplitude.

the notations stand for the side of the body.

(see section 2.2) was applied once.

**4.4. Results**

**Figure 9.** The EMG and acceleration signals of one PD patient with DBS on and with DBS off.

10. It is observed that the number of recurring EMG bursts and the amplitude of tremor decrease with medication and start to increase three hours after taking the medication.

**Figure 10.** The EMG and acceleration signals of one PD patient in four medication conditions: with medication off, and two and three and four hours after taking the medication.

#### **4.2. EMG and acceleration signal features for characterizing effects of treatment**

Several EMG and acceleration signal features were observed to be effective in characterizing the effects of treatment on PD patients in [30, 31]. These features are detailed in Table 2.


**Table 2.** PD characteristic signal features for quantifying effects of treatment. The subscripts r and l in the notations stand for the side of the body.

The parameters were calculated as described in section 3.2. The root mean square amplitude of acceleration was calculated for quantifying tremor amplitude.

#### **4.3. Principal components in quantifying the effects of treatment**

In [30], the ten signal features (five features from each body side) in Table 2 were normalized (to zero mean and unit SD of healthy subjects) and used to form feature vectors for subjects. One feature vector was formed for each healthy subject and two feature vectors for each patient: one with DBS on and one with DBS off. The PC approach (see section 2.2) was applied once. The eigenvectors were solved by using the feature vectors of healthy subjects. In this way, the healthy subject group formed the normal group for later comparison.

In [31], the eight signal parameters in Table 2 were normalized (to zero mean and unit SD of all patients) and used to form feature vectors for PD patients. Four feature vectors were formed for each patient (one feature vector in each medication condition). The PC approach (see section 2.2) was applied once.

#### **4.4. Results**

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**Figure 9.** The EMG and acceleration signals of one PD patient with DBS on and with DBS off.

10. It is observed that the number of recurring EMG bursts and the amplitude of tremor decrease with medication and start to increase three hours after taking the medication.

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**Figure 10.** The EMG and acceleration signals of one PD patient in four medication conditions: with

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In [30], the group mean values of the parameters *D*<sup>2</sup> and SampEn increased and the group mean values of the parameters %REC, RMS and Coh decreased with DBS for the patient group. However, the SDs of the parameters were very high for the patient group because of its heterogeneity. Therefore, the patient measurements were studied individually. The first and the third PCs worked best in characterizing effects of DBS and differences between patients and healthy subjects. According to the results in Figure 11, 12 out of 13 patients are closer to the center point of healthy subjects with DBS on than with DBS off in the two-dimensional feature space (*θ*3(*j*) with respect to *θ*1(*j*)). That is, the EMG and acceleration signals of PD patients are more similar with the signals of the healthy subjects with DBS on than with DBS off. The distances of the patients from the center of healthy subjects and the clinical UPDRS -motor scores are highly individual (see Table 3). It was observed in a more 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, the measurement results contradict the subjective clinical scores.

Patient no. UPDRS off UPDRS on Distance off Distance on 56 43 26 25 64 48 32 12 59 40 7 5 34 14 180 30 71 42 289 4 38 31 5 12 47 28 6 2 57 33 6 4 43 34 13 11 43 24 11 10 44 30 6 5 62 38 5 4 43 30 5 3 **Table 3.** Total UPDRS -motor scores and the distances from the center of healthy subjects with DBS on 239

Feature Extraction Methods for Studying Surface

Electromyography and Kinematic Measurements in Parkinson's Disease

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

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

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

of EMG. Therefore, more novel methods of EMG analysis are needed for PD.

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

specificities of the methods in the subject groups that were studied.

and off.

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