Cross-correlation time-frequency analysis for multiple EMG signals in Parkinson's disease: a wavelet approach

Using a wavelet analysis approach, it is possible to investigate better the transient and intermittent behavior of multiple electromyographic (EMG) signals during ballistic movements in Parkinsonian patients. In particular, a wavelet cross-correlation analysis on surface signals of two different shoulder muscles allows us to evidence the related unsteady and synchronization characteristics. With a suitable global parameter extracted from local wavelet power spectra, it is possible to accurately classify the subjects in terms of a reliable statistic and to study the temporal evolution of the Parkinson's disease level. Moreover, a local intermittency measure appears as a new promising index to distinguish the low-frequency behavior from normal subjects to Parkinsonian patients.     

基于子波变换阿尔茨海默病脑电信号多尺度定量特征的研究

目的：研究阿尔茨海默病（AlzheimerDisease,AD）脑电信号的多尺度定量特征和相位平均波形。方法：采集32例重度AD患者,30例轻度AD患者和30例正常对照的清醒安静闭目状态下的脑电信号,进行Gauss连续子波变换,提取脑电信号的时频分布特征和多尺度功率谱分布特征;应用条件采样和相位平均的方法提取脑电信号分尺度相位平均波形。结果：AD脑电信号的时频结构特征表现为尺度单一,节律性活动紊乱,而正常对照脑电信号尺度结构丰富,在0．1Hz、1Hz和10Hz频带上形成稳定的节律性活动。AD患者脑电信号的多尺度功率谱分布特征表现为在1Hz附近出现窄而高的功率峰,而正常对照老年人脑电信号表现为在0．1Hz、1Hz和10Hz附近出现三个宽而低的功率峰。多尺度相位平均波形的结果显示,不同导联脑电信号第9尺度（频率中心10Hz）的相位平均波形的波长在重度AD组、轻度AD组和正常对照组三组之间比较存在显著差异（P〈0．01）,组间两两比较也存在显著差异（P〈0．05）。不同导联脑电信号第9尺度的相位平均波形的波长与简易智能精神状态量表（MMSE）评分之间存在负相关（P〈0．01）,说明这一参数与病情严重程度相关。结论：子波分析适用于痴呆病人脑电信号的定量分析,研究表明脑电信号的时频结构、多尺度功率谱分布和第9尺度相位平均波形的波长可以作为AD诊断和评估的定量电生理指标。     

Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion

In this paper we present an optimal wavelet packet (OWP) method based on Davies-Bouldin criterion for the classification of surface electromyographic signals. To reduce the feature dimensionality of the outputs of the OWP decomposition, the principle components analysis was employed. Then we chose a neural network classifier to discriminate four types of prosthesis movements. The proposed method achieved a mean classification accuracy of 93.75%, which outperformed the method using the energy of wavelet packet coefficients (with mean classification accuracy 86.25%) and the fuzzy wavelet packet method (87.5%).     

Spontaneous blinks of Parkinson's disease patients evaluated by EMG and EOG

In a study of spontaneous blinks, both electromyographic (EMG) activities from m. orbicularis oculi which is responsible for initiating closure of the eyelid and electro-oculogram (EOG) of vertical direction to the movement of the eyelid were measured in ten patients with Parkinson's disease and in thirty normal subjects. The aim of this study was to evaluate the generative mechanism of the spontaneous blinks by comparison of both the EMG and the EOG waveforms in the patients with Parkinson's disease and those in the normal subjects. The mean duration and the amplitude of both the EMG and the EOG were evaluated by the averaging of ten waveforms for the spontaneous blinks. The time lag between the onset of the generation of the EMG and the onset of the EOG signal was analyzed. The mean duration of the EMG and the mean amplitude of both the EMG and the EOG in the patients with Parkinson's disease were shorter and smaller than those in the normal subjects by the significant level of 1%, respectively. There was no difference of the time lag between the subject groups. These results suggest that the function of m. orbicularis oculi for the spontaneous blinks is reduced in patients with Parkinson's disease, because the motoneurones of the facial nucleus innervating the m. orbicularis oculi becomes hypoactive due to abnormal output of basal ganglia.     

