Alexander Meigal

Surface EMG and acceleration signals in Parkinson’s disease: feature extraction and cluster analysis

We present an advanced method for feature extraction and cluster analysis of surface electromyograms (EMG) and acceleration signals in Parkinson’s disease (PD). In the method, 12 different EMG and acceleration signal features are extracted and used to form high-dimensional feature vectors. The dimensionality of these vectors is then reduced by using the principal component approach. Finally, the cluster analysis of feature vectors is performed in a low-dimensional eigenspace. The method was tested with EMG and acceleration data of 42 patients with PD and 59 healthy controls. The obtained discrimination between patients and controls was promising. According to clustering results, one cluster contained 90% of the healthy controls and two other clusters 76% of the patients. Seven patients with severe motor dysfunctions were distinguished in one of the patient clusters. In the future, the clinical value of the method should be further evaluated.

Gross and fine neuromuscular performance at cold shivering

Cold exposure may decrease the efficiency of voluntary motor activity due to a decrease of muscular temperature, and to involvement of the motor system in thermoregulator y motor behaviour and shivering thermogenesis.Traditionally, shivering thermogenesis is believed to comprise two patterns — 1) thermoregulatory muscle tone (or preshivering tone) and 2) cold shivering itself (Bur ton, Bronk, 1937). Since it was observed in animal experiments, this classification has been proved to be practical in human measurements. Shivering thermogenesis is aimed to prevent hypothermia by increasing heat production. Shivering and voluntary movements compete for common neural circuits (Kleinebeckel, Klussmann, 1990), and this may affect fine and gross performance. Mechanical tremor and elevated muscle tone during shivering may also influence accuracy of movements.This review focuses on the mechanisms of interaction between shivering thermogenesis and exercise and on the mechanisms which may compensate for this interaction.

EMG signal morphology and kinematic parameters in essential tremor and Parkinson’s disease patients

The aim of this work was to differentiate patients with essential tremor from patients with Parkinsons disease. Electromyographic data from biceps brachii muscles and kinematic data from arms during isometric tension of the arms were measured from 17 patients with essential tremor, 35 patients with Parkinsons disease and 40 healthy controls. The EMG signals were divided to smaller segments from which histograms were calculated. The histogram shape was analysed with a feature dimension reduction method, the principal component analysis, and the shape parameters were used to differentiate between different subject groups. Three parameters, RMS-amplitude, sample entropy and peak frequency were determined from the kinematic measurements of the arms. The height and the side differences of the histogram were the most effective for differentiating between essential tremor and Parkinsons disease groups. The histogram parameters of patients with essential tremor were more similar to patients with Parkinsons disease than healthy controls. With this method it was possible to discriminate 13/17 patients with essential tremor from 26/35 patients with Parkinsons disease and 14/17 patients with essential tremor from 29/40 healthy controls. The kinematic parameters of patients with essential tremor were closer to parameters of patients with Parkinsons disease compared to healthy controls. Combining EMG and kinematic analysis did not increase discrimination efficiency but provided more reliability to the discrimination of subject groups.

Influence of cold and hot conditions on postactivation in human skeletal muscles.

The influence of cold (+5° C), room temperature (+22° C) and hot (+75° C) air exposures on post-activation effects (PAE) in brachial biceps (BBs) and triceps (TBs) muscles were investigated bilaterally in six male subjects. PAE were evoked by 1 min volitional isometric contraction (VIC) at submaximal level in BBs by holding an inertial weight by palms, with right-angled elbows. At room temperature, average EMG during PAE (PAEav) usually was 2–4% and the integral of EMG (PAEint) was 3-7% of that of VIC respectively. PEA duration was 1–6 min. Cold exposure evoked an approximately two-fold increase of PAEintt (P < 0.01). Hot exposure decreased PAEint, (P < 0.01) and shortened PAE duration by approximately 50% (P < 0.01). In two subjects, long- term modulation of EMG intensity during PAE was observed. Cold increased the frequency and amplitude of these waves, while heat decreased them. In two subjects, alternation of BBs and TBs in EMG activity during PAE was observed. The data obtained suggest that postactivation of muscles strongly depends on the environmental temperature.

Non-linear EMG parameters for differential and early diagnostics of Parkinson's disease

The pre-clinical diagnostics is essential for management of Parkinson’s disease (PD). Although PD has been studied intensively in the last decades, the pre-clinical indicators of that motor disorder have yet to be established. Several approaches were proposed but the definitive method is still lacking. Here we report on the non-linear characteristics of surface electromyogram (sEMG) and tremor acceleration as a possible diagnostic tool, and, in prospective, as a predictor for PD. Following this approach we calculated such non-linear parameters of sEMG and accelerometer signal as correlation dimension, entropy, and determinism. We found that the non-linear parameters allowed discriminating some 85% of healthy controls from PD patients. Thus, this approach offers considerable potential for developing sEMG-based method for pre-clinical diagnostics of PD. However, non-linear parameters proved to be more reliable for the shaking form of PD, while diagnostics of the rigid form of PD using EMG remains an open question.

