Long-term EMG recordings differentiate between parkinsonian and essential tremor

Automatic speech-based assessment to discriminate Parkinson's disease from essential tremor with a cross-language approach

Parkinson's disease (PD) and essential tremor (ET) are prevalent movement disorders that mainly affect elderly people, presenting diagnostic challenges due to shared clinical features. While both disorders exhibit distinct speech patterns-hypokinetic dysarthria in PD and hyperkinetic dysarthria in ET-the efficacy of speech assessment for differentiation remains unexplored. Developing technology for automatic discrimination could enable early diagnosis and continuous monitoring. However, the lack of data for investigating speech behavior in these patients has inhibited the development of a framework for diagnostic support. In addition, phonetic variability across languages poses practical challenges in establishing a universal speech assessment system. Therefore, it is necessary to develop models robust to the phonetic variability present in different languages worldwide. We propose a method based on Gaussian mixture models to assess domain adaptation from models trained in German and Spanish to classify PD and ET patients in Czech. We modeled three different speech dimensions: articulation, phonation, and prosody and evaluated the models' performance in both bi-class and tri-class classification scenarios (with the addition of healthy controls). Our results show that a fusion of the three speech dimensions achieved optimal results in binary classification, with accuracies up to 81.4 and 86.2% for monologue and /pa-ta-ka/ tasks, respectively. In tri-class scenarios, incorporating healthy speech signals resulted in accuracies of 63.3 and 71.6% for monologue and /pa-ta-ka/ tasks, respectively. Our findings suggest that automated speech analysis, combined with machine learning is robust, accurate, and can be adapted to different languages to distinguish between PD and ET patients.

Upper limb intention tremor assessment: opportunities and challenges in wearable technology

BACKGROUND

Tremors are involuntary rhythmic movements commonly present in neurological diseases such as Parkinson's disease, essential tremor, and multiple sclerosis. Intention tremor is a subtype associated with lesions in the cerebellum and its connected pathways, and it is a common symptom in diseases associated with cerebellar pathology. While clinicians traditionally use tests to identify tremor type and severity, recent advancements in wearable technology have provided quantifiable ways to measure movement and tremor using motion capture systems, app-based tasks and tools, and physiology-based measurements. However, quantifying intention tremor remains challenging due to its changing nature.

METHODOLOGY & RESULTS

This review examines the current state of upper limb tremor assessment technology and discusses potential directions to further develop new and existing algorithms and sensors to better quantify tremor, specifically intention tremor. A comprehensive search using PubMed and Scopus was performed using keywords related to technologies for tremor assessment. Afterward, screened results were filtered for relevance and eligibility and further classified into technology type. A total of 243 publications were selected for this review and classified according to their type: body function level: movement-based, activity level: task and tool-based, and physiology-based. Furthermore, each publication's methods, purpose, and technology are summarized in the appendix table.

CONCLUSIONS

Our survey suggests a need for more targeted tasks to evaluate intention tremors, including digitized tasks related to intentional movements, neurological and physiological measurements targeting the cerebellum and its pathways, and signal processing techniques that differentiate voluntary from involuntary movement in motion capture systems.

Wearable sensor-based gait analysis to discriminate early Parkinson's disease from essential tremor

BACKGROUND

Differentiating early-stage Parkinson's disease (PD) from essential tremor (ET) is challenging since they have some overlapping clinical features. Since early-stage PD may present with slight gait impairment and ET generally does not, gait analysis could be used to differentiate PD from ET using machine learning.

OBJECTIVE

To differentiate early-stage PD from ET via machine learning using gait and postural transition parameters calculated using the raw kinematic signal captured from inertial measurement unit (IMU) sensors.

METHODS

Gait and postural transition parameters were collected from 84 early-stage PD and 80 ET subjects during the Time Up and Go (TUG) test. We randomly split our data into training and test data. Within the training data, we separated the TUG test into four components: standing, straight walk, turning, and sitting to build weighted average ensemble classification models. The four components' weight indices were trained using logistic regression. Several ensemble models' leave-one-out cross-validation (LOOCV) performances were compared. Independent test data were used to evaluate the model with the best LOOCV performance.