Decomposition of surface EMG signals

This report describes an early version of a technique for decomposing surface electromyographic (sEMG) signals into the constituent motor unit (MU) action potential trains. A surface sensor array is used to collect four channels of differentially amplified EMG signals. The decomposition is achieved by a set of algorithms that uses a specially developed knowledge-based Artificial Intelligence framework. In the automatic mode the accuracy ranges from 75 to 91%. An Interactive Editor is used to increase the accuracy to > 97% in signal epochs of about 30-s duration. The accuracy was verified by comparing the firings of action potentials from the EMG signals detected simultaneously by the surface sensor array and by a needle sensor. We have decomposed up to six MU action potential trains from the sEMG signal detected from the orbicularis oculi, platysma, and tibialis anterior muscles. However, the yield is generally low, with typically < or = 5 MUs per contraction. Both the accuracy and the yield should increase as the algorithms are developed further. With this technique it is possible to investigate the behavior of MUs in muscles that are not easily studied by needle sensors. We found that the inverse relationship between the recruitment threshold and the firing rate previously reported for muscles innervated by spinal nerves is also present in the orbicularis oculi and the platysma, which are innervated by cranial nerves. However, these two muscles were found to have greater and more widespread values of firing rates than those of large limb muscles.     

Impact of Parkinson's disease and dopaminergic medication on proprioceptive processing

Increasing evidence suggests that the pathophysiology of movement disorders in Parkinson's disease (PD) includes deficits in sensory processing and integration. However, the exact nature of these deficits and the ability of dopamine medication to correct them have not been thoroughly examined in previous studies. For instance, it remains unclear whether PD patients have globally impaired sensorimotor integration functions or selective deficiencies in processing proprioception. We evaluated the specific deficits of PD patients in sensorimotor integration and proprioceptive processing by testing their ability to perform three-dimensional (3D) reaching movements in four conditions in which the sensory signals defining target and hand positions (visual and/or proprioceptive) varied. Ten healthy subjects and 11 PD patients, ON dopamine medication and in the OFF state, were tested. PD patients in the OFF state showed a greater mean level of 3D errors relative to controls when the only available sensory information about target and hand position came from proprioception, but this difference did not reach significance. This indicates that deficient proprioception is not an early key feature of PD. Interestingly, the inaccuracies of a number of PD subjects further increased in the ON medicated state relative to healthy controls when reaching to proprioceptively-defined targets, and this between group difference was statistically significant. However, dopamine medication did not consistently degrade the reaching accuracy of PD patients, with both negative and positive effects on accuracy of reaching to proprioceptive-defined targets. Together, these findings indicate that dopamine replacement therapy not only did not normalize sensorimotor performance to the level of controls, but also induced deficits in the processing of proprioceptive information in some of the PD patients tested. Furthermore, the diversity of effects of medication on accuracy of reaching to proprioceptively-defined targets supports the idea that dysfunction of dopaminergic circuits within the basal ganglia is not primarily responsible for the proprioceptive processing deficits of PD patients.     

Genome-wide scan linkage analysis for Parkinson's disease: the European genetic study of Parkinson's disease

Objective: To undertake a full genome-wide screen for Parkinson's disease susceptibility loci.

Methods: A genome-wide linkage study was undertaken in 227 affected sibling pairs from 199 pedigrees with Parkinson's disease. The pedigree sample consisted of 188 pedigrees from five European countries, and 11 from the USA. Individuals were genotyped for 391 microsatellite markers at ~10 cM intervals throughout the genome. Multipoint model-free affected sibling pair linkage analyses were carried out using the MLS (maximum LOD score) test.

