Saara M. Rissanen

Novel parameters of surface EMG in patients with Parkinson’s disease and healthy young and old controls

The aim of this study was to evaluate a variety of traditional and novel surface electromyography (SEMG) characteristics of biceps brachii muscle in patients with Parkinsons disease (PD) and compare the results with the healthy old and young control subjects. Furthermore, the aim was to define the optimal biceps brachii loading level that would most likely differentiate patients from controls. The results indicated that such nonlinear SEMG parameters as %Recurrence, %Determinism and SEMG distribution kurtosis, correlation dimension and sample entropy were significantly different between the PD patients and healthy controls. These novel nonlinear parameters, unlike traditional spectral or amplitude parameters, correlated with the Unified Parkinsons Disease Rating Scale (UPDRS) and finger tapping scores. The most significant between group differences were found in the loading condition where no additional weights were applied in isometric elbow flexion. No major difference of SEMG characteristics was detected between old and young control subjects. In conclusion, the novel SEMG parameters can differentiate the patients with PD from healthy control subjects and these parameters may have potential in the assessment of the severity of PD.

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.

Analysis of surface EMG signal morphology in Parkinson's disease.

A novel approach is presented for the analysis of surface electromyogram (EMG) morphology in Parkinsons disease (PD). The method is based on histogram and crossing rate (CR) analysis of the EMG signal. In the method, histograms and CR values are used as high-dimensional feature vectors. The dimensionality of them is then reduced using the Karhunen-Loève transform (KLT). Finally, the discriminant analysis of feature vectors is performed in low-dimensional eigenspace. Histograms and CR values were chosen for analysis, because Parkinsonian EMG signals typically involve patterns of EMG bursts. Traditional methods of EMG amplitude and spectral analysis are not effective in analyzing impulse-like signals. The method, which was tested with EMG signals measured from 25 patients with PD and 22 healthy controls, was promising for discriminating between these two groups of subjects. The ratio of correct discrimination by augmented KLT was 86% for the control group and 72% for the patient group. On the basis of these results, further studies are suggested in order to evaluate the usability of this method in early stage diagnostics of PD.

Analysis of EMG and Acceleration Signals for Quantifying the Effects of Deep Brain Stimulation in Parkinson’s Disease

Deep brain stimulation (DBS) is effective in reducing motor symptoms in Parkinsons disease (PD). However, objective methods for quantifying its efficacy are lacking. We present a principal component (PC)-based tracking method for quantifying the effects of DBS in PD by using electromyography (EMG) and acceleration measurements. Ten parameters capturing PD characteristic signal features were initially extracted from isometric EMG and acceleration recordings. Using a PC approach, the original parameters were transformed into a smaller number of PCs. Finally, the effects of DBS were quantified by examining the PCs in a low-dimensional feature space. The EMG and acceleration data from 13 PD patients with DBS ON and OFF, and 13 healthy age-matched controls were used for analysis. Clinical evaluation of patients showed that their motor symptoms were effectively reduced with DBS. The analysis results showed that the signal characteristics of 12 patients were more similar to those of the healthy controls with DBS ON than with DBS OFF. These observations indicate that the PC-based tracking method can be used to objectively quantify the effects of DBS on the neuromuscular function of PD patients. Further studies are suggested to estimate the clinical sensitivity of the method to different types of PD.

Linear and nonlinear tremor acceleration characteristics in patients with Parkinson's disease

The purpose of the study was to evaluate linear and nonlinear tremor characteristics of the hand in patients with Parkinsons disease (PD) and to compare the results with those of healthy old and young control subjects. Furthermore, the aim was to study correlation between tremor characteristics and clinical signs. A variety of nonlinear (sample entropy, cross-sample entropy, recurrence rate, determinism and correlation dimension) and linear (amplitude, spectral peak frequency and total power, and coherence) hand tremor parameters were computed from acceleration measurements for PD patients (n = 30, 68.3 ± 7.8 years), and old (n = 20, 64.2 ± 7.0 years) and young (n = 20, 18.4 ± 1.1 years) control subjects. Nonlinear tremor parameters such as determinism, sample entropy and cross-sample entropy were significantly different between the PD patients and healthy controls. These parameters correlated with the Unified Parkinsons disease rating scale (UPDRS), tremor and finger tapping scores, but not with the rigidity scores. Linear tremor parameters such as the amplitude and the maximum power (power corresponding to peak frequency) also correlated with the clinical findings. No major difference was detected in the tremor characteristics between old and young control subjects. The study revealed that tremor in PD patients is more deterministic and regular when compared to old or young healthy controls. The nonlinear tremor parameters can differentiate patients with PD from healthy control subjects and these parameters may have potential in the assessment of the severity of PD (UPDRS).

