Ambulatory monitoring of physical activities in patients with Parkinson's disease

A new ambulatory method of monitoring physical activities in Parkinson's disease (PD) patients is proposed based on a portable data-logger with three body-fixed inertial sensors. A group of ten PD patients treated with subthalamic nucleus deep brain stimulation (STN-DBS) and ten normal control subjects followed a protocol of typical daily activities and the whole period of the measurement was recorded by video. Walking periods were recognized using two sensors on shanks and lying periods were detected using a sensor on trunk. By calculating kinematics features of the trunk movements during the transitions between sitting and standing postures and using a statistical classifier, sit-to-stand (SiSt) and stand-to-sit (StSi) transitions were detected and separated from other body movements. Finally, a fuzzy classifier used this information to detect periods of sitting and standing. The proposed method showed a high sensitivity and specificity for the detection of basic body postures allocations: sitting, standing, lying, and walking periods, both in PD patients and healthy subjects. We found significant differences in parameters related to SiSt and StSi transitions between PD patients and controls and also between PD patients with and without STN-DBS turned on. We concluded that our method provides a simple, accurate, and effective means to objectively quantify physical activities in both normal and PD patients and may prove useful to assess the level of motor functions in the latter.     

A new measure of movement symmetry in early Parkinson's disease patients using symbolic processing of inertial sensor data

Movement asymmetry is one of the motor symptoms associated with Parkinson's disease (PD). Therefore, being able to detect and measure movement symmetry is important for monitoring the patient's condition. The present paper introduces a novel symbol based symmetry index calculated from inertial sensor data. The method is explained, evaluated, and compared to six other symmetry measures. These measures were used to determine the symmetry of both upper and lower limbs during walking of 11 early-to-mid-stage PD patients and 15 control subjects. The patients included in the study showed minimal motor abnormalities according to the unified Parkinson's disease rating scale (UPDRS). The symmetry indices were used to classify subjects into two different groups corresponding to PD or control. The proposed method presented high sensitivity and specificity with an area under the receiver operating characteristic (ROC) curve of 0.872, 9% greater than the second best method. The proposed method also showed an excellent intraclass correlation coefficient (ICC) of 0.949, 55% greater than the second best method. Results suggest that the proposed symmetry index is appropriate for this particular group of patients.     

Quantification of tremor and bradykinesia in Parkinson's disease using a novel ambulatory monitoring system

An ambulatory system for quantification of tremor and bradykinesia in patients with Parkinson's disease (PD) is presented. To record movements of the upper extremities, a sensing units which included miniature gyroscopes, has been fixed to each of the forearms. An algorithm to detect and quantify tremor and another algorithm to quantify bradykinesia have been proposed and validated. Two clinical studies have been performed. In the first study, 10 PD patients and 10 control subjects participated in a 45-min protocol of 17 typical daily activities. The algorithm for tremor detection showed an overall sensitivity of 99.5% and a specificity of 94.2% in comparison to a video reference. The estimated tremor amplitude showed a high correlation to the Unified Parkinson's Disease Rating Scale (UPDRS) tremor subscore (e.g., r = 0.87, p < 0.001 for the roll axis). There was a high and significant correlation between the estimated bradykinesia related parameters estimated for the whole period of measurement and respective UPDRS subscore (e.g., r = -0.83, p < 0.001 for the roll axis). In the second study, movements of upper extremities of 11 PD patients were recorded for periods of 3-5 hr. The patients were moving freely during the measurements. The effects of selection of window size used to calculate tremor and bradykinesia related parameters on the correlation between UPDRS and these parameters were studied. By selecting a window similar to the period of the first study, similar correlations were obtained. Moreover, one of the bradykinesia related parameters showed significant correlation (r = -0.74, p < 0.01) to UPDRS with window sizes as short as 5 min. Our study provides evidence that objective, accurate and simultaneous assessment of tremor and bradykinesia can be achieved in free moving PD patients during their daily activities.     