Results: There were six chromosomal regions with maximum MLS peaks of 1 or greater (pointwise p<0.018). Four of these chromosomal regions appear to be newly identified regions, and the highest MLS values were obtained on chromosomes 11q (MLS = 1.60, at 91 cM, D11S4175) and 7p (MLS = 1.51, at 5 cM, D7S531). The remaining two MLS peaks, on 2p11–q12 and 5q23, are consistent with excess sharing in regions reported by other studies. The highest MLS peak was observed on chromosome 2p11–q12 (MLS = 2.04, between markers D2S2216 and D2S160), within a relatively short distance (~17 cM) from the PARK3 region. Although a stronger support of linkage to this region was observed in the late age of onset subgroup of families, these differences were not significant. The peak on 5q23 (MLS = 1.05, at 130 cM, D5S471) coincides with the region identified by three other genome scans. All peak locations fell within a 10 cM distance.

Conclusions: These stratified linkage analyses suggest linkage heterogeneity within the sample across the 2p11–q12 and 5q23 regions, with these two regions contributing independently to Parkinson's disease susceptibility.

     

Mutational analysis of TARDBP in Parkinson's disease

Mutations in TAR DNA-binding protein (TARDBP) are associated with heterogenic phenotypes, including amyotrophic lateral sclerosis, frontotemporal dementia, and Parkinson's disease. In this study, we investigated the presence of TARDBP mutations in a cohort of 429 Dutch patients with Parkinson's disease. Though we detected 1 silent mutation, p.S332S, no missense mutations were present in our cohort. Our findings, therefore, demonstrate that TARDBP mutations do not appear to contribute to the pathogenesis of Parkinson's disease in The Netherlands.     

Quantitative plasma DNA analysis in Parkinson's disease

Parkinson disease (PD) is a neurodegenerative disease resulting from the loss of the dopaminergic neurons from the substantia nigra pars compacta (SNpc). It is characterized by bradykinesia, rigidity, resting tremor and/or postural instability. The diagnosis of PD is essentially clinical and there is no reliable biological marker to assess its progression. Recently, investigations have been performed on the potential use of circulating cell-free deoxyribonucleic acid (DNA) in the plasma for clinical diagnosis, prognosis and monitoring of human diseases. The aim of this work was to assess the role of free DNA as a biological marker of PD. Forty-two patients with PD (19 men, 23 women) and 20 healthy (7 men, 13 women) subjects were enrolled in this study. Mean ± SD plasma DNA concentration in PD patients and control subjects were, respectively, 16,487 ± 16,378 (range: 100–62,034) kilogenomes-equivalents/L and 37,975 ± 17,832 (range: 15,143–78,783) kilogenomes-equivalents/L. There was a significant difference between control and PD groups (p < 0.001). There was no correlation between plasma DNA levels and demographic or clinical parameters in PD patients. Free DNA does not seem to be a reliable marker of PD progression. Further research is warranted to confirm the present results to have some value as biomarkers in other neurodegenerative diseases.     

Performance analysis of four nonlinearity analysis methods using a model with variable complexity and application to uterine EMG signals

Several measures have been proposed to detect nonlinear characteristics in time series. Results on time series, multiple surrogates and their z-score are used to statistically test for the presence or absence of non-linearity. The z-score itself has sometimes been used as a measure of nonlinearity. The sensitivity of nonlinear methods to the nonlinearity level and their robustness to noise have rarely been evaluated in the past. While surrogates are important tools to rigorously detect nonlinearity, their usefulness for evaluating the level of nonlinearity is not clear. In this paper we investigate the performance of four methods arising from three families that are widely used in non-linearity detection: statistics (time reversibility), predictability (sample entropy, delay vector variance) and chaos theory (Lyapunov exponents). We used sensitivity to increasing complexity and the mean square error (MSE) of Monte Carlo instances for quantitative comparison of their performances. These methods were applied to a Henon nonlinear synthetic model in which we can vary the complexity degree (CD). This was done first by applying the methods directly to the signal and then using the z-score (surrogates) with and without added noise. The methods were then applied to real uterine EMG signals and used to distinguish between pregnancy and labor contraction bursts. The discrimination performances were compared to linear frequency based methods classically used for the same purpose such as mean power frequency (MPF), peak frequency (PF) and median frequency (MF). The results show noticeable difference between different methods, with a clear superiority of some of the nonlinear methods (time reversibility, Lyapunov exponents) over the linear methods. Applying the methods directly to the signals gave better results than using the z-score, except for sample entropy.     