Analysis of Dynamic Voluntary Muscle Contractions in Parkinson's Disease

A novel method for discrimination of dynamic muscle contractions between patients with Parkinsons disease (PD) and healthy controls on the basis of surface electromyography (EMG) and acceleration measurements is presented. In this method, dynamic EMG and acceleration measurements are analyzed using nonlinear methods and wavelets. Ten parameters capturing Parkinsons disease (PD) characteristic features in the measured signals are extracted. Each parameter is computed as time-varying, and for elbow flexion and extension movements separately. For discrimination between subjects, the dimensionality of the feature vectors formed from these parameters is reduced using a principal component approach. The cluster analysis of the low-dimensional feature vectors is then performed for flexion and extension movements separately. The EMG and acceleration data measured from 49 patients with PD and 59 healthy controls are used for analysis. According to clustering results, the method could discriminate 80% of patient extension movements from 87% of control extension movements, and 73% of patient flexion movements from 82% of control flexion movements. The results show that dynamic EMG and acceleration measurements can be informative for assessing neuromuscular dysfunction in PD, and furthermore, they may help in the objective clinical assessment of the disease.

Concurrent validity and reliability of a novel wireless inertial measurement system to assess trunk movement.

INTRODUCTION Assessment of movement dysfunctions commonly comprises trunk range of motion (ROM), movement or control impairment (MCI), repetitive movements (RM), and reposition error (RE). Inertial measurement unit (IMU)-systems could be used to quantify these movement dysfunctions in clinical settings. The aim of this study was to evaluate a novel IMU-system when assessing movement dysfunctions in terms of concurrent validity and reliability. METHODS The concurrent validity of the IMU-system was tested against an optoelectronic system with 22 participants. The reliability of 14 movement dysfunction tests were analysed using generalizability theory and coefficient of variation, measuring 24 participants in seven trials on two days. RESULTS The IMU-system provided valid estimates of trunk movement in the primary movement direction when compared to the optoelectronic system. Reliability varied across tests and variables. On average, ROM and RM were more reliable, compared to MCI and RE tests. DISCUSSION When compared to the optoelectronic system, the IMU-system is valid for estimates of trunk movement in the primary movement direction. Four ROM, two MCI, one RM, and one RE test were identified as reliable and should be studied further for inter-subject comparisons and monitoring changes after an intervention.

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.

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.

Pain intensity attenuates movement control of the lumbar spine in low back pain

INTRODUCTION Pain intensity attenuates muscular activity, proprioception, and tactile acuity, with consequent changes of joint kinematics. People suffering from low back pain (LBP) frequently show movement control impairments of the lumbar spine in sagittal plane. This cross-sectional, observational study investigated if the intensity of LBP attenuates lumbar movement control. The hypothesis was that lumbar movement control becomes more limited with increased pain intensity. METHODS The effect of LBP intensity, measured with a numeric rating scale (NRS), on lumbar movement control was tested using three movement control tests. The lumbar range of motion (ROM), the ratio of lumbar and hip ROM as indicators of direction specific movement control, and the recurrence and determinism of repetitive lumbar movement patterns were assessed in ninety-four persons suffering from LBP of different intensity and measured with an inertial measurement unit system. Generalized linear models were fitted for each outcome. RESULTS Lumbar ROM (+ 0.03°, p = 0.24) and ratio of lumbar and hip ROM (0.01, p = 0.84) were unaffected by LBP intensity. Each one point increase on the NRS resulted in a decrease of recurrence and determinism of lumbar movement patterns (-3.11 to -0.06, p ⩽ 0.05). DISCUSSION Our results indicate changes in movement control in people suffering from LBP. Whether decreased recurrence and determinism of lumbar movement patterns are intensifiers of LBP intensity or a consequence thereof should be addressed in a future prospective study.