EMG signal morphology in essential tremor and Parkinson's disease

The aim of this work was to differentiate patients with essential tremor from patients with Parkinsons disease. The electromyographic signal from the biceps brachii muscle was measured during isometric tension from 17 patients with essential tremor, 35 patients with Parkinsons disease, and 40 healthy controls. The EMG signals were high pass filtered and divided to smaller segments from which histograms were calculated using 200 histogram bins. EMG signal histogram shape was analysed with a feature dimension reduction method, the principal component analysis, and the shape parameters were used to differentiate between different patient groups. The height of the histogram and the side difference between left and right hand were the best discriminators between essential tremor and Parkinsons disease groups. With this method, it was possible to discriminate 13/17 patients with essential tremor from 26/35 patients with Parkinsons disease and 14/17 patients with essential tremor from 29/40 healthy controls.

Discrimination of EMG and acceleration measurements between patients with Parkinson's disease and healthy persons

In this paper, we examine the potential of electromyographic (EMG) and acceleration measurements in discriminating patients with Parkinsons disease (PD) from healthy persons. Two types of muscle contractions are examined: static contractions of biceps brachii muscles and elbow extension movements. Twelve features are extracted from static and ten features from extension measurements. These features describe signal morphology and nonlinear characteristics, power spreading in EMG wavelet scalograms and spectral coherence. Principal component approach is applied separately for static and extension trial to reduce the number of features before discrimination. The discrimination between subjects is done in a two-dimensional space by applying cluster analysis to the best discriminating principal components. The discrimination power of the used method was estimated with EMG and acceleration data measured from 56 patients with PD and 59 healthy controls. In the cluster analysis, three clusters were formed: one cluster with most (85%) of the healthy persons and two clusters with 80% of patients. Patients were divided into two clusters based on their type of motor disability (problems during movement and/or static contraction). Discrimination results show that EMG and acceleration measurements are potential for discriminating patients with PD from healthy persons. Furthermore, they have potential in the objective clinical assessment of PD.

Surface EMG parameters in schizophrenia patients

The aim of the study was to compare a variety of surface EMG (sEMG) parameters in several groups of schizophrenia (SZ, n=69) patients and healthy controls (n=44). We computed spectral, mutual information (MI) based and recurrence quantification analysis (RQA) parameters of sEMG. The major finding is that sEMG of the controls had higher values of the MI-based parameter, mean and median spectrum frequencies, and lower values of most of RQA parameters. It means higher content of recurrent fragments in sEMG of SZ patients. We suggest that the differences might be caused by either denervation/renervation process of single muscle fibers in SZ patients and/or by increased motor unit synchronization induced by antipsychotic therapy.

Analysis of dynamic EMG and acceleration measurements in Parkinson's disease

In this paper, we bring out modern methods that are potential in analysing differences in the dynamic surface electromyographic (EMG) and acceleration measurements between patients with Parkinsons disease (PD) and healthy persons. These methods are the correlation dimension of EMG, the recurrence rate of EMG, the power of acceleration and the sample entropy of acceleration. In this study, these methods were used to extract features from surface EMG and acceleration recordings measured during elbow flexion and extension movements. The extracted features were used to form high-dimensional feature vectors and the dimensionality of these vectors was then reduced by using the principal component approach. Finally, the feature vectors were discriminated between subjects by using the principal components. The discrimination power of the presented approach was tested with EMG and acceleration data measured from 46 patients with PD (on-medication) and 59 healthy controls. Discrimination results showed that the present method was able to discriminate dynamic EMG and acceleration recordings between patients with PD and healthy controls. Therefore, dynamic surface EMG and acceleration measurements may have potential in the objective and quantitative assessment and diagnosis of PD.

Mobile health service is promising to detect the blood pressure and HRV fluctuations across the menstrual and the lunar cycle

Here we report on experimental feasibility and preliminary results on mobile health services to detect enigmatic effect of the lunar cycle (LC) on heart rate variability (HRV) parameters and blood pressure (BP) in young females, with respect to the menstrual cycle (MC). We found that the MC exerted strong effect on varied HRV parameters specifically in the ovulation phase. Unlike the MC, the LC presented only tiny and non-significant, though still notable, effect on BP and HRV seen primarily in the full moon phase. We hypothesize on potential usability of e-medicine services, such as mobile-nested on-line day-to-day monitoring of electrocardiogram (ECG) for HRV, as a research tool for a longitudinal experiment with ultimate goal to detect the LC impact on human circulation system. Would such hypothetic effect experimentally been found, a mobile medicine services could be invented which allows user synchronizing the MC with the LC for better awareness on current physiological status and planning of daily activities and reproductive behavior.