A new actigraph for long-term registration of the duration andintensity of tremor and movement

Actigraphy, the long-term measurement of human movement with a small solid state recorder, is gaining acceptance as a useful method in many research fields. Currently available actigraphs assess or estimate the movement duration per time interval. However, the output gives no information on movement type or intensity, and cannot be used in subjects suffering from tremor. The present paper describes a new type of actigraph, that has been developed primarily for the long-term evaluation of motor symptoms in Parkinson patients. The device is the first to discriminate tremor from other movements and to assess both duration and intensity of the two types of movement. It is based on a Motorola 68HC805B6 microcontroller and contains: an accelerometer, programmable gain stages, programmable low- and highpass filters, a programmable level comparator, a peak detector, interface circuits, a real time clock, data storage, and control circuitry. The micro-controller performs a period amplitude sequence analysis (PASA) on the conditioned accelerometer signal, and stores four output variables (tremor duration, tremor amplitude, movement duration, and movement amplitude) at the end of programmable time intervals. The analysis of fluctuations in the motor symptoms of, e.g., Parkinson patients using this actigraph can be of great help in the pharmacological management of symptoms     

Tracking tremor frequency in spike trains using the extended Kalman smoother

Tremor is one of the most disabling symptoms in patients with many movement disorders including Parkinson's disease (PD) and essential tremor (ET). Neural tremor manifests itself as a quasi-periodic fluctuation of the firing rate. We describe a frequency tracking method based on the extended Kalman smoother (EKS) to estimate the instantaneous tremor frequency (ITF) exhibited in binary spike trains detected from neural recordings. Simulation results demonstrate that the EKS frequency tracker can estimate the ITF accurately, even though the signal of interest is not sinusoidal and the noise is not Gaussian. The EKS frequency tracker can obtain a normalized mean squared error (NMSE) as low as 0.1 and performs much better than the conventional approach based on the Hilbert transform.     

The Nature and Etiology of Normal and Alcohol Withdrawal Tremor

Alcohol withdrawal tremor is principally a postural-type tremor. That is, the tremor manifests itself most when the limbs are voluntarily maintained in a stationary position. The tremor of alcohol withdrawal is typically 5 to 20 times as large and approximately the same frequency as normal postural tremor. The fact that withdrawal tremor is uncorrelated in both outstretched hands argues against the involvement of a central pacemaker in the genesis of the tremor. Both withdrawal and normal physiologic type hand tremors are associated with synchronous firing of extensor motor units in phase with tremor velocity at the frequency of limb resonance. Both kinds of tremor are reduced by the p-adrenergic receptor blocking drug, propranolol. The reduction in tremor is caused by a decrease in the synchrony of motor units and not a decrease in the total number of units involved in maintaining limb posture. It is proposed that increased tremor during withdrawal is due to the entrainment by 1 A muscle spindle afferents of asynchronously firing motoneurons. Tremor is also increased by entrainment of motoneurons firing synchronously at 9 to 10 Hz due to an oscillation in the stretch reflex control system. Tremor reduction following propranolol is due to a decrease in entrainment because of decreased 1 A afferent inflow to the spinal cord or because of reduced sensitivity of alpha motoneurons to 1 A spindle afferent discharges.     

基于双谱分析和支持向量机的 手震颤加速度信号识别

 针对目前临床上对特发性、帕金森病、生理性等3种常见震颤误诊断的问题,本文提出了一种基于双谱分析和支持向量机识别3种不同类型震颤的新方法.首先测量3种震颤类型志愿受试者手震颤的加速度信号并分别对其用Hinich方法检验,发现该类信号具有非高斯、非线性特性,然后用适合处理该类信号的双谱分析方法提取手震颤加速度信号的双谱对角切片的特征信息,最后采用"一对一"和"一对多"两种多分类的支持向量机算法对受试人的手震颤特征进行分类.交叉验证表明"一对一"算法的平均分类正确率高于"一对多"算法,分类正确率最高达到93.13%.该方法为临床医生提供了辅助识别不同类型震颤的新途径.     

EMG-based characterization of pathological tremor using the iterated Hilbert transform

The identification and characterization of pathological tremor are necessary for the development of techniques for tremor suppression, for example, based on functional electrical stimulation. For this purpose, the amplitude and phase characteristics of the tremor signal should be estimated by effective detection techniques, either from the kinematics or from muscle recordings. This paper presents an approach for the estimation of the characteristics of pathological tremor from the surface electromyogram (EMG) signal based on the iterated Hilbert transform (IHT). It is shown that the IHT allows an asymptotically exact modeling of the tremor and the voluntary activity components in the surface EMG, and an effective demodulation of the pathological tremor parameters. The method was tested on signals generated by a recent model for tremor generation as well as experimentally recorded from patients affected by pathological tremor. The results showed the ability of the proposed approach to demodulate effectively the tremor amplitude (average correlation with imposed amplitude: R(2)=0.52), the frequency (root mean square error in frequency estimation: 2.6 Hz), and phase, as well as the degree of voluntary activity (correlation with simulated inertial load: R(2)=0.62). The application of the method to the experimental data indicated that the estimated tremor component closely resembles inertial measurements of limb movement (peak cross correlation across four patients: 0.62±0.15). Compared to the performance of empirical mode decomposition, the proposed method proved to be more accurate for tremor characterization without a priori knowledge of the tremor characteristics. This method can be used as a part of a control system in strategies for suppression of tremor.     

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 Parkinson's 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.     

基于三维卷积的帕金森患者拖步识别

冻结步态(FoG)是一种在帕金森病(PD)中常见的异常步态,而拖步则是冻结步态的一种表现形式,也是医生用来判断患者的治疗状况的重要因素,并且拖步状态也对PD患者的日常生活有很大影响。该文提出一种通过计算机视觉来实现患者拖步状态自动识别的方法,该方法通过以3维卷积为基础的网络结构,可以从PD患者的TUG测试视频中自动识别出患者是否具有拖步症状。其思路是首先利用特征提取模块从经过预处理的视频序列中提取出时空特征,然后将得到的特征在不同空间和时间尺度上进行融合,之后将这些特征送入分类网络中得到相应的识别结果。在该工作中共收集364个正常步态样本和362个具有拖步状态的样本来构成实验数据集,在该数据集上的实验表明,该方法的平均准确率能够达到91.3%。并且其能从临床常用的TUG测试视频中自动准确地识别出患者的拖步状态,这也为远程监测帕金森病患者的治疗状态提供了助力。     

Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease

There has been considerable recent research into the connection between Parkinson's disease (PD) and speech impairment. Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to predict PD symptom severity using speech signals have been introduced. In this paper, we test how accurately these novel algorithms can be used to discriminate PD subjects from healthy controls. In total, we compute 132 dysphonia measures from sustained vowels. Then, we select four parsimonious subsets of these dysphonia measures using four feature selection algorithms, and map these feature subsets to a binary classification response using two statistical classifiers: random forests and support vector machines. We use an existing database consisting of 263 samples from 43 subjects, and demonstrate that these new dysphonia measures can outperform state-of-the-art results, reaching almost 99% overall classification accuracy using only ten dysphonia features. We find that some of the recently proposed dysphonia measures complement existing algorithms in maximizing the ability of the classifiers to discriminate healthy controls from PD subjects. We see these results as an important step toward noninvasive diagnostic decision support in PD.     

Quantitative Evaluation of Long-Term Parkinson Tremor

Because of wide temporal fluctuations in the severity of Parkinson tremor, long-term observation is required to objectively assess the effects of therapy. This study was conducted to design a system for long-term recording of tremor in the digits or wrist, to examine the stochastic properties of tremor, and to develop a means for temporally compressing the data for rapid clinical interpretation. Forty patients were studied. A telemetry system was used for data acquistion to allow the patients freedom of movement during the recording session. Recording length ranged from ?to 6 h with a mean of 2 h. The data were found to be self-stationary in the wide sense over 1-min epochs. The statistical properties varied with time in individual subjects and there was little uniformity among different subjects. Cross-correlation and cross-spectral density analyses indicated that tremor at corresponding sites in contralateral limbs is relatively independent. Graphs of percent on time, mean frequency, and mean amplitude for contiguous 3-5-min epochs were found most suitable for clinical purposes.     

Toward a direct brain interface based on human subdural recordings and wavelet-packet analysis

Highly accurate asynchronous detection of movement related patterns in individual electrocorticogram channels has been shown using detection based on either event-related potentials (ERPs) or event-related desynchronization and synchronization (ERD/ERS). A method using wavelet-packet features selected with a genetic algorithm was proposed to simultaneously detect ERP and ERD/ERS and was tested on data from seven subjects and four motor tasks. The proposed wavelet method performed better than previous methods with perfect detection for four subject/task combinations and hit percentages greater than 90% with false positive percentages less than 15% for at least one task for all seven subjects.     

A Quiet Standing Index for Testing the Postural Sway of Healthy and Diabetic Adults Across a Range of Ages

A quiet standing index is developed for tracking the postural sway of healthy and diabetic adults over a range of ages. Several postural sway features are combined into a single composite feature C that increases with age a. Sway features are ranked based on the r ²-values of their linear regression models, and the composite feature is a weighted sum of selected sway features with optimal weighting coefficients determined using principal component analysis. A performance index based on both reliability and sensitivity is used to determine the optimal number of features. The features used to form C include power and distance metrics. The quiet standing index is a scalar that compares the composite feature C to a linear regression model f(a) using C ^'(a) = C/f(a). For a motionless subject, C ^' = 0, and when the composite feature exactly matches the healthy control (HC) model, C ^' = 1. Values of C ^' >> 1 represent excessive postural sway and may indicate impaired postural control. Diabetic neurologically intact subjects, nondiabetic peripheral neuropathy subjects (PN), and diabetic PN subjects (DPN) were evaluated. The quiet standing indexes of the PN and DPN groups showed statistically significant increases over the HC group. Changes in the quiet standing index over time may be useful in identifying people with impaired balance who may be at an increased risk of falling.     

A web-based system for home monitoring of patients with Parkinson's disease using wearable sensors

This letter introduces MercuryLive, a platform to enable home monitoring of patients with Parkinson's disease (PD) using wearable sensors. MercuryLive contains three tiers: a resource-aware data collection engine that relies upon wearable sensors, web services for live streaming and storage of sensor data, and a web-based graphical user interface client with video conferencing capability. Besides, the platform has the capability of analyzing sensor (i.e., accelerometer) data to reliably estimate clinical scores capturing the severity of tremor, bradykinesia, and dyskinesia. Testing results showed an average data latency of less than 400 ms and video latency of about 200 ms with video frame rate of about 13 frames/s when 800 kb/s of bandwidth were available and we used a 40% video compression, and data feature upload requiring 1 min of extra time following a 10 min interactive session. These results indicate that the proposed platform is suitable to monitor patients with PD to facilitate the titration of medications in the late stages of the disease.     

Portable system for quantifying motor abnormalities in Parkinson'sdisease

To quantify motor disabilities in Parkinson's disease (PD), a compact, portable neurophysiological system based on a personal computer was designed to measure tremor, bradykinesia, and muscle tone. Tremor was detected by solid-state accelerometers and translated into a digital signal. The system displayed the root-mean-square (RMS) displacements and frequency distribution of the tremor in the horizontal and vertical planes, along with a reconstructed graphic image of the displacement. Bradykinesia was measured using a panel that detects release and depression of switches in response to auditory and visual signals; the system calculated subjects' reaction times and movement times in milliseconds. Tone at the elbow was measured by strapping the upper extremity to a lightweight low-friction cradle and then passively moving the cradle with an instrumented handle. Signals representing torque and arm angle were processed by the computer and displayed in real time on the screen with stiffness as a mean slope in Nm/degree. Clinical tests indicate that this system provides a convenient and accurate method for quantifying important aspects of the Parkinsonian syndrome and might be applicable to other movement disorders     

Hybrid threshold optimization between global image and local regions in image segmentation for melasma severity assessment

Melasma image segmentation plays a fundamental role for computerized melasma severity assessment. A method of hybrid threshold optimization between a given image and its local regions is proposed and used for melasma image segmentation. An analytic optimal hybrid threshold solution is obtained by minimizing the deviation between the given image and its segmented outcome. This optimal hybrid threshold comprises both local and global information around image pixels and is used to develop an optimal hybrid thresholding segmentation method. The developed method is firstly evaluated based on synthetic images and subsequently used for melasma segmentation and severity assessment. Statistical evaluations of experimental results based on real-world melasma images show that the proposed method outperforms other state-of-the-art thresholding segmentation methods for melasma severity assessment.     

Image deblurring method for satellite platform caused by jitter

TDI CCD camera with its multi-level integral imaging features has become the mainstream sensor of current high-resolution optical remote sensor imaging system,but if there is platform tremor in its imaging process,it will produce images blur,affecting image quality.The influence of platform tremor on imaging quality based on the imaging principle of TDI CCD camera was analyzed.According to the jitter image trajectory obtained by jitter detection,a method was proposed to eliminate image degradation caused by jitter.ZY-3 multi-spectral images were experimented.The experimental results show that the quantitative evaluation index of the image after the jitter image is improved,and the validity of the method is verified.