RESULTS

The best weighted average ensemble classification model LOOCV results included an accuracy of 84%, Kappa of 0.68, sensitivity of 85.9%, specificity of 82.1%, and AUC of 0.912. Thirty-three gait and postural transition parameters, such as Arm-Symbolic Symmetry Index and 180° Turn-Max Angular Velocity, were included in Feature Group III. The independent test data achieved a 75.8% accuracy.

CONCLUSIONS

Our findings suggest that gait and postural transition parameters obtained from wearable sensors combined with machine learning had the potential to distinguish between early-stage PD and ET.

Investigation of Phase Shifts Using AUC Diagrams: Application to Differential Diagnosis of Parkinson's Disease and Essential Tremor

This study was motivated by the well-known problem of the differential diagnosis of Parkinson's disease and essential tremor using the phase shift between the tremor signals in the antagonist muscles of patients. Different phase shifts are typical for different diseases; however, it remains unclear how this parameter can be used for clinical diagnosis. Neurophysiological papers have reported different estimations of the accuracy of this parameter, which varies from insufficient to 100%. To address this issue, we developed special types of area under the ROC curve (AUC) diagrams and used them to analyze the phase shift. Different phase estimations, including the Hilbert instantaneous phase and the cross-wavelet spectrum mean phase, were applied. The results of the investigation of the clinical data revealed several regularities with opposite directions in the phase shift of the electromyographic signals in patients with Parkinson's disease and essential tremor. The detected regularities provide insights into the contradictory results reported in the literature. Moreover, the developed AUC diagrams show the potential for the investigation of neurodegenerative diseases related to the hyperkinetic movements of the extremities and the creation of high-accuracy methods of clinical diagnosis.

A standardized accelerometry method for characterizing tremor: Application and validation in an ageing population with postural and action tremor

Background

Ordinal scales based on qualitative observation are the mainstay in the clinical assessment of tremor, but are limited by inter-rater reliability, measurement precision, range, and ceiling effects. Quantitative tremor evaluation is well-developed in research, but clinical application has lagged, in part due to cumbersome mathematical application and lack of established standards.

Objectives

To develop a novel method for evaluating tremor that integrates a standardized clinical exam, wrist-watch accelerometers, and a software framework for data analysis that does not require advanced mathematical or computing skills. The utility of the method was tested in a sequential cohort of patients with predominant postural and action tremor presenting to a specialized surgical clinic with the presumptive diagnosis of Essential Tremor (ET).

Methods

Wristwatch accelerometry was integrated with a standardized clinical exam. A MATLAB application was developed for automated data analysis and graphical representation of tremor. Measures from the power spectrum of acceleration of tremor in different upper limb postures were derived in 25 consecutive patients. The linear results from accelerometry were correlated with the commonly used non-linear Clinical Rating Scale for Tremor (CRST).

Results

The acceleration power spectrum was reliably produced in all consecutive patients. Tremor frequency was stable in different postures and across patients. Both total and peak power of acceleration during postural conditions correlated well with the CRST. The standardized clinical examination with integrated accelerometry measures was therefore effective at characterizing tremor in a population with predominant postural and action tremor. The protocol is also illustrated on repeated measures in an ET patient who underwent Magnetic Resonance-Guided Focused Ultrasound thalamotomy.

Conclusion

Quantitative assessment of tremor as a continuous variable using wristwatch accelerometry is readily applicable as a clinical tool when integrated with a standardized clinical exam and a user-friendly software framework for analysis. The method is validated for patients with predominant postural and action tremor, and can be adopted for characterizing tremor of different etiologies with dissemination in a wide variety of clinical and research contexts in ageing populations.

New horizons in late-onset essential tremor: a pre-cognitive biomarker of dementia?

Essential tremor (ET) is the most common cause of tremor in older adults. However, it is increasingly recognised that 30–50% of ET cases are misdiagnosed. Late-onset ET, when tremor begins after the age of 60, is particularly likely to be misdiagnosed and there is mounting evidence that it may be a distinct clinical entity, perhaps better termed ‘ageing-related tremor’. Compared with older adults with early-onset ET, late-onset ET is associated with weak grip strength, cognitive decline, dementia and mortality. This raises questions around whether late-onset ET is a pre-cognitive biomarker of dementia and whether modification of dementia risk factors may be particularly important in this group. On the other hand, it is possible that the clinical manifestations of late-onset ET simply reflect markers of healthy ageing, or frailty, superimposed on typical ET. These issues are important to clarify, especially in the era of specialist neurosurgical treatments for ET being increasingly offered to older adults, and these may not be suitable in people at high risk of cognitive decline. There is a pressing need for clinicians to understand late-onset ET, but this is challenging when there are so few publications specifically focussed on this subject and no specific features to guide prognosis. More rigorous clinical follow-up and precise phenotyping of the clinical manifestations of late-onset ET using accessible computer technologies may help us delineate whether late-onset ET is a separate clinical entity and aid prognostication.

Discrimination between parkinsonian tremor and essential tremor using artificial neural network with hybrid features

BACKGROUND

Essential tremor (ET) and the tremor in Parkinson's disease (PD) are the two most common pathological tremors with a certain overlap in the clinical presentation.

OBJECTIVE

The main purpose of this work is to use an artificial neural network to select the best features and to discriminate between the two types of tremors. The features used are of hybrid type obtained from two different algorithms: the statistical signal characterization (SSC) of the signal describing its morphology, and the soft-decision wavelet-decomposition (SDWD) features extracted from the accelerometer and surface EMG signals.

METHODS

The SSC method is used to obtain morphology-based features of the spectrum of the accelerometer and two surface EMG signals. The SDWD technique is used in this work to obtain the approximate spectral representation of both accelerometer and the two surface EMG signals. Two sets of data (training and test) are used in this paper. The training set consists of 21 ET subjects and 19 PD subjects, while the test set consists of 20 ET and 20 PD subjects. A neural network of the type feed forward back propagation has been used to combine best SSC features and best SDWD features of the accelerometer and EMG signals.

RESULTS

Efficiency result of 92.5% was obtained using best hybrid features.

CONCLUSIONS

The artificial neural network has been used successfully to combine two types of features in an automatic discrimination system between PD and ET.

A Pilot Study on Long-term Physiological Signal Monitoring using Anhydrous Viscoplastic Electrodes

Electrocardiography (ECG) and Electromyogram (EMG) are widely used to help physicians to diagnose various diseases. Besides, long-term physiological signals monitoring is of great significance for circumstances where certain diseases may not be observed in short-term monitoring. At present, wet electrodes are widely used in the clinic and are considered as a standard method to acquire physiological signals in high fidelity. However, current wet electrodes achieve high-quality signal acquisition by using conductive gel which will dry up as time elapses and finally leads to degradation of the signal quality. Therefore, an anhydrous viscoplastic electrode was proposed in this paper to solve the abovementioned problem. The proposed electrode, which is anhydrous and viscoplastic, enables high quality physiological signal acquisition with firm contact with the skin and it will not dry up within a long period of time. The results showed that the impedance of the proposed viscoplastic electrode could maintain relative stability after two days while that of the gel electrodes would increase significantly due to the gel dried up. Besides, the proposed electrode obtained physiological signals with high quality in both ECG and EMG tasks. After 24 hours of monitoring, the signal quality of the proposed electrode remained unchanged, indicated by the clearly recognizable time-domain signals. However, the signal waveform completely submerged in noise after the gel dried up. Moreover, the superior performance of the viscoplastic electrodes could be confirmed by the SNR difference between the two days, SNR further confirmed the superiority of the, with -2.03±2.10 dB and -3.40±8.27 dB for ECG and EMG respectively, and the SNR difference of gel electrodes were -7.59 ± 5.70 dB and -35.39±15.71 dB respectively. The proposed electrodes could be a great candidate for long-term physiological signal monitoring in risk management of healthcare.Clinical Relevance- The proposed electrode could achieve long-term physiological signals monitoring with high quality.

Technology-based therapy-response and prognostic biomarkers in a prospective study of a de novo Parkinson's disease cohort

Early noninvasive reliable biomarkers are among the major unmet needs in Parkinson's disease (PD) to monitor therapy response and disease progression. Objective measures of motor performances could allow phenotyping of subtle, undetectable, early stage motor impairments of PD patients. This work aims at identifying prognostic biomarkers in newly diagnosed PD patients and quantifying therapy-response. Forty de novo PD patients underwent clinical and technology-based kinematic assessments performing motor tasks (MDS-UPDRS part III) to assess tremor, bradykinesia, gait, and postural stability (T0). A visit after 6 months (T1) and a clinical and kinematic assessment after 12 months (T2) where scheduled. A clinical follow-up was provided between 30 and 36 months after the diagnosis (T3). We performed an ANOVA for repeated measures to compare patients' kinematic features at baseline and at T2 to assess therapy response. Pearson correlation test was run between baseline kinematic features and UPDRS III score variation between T0 and T3, to select candidate kinematic prognostic biomarkers. A multiple linear regression model was created to predict the long-term motor outcome using T0 kinematic measures. All motor tasks significantly improved after the dopamine replacement therapy. A significant correlation was found between UPDRS scores variation and some baseline bradykinesia (toe tapping amplitude decrement, p = 0.009) and gait features (velocity of arms and legs, sit-to-stand time, p = 0.007; p = 0.009; p = 0.01, respectively). A linear regression model including four baseline kinematic features could significantly predict the motor outcome (p = 0.000214). Technology-based objective measures represent possible early and reproducible therapy-response and prognostic biomarkers.

Tremor assessment using smartphone sensor data and fuzzy reasoning

Background

Tremor severity assessment is an important step for the diagnosis and treatment decision-making of essential tremor (ET) patients. Traditionally, tremor severity is assessed by using questionnaires (e.g., ETRS and QUEST surveys). In this work we assume the possibility of assessing tremor severity using sensor data and computerized analyses. The goal of this work is to assess severity of tremor objectively, to be better able to asses improvement in ET patients due to deep brain stimulation or other treatments.

Methods

We collect tremor data by strapping smartphones to the wrists of ET patients. The resulting raw sensor data is then pre-processed to remove any artifact due to patient’s intentional movement. Finally, this data is exploited to automatically build a transparent, interpretable, and succinct fuzzy model for the severity assessment of ET. For this purpose, we exploit pyFUME, a tool for the data-driven estimation of fuzzy models. It leverages the FST-PSO swarm intelligence meta-heuristic to identify optimal clusters in data, reducing the possibility of a premature convergence in local minima which would result in a sub-optimal model. pyFUME was also combined with GRABS, a novel methodology for the automatic simplification of fuzzy rules.

Results

Our model is able to assess tremor severity of patients suffering from Essential Tremor, notably without the need for subjective questionnaires nor interviews. The fuzzy model improves the mean absolute error (MAE) metric by 78–81% compared to linear models and by 71–74% compared to a model based on decision trees.

Conclusion

This study confirms that tremor data gathered using the smartphones is useful for the constructing of machine learning models that can be used to support the diagnosis and monitoring of patients who suffer from Essential Tremor. The model produced by our methodology is easy to inspect and, notably, characterized by a lower error with respect to approaches based on linear models or decision trees.

Three Days of Measurement Provide Reliable Estimates of Daily Tremor Characteristics: A Pilot Study in Organic and Functional Tremor Patients

Background

Long-term tremor recording is particularly useful for the assessment of overall severity and therapeutic interventions in tremor patients. The purpose of this paper is to investigate the optimal number of days needed to obtain reliable estimates of tremor percentage, tremor frequency variability and tremor intensity in tremor patients using long-term tremor recordings.

Methods

Participants were 18 years or older and were diagnosed with tremor by a movement disorders specialist. Participants wore an accelerometer on the wrist of the most affected arm during 30 consecutive days. Tremor presence, frequency variability and intensity were calculated per day. We used reliability analysis to determine the minimum number of days needed to obtain reliable estimates of these tremor characteristics.

Results

Data from 36 adult organic (OrgT) and functional tremor (FT) patients (24 males; mean age 63.9 ± 11.9 years; 15 FT) were analyzed. Using five hours per day, one day of measurement is enough, except for tremor frequency variability in the OrgT group, where three days are needed and for tremor intensity where two days are always needed.

Discussion

Visual analysis suggested that reliability can be increased considerably by using data from three days instead of one day even when using six hours of data per day. Three days with at least three hours of tremor data provide estimates of tremor percentage, frequency variability and intensity with good to excellent reliability, both for organic and functional tremor.

Quantifying upper limb tremor in people with multiple sclerosis using Fast Fourier Transform based analysis of wrist accelerometer signals

Introduction

Tremor is a disabling symptom of Multiple Sclerosis (MS). The development of objective methods of tremor characterisation to assess intervention efficacy and disease progression is therefore important. The possibility of using a Fast Fourier Transform (FFT) method for tremor detection was explored.

Methods

Acceleration from a wrist-worn device was analysed using FFTs to identify and characterise tremor magnitude and frequency. Processing parameters were explored to provide insight into the optimal algorithm. Participants wore a wrist tri-axial accelerometer during 9 tasks. The FAHN clinical assessment of tremor was used as the reference standard.

Results

Five people with MS and tremor (57.6 ± 15.3 years, 3 F/2M) and ten disease-free controls (42.4 ± 10.9 years, 5 M/5F) took part. Using specific algorithm settings tremor identification was possible (peak frequency 3–15Hz; magnitude greater than 0.06 g; 2 s windows with 50% overlap; using 2 of 3 axes of acceleration), giving sensitivity 0.974 and specificity 0.971 (38 tremor occurrences out of 108 tasks, 1 false positive, 2 false negatives). Tremor had frequency 3.5–13.0 Hz and amplitude 0.07–2.60g.

Conclusions

Upper limb tremor in people with MS can be detected using a FFT approach based on acceleration recorded at the wrist, demonstrating the possibility of using this minimally encumbering technique within clinical practice.

Development and Validation of a New Wearable Mobile Device for the Automated Detection of Resting Tremor in Parkinson's Disease and Essential Tremor

Involuntary tremor at rest is observed in patients with Parkinson's disease (PD) or essential tremor (ET). Electromyography (EMG) studies have shown that phase displacement between antagonistic muscles at prevalent tremor frequency can accurately differentiate resting tremor in PD from that detected in ET. Currently, phase evaluation is qualitative in most cases. The aim of this study is to develop and validate a new mobile tool for the automated and quantitative characterization of phase displacement (resting tremor pattern) in ambulatory clinical settings. A new low-cost, wearable mobile device, called µEMG, is described, based on low-end instrumentation amplifiers and simple digital signal processing (DSP) capabilities. Measurements of resting tremor characteristics from this new device were compared with standard EMG. A good level of agreement was found in a sample of 21 subjects (14 PD patients with alternating resting tremor pattern and 7 ET patients with synchronous resting tremor pattern). Our results demonstrate that tremor analysis using µEMG is easy to perform and it can be used in routine clinical practice for the automated quantification of resting tremor patterns. Moreover, the measurement process is handy and operator-independent.

Ambulatory surface electromyography with accelerometry for evaluating daily motor fluctuations in Parkinson's disease

OBJECTIVE

To evaluate motor fluctuations in patients with advanced Parkinson's disease (PD) using a small-sized wearable device for surface electromyography (EMG) with accelerometry (ACC) for 24 hours.

METHODS

Seven PD patients with medication were measured once, and nine patients with directional deep brain stimulation (dDBS) twice: before and after the dDBS reprogramming. EMG and ACC parameters were compared with clinical rating scores and patients' home diaries.

RESULTS

The combination of EMG and ACC parameters (first principal component PC₁) correlated significantly with patient's condition as quantified by the motor score of Unified Parkinson's Disease Rating Scale and it changed significantly with dDBS reprogramming in line with decreased PD symptoms. Monitoring data detected in comparison with the home diaries: 91 % concordance with tremor, 76 % with rigidity, and 74 % with dyskinesia. In the DBS group, the wake-up time with abnormal neