**1.1. Parkinson's disease (PD)**

Parkinson's disease is a progressive neurodegenerative disease that affects 1 % of people over 60 years of age [9]. In PD, there is a dopaminergic neuronal loss in the substantia nigra in the basal ganglia of the cerebra [48]. It has been observed that the basal ganglia has a specific effect on the temporal organization of motor cortical activity during muscle contractions. In this way, the dysfunction of the basal ganglia may lead to motor symptoms of PD. [37] The primary symptoms of PD include tremor, muscle rigidity and slowness of movements. The diagnosis is based on the presence of the primary symptoms and on the response to medication. [17, 18]. However, the diagnosis can be problematic. Clinicopathological studies from the UK and Canada have shown that the disease is diagnosed incorrectly in about 25 % of patients [48]. The pre-motor period before diagnosis may be long (5–20 years) and at the time of the diagnosis already 50–60 % of the dopaminergic neurons may be lost [22, 38].

Although there is no cure for PD, the symptoms can be relieved reasonably with medication or with the deep brain stimulation (DBS) [17]. The motor impairment, the disease progression and the efficacy of treatment are commonly evaluated subjectively using standardized rating scales such as the Unified Parkinson's disease rating scale (UPDRS) [12, 15]. No objectively measured characteristics and methods are widely used for quantifying motor symptoms of PD [2].

Several objective methods have been proposed for improving the diagnostic accuracy of PD, for enabling earlier diagnosis, and for quantifying the disease severity, progression and the efficacy of treatment. These methods include: kinematic measurements of motor tasks (e.g. finger tapping), testing of olfactory loss, imaging techniques (e.g. magnetic resonance imaging and positron emission tomography), and biochemical tests of blood and cerebrospinal fluid.

©2012 Rissanen et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0),which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ©2012 Rissanen et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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

objective methods for discriminating between PD patients and healthy subjects on the basis of surface EMG signal morphology [32] and on the basis of simultaneous EMG and acceleration (ACC) recordings during isometric [28] and dynamic muscle contractions [29]. Another aim was to develop methods based on surface EMG and kinematic measurements and analysis for quantifying effects of PD treatment (medication and DBS) on individual level. All of those studies presented an innovative approach, that combines a principal component (PC) -based method with a set of effective signal features, for analyzing the EMG and acceleration signals in PD. In the following sections 2, 3 and 4, we describe the methods that were developed and used for feature extraction and discrimination between subjects in [28–32]. All methods were tested with the measured data. In total, the measurement data from 62 PD patients and 72 healthy subjects were analyzed. The main findings of those studies are also described.

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Feature Extraction Methods for Studying Surface

Electromyography and Kinematic Measurements in Parkinson's Disease

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

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)

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

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

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

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

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

in bipolar connection. The sampling rate was 1000 Hz.

**2.2. Discrimination analysis between subjects**

signal of the healthy subject does not.

than those of the healthy subject.

patters.