Adaptive certainty-based classification for decomposition of EMG signals

An adaptive certainty-based supervised classification approach for electromyographic (EMG) signal decomposition is presented and evaluated. Similarity criterion used for grouping motor unit potentials (MUPs) is based on a combination of MUP shapes and two modes of use of motor unit (MU) firing pattern information: passive and active. Performance of the developed classifier was evaluated using synthetic signals of known properties and real signals and compared with the performance of the certainty classifier (CC). Across the sets of simulated and real EMG signals used for comparison, the adaptive certainty classifier (ACC) had both better average performance and lower performance variability. For simulated signals of varying intensity, the ACC had an average correct classification rate (CC _r ) of 83.7% with a mean absolute deviation (MAD) of 5.8% compared to 78.3 and 8.7%, respectively, for the CC. For simulated signals with varying amounts of shape and/or firing pattern variability, the ACC had a CC _r of 79.7% with a MAD of 4.7% compared to 76.6 and 6.9%, respectively, for the CC. For real signals, the ACC had a CC _r of 70.0% with a MAD of 6.3% compared to 64.9 and 6.4%, respectively, for the CC. The test results demonstrate that the ACC can manage both MUP shape variability as well as MU firing pattern variability. The ACC adapts to EMG signal characteristics to create dynamic data driven classification criteria so that the number of MUP assignments made reflects the signal complexity and the number of erroneous assignments is kept sufficiently low. The ability of the ACC to adjust to specific signal characteristics suggests that it can be successfully applied to a wide variety of EMG signals.     

Stress-induced Parkinson's disease: a working hypothesis

Some cases of Parkinson's disease (PD) can be attributed to genetic mutations, others to specific environmental factors; yet the cause of a great majority of cases is unknown. Physical and emotional traumas were once briefly considered as factors in the pathophysiology of this disorder. With increasing evidence that stress can indeed increase neuronal loss in some brain regions, this hypothesis deserves to be reexamined. Stress increases the extracellular availability of glucocorticoids (GCs), dopamine (DA), and glutamate in the striatum as well as other brain regions. These factors undoubtedly can serve to enhance the functions of the striatum. However, each also has the capacity to be neurotoxic. Moreover, they can act synergistically to promote neuronal loss. Thus, we propose that stress might, indeed, be a key factor in the loss of DA neurons that underlies PD.     

Detection of hidden rhythms in surface EMG signals with a non-linear time-series tool

The analysis of the surface electromyographic (sEMG) signal is particularly attractive because it provides relatively easy access to those physiological processes that allow the muscle to generate force and movement. In this paper, one of the possible applications of recurrence plot strategy to the analysis of sEMG is described. Recurrence Quantification Analysis (RQA) is an efficient time-series analysis tool pertaining to the class of non-linear dynamics time-domain processing. We analysed sEMG recorded on the biceps brachii during isometric contraction both at constant (CF) and non constant force (NCF). For comparison purposes, experimental data were analysed over epochs of 1 s so that the hypothesis of sEMG stationarity could be accepted. The analysis concerned one of the most widely used frequency parameters (namely the median frequency, MDF) and one parameter (i.e., the percent determinism %DET) extracted using the non-linear technique. Our main results are: (i) the gross average evaluated for all subjects on %DET data shows a comparable variation with respect to MDF throughout the course of CF experiments; (ii) %DET seems able to detect motor unit (MU) synchronisation; (iii) during non constant force experiments, %DET is more effective than MDF in detecting sEMG changes determined by brisk transients of force output.     

Adaptive spatio-temporal filtering of multichannel surface EMG signals

A motor unit (MU) is defined as an anterior horn cell, its axon, and the muscle fibres innervated by the motor neuron. A surface electromyogram (EMG) is a superposition of many different MU action potentials (MUAPs) generated by active MUs. The objectives of this study were to introduce a new adaptive spatio-temporal filter, here called maximum kurtosis filter (MKF), and to compare it with existing filters, on its performance to detect a single MUAP train from multichannel surface EMG signals. The MKF adaptively chooses the filter coefficients by maximising the kurtosis of the output. The proposed method was compared with five commonly used spatial filters, the weighted low-pass differential filter (WLPD) and the marginal distribution of a continuous wavelet transform. The performance was evaluated using simulated EMG signals. In addition, results from a multichannel surface EMG measurement fro from a subject who had been previously exposed to radiation due to cancer were used to demonstrate an application of the method. With five time lags of the MKF, the sensitivity was 98.7% and the highest sensitivity of the traditional filters was 86.8%, which was obtained with the WLPD. The positive predictivities of these filters were 87.4 and 80.4%, respectively. Results from simulations showed that the proposed spatio-temporal filtration technique significantly improved performance as compared with existing filters, and the sensitivity and the positive predictivity increased with an increase in number of time lags in the filter.     

Adaptation of wavelet transform analysis to the investigation of biological variations in speech signals

The purpose of this study was to adapt wavelet analysis as a tool for discriminating speech samples taken from healthy subjects across two biological states. Speech pressure waveforms were drawn from a study on effects of hormone fluctuations across the menstrual cycle on language functions. Speech samples from the vowel portion of the syllable 'pa', taken at the low- and high-hormone phases of the menstrual cycle, were extracted for analysis. Initial analysis applied Fourier transforms to examine the fundamental and formant frequencies. Wavelet analysis was used to investigate spectral differences at a more microbehavioural level. The key finding showed that wavelet coefficients for the fundamental frequency of speech samples taken from the high-hormone phase had larger amplitudes than those from the low-hormone phase. This study provided evidence for differences in speech across the menstrual cycle that affected the vowel portion of syllables. This evidence complements existing data on the temporal features of speech that characterise the consonant portion of syllables. Wavelet analysis provides a new tool for examination of behavioural differences in speech linked to hormonal variation.     

Genetic algorithm for analysis of mutations in Parkinson's disease

OBJECTIVE: Mitochondrial genetics has unique features that impede analysis of the biological significance of mitochondrial mutations. Simple searches for differences in total mutational load between normal and pathological samples have been frequently unrewarding, raising the possibility that more complex patterns of mutations may be responsible for some conditions. We explore this possibility in the context of Parkinson's disease (PD). METHODS AND MATERIALS: We report the development of a modified genetic algorithm suited for detection of biologically meaningful patterns of mitochondrial mutations. The algorithm is applied to a database of mutations derived from biological samples, and verified by the use of shuffled data, and repeated leave-one-out testing. RESULTS: It is possible to derive, from a very small sample, multiple accurate classifier functions that correlate with biological features. The methodology is validated statistically through experiments with fabricated data. CONCLUSION: This algorithm might be generally applicable to conditions where interactions among multiple mitochondrial DNA mutations are important. The patterns embodied in the classifier functions obtained should be the subject of further experimental study.     

Mutational analysis of the VCP gene in Parkinson's disease

Mutations in the valosin-containing protein gene (VCP) have been identified in neurological disorders (inclusion body myopathy—early Paget's disease of the bone—frontotemporal dementia and amyotrophic lateral sclerosis) and are thought to play a role in the clearance of abnormally folded proteins. Parkinsonism has been noted in kindreds with VCP mutations. Based on this, we hypothesized that mutations in VCP may also contribute to idiopathic Parkinson's disease (PD). We screened the coding region of the VCP gene in a large cohort of 768 late-onset PD cases (average age at onset, 70 years), both sporadic and with positive family history. We identified a number of rare single nucleotide changes, including a variant previously described to be pathogenic, but no clear disease-causing variants. We conclude that mutations in VCP are not a common cause for idiopathic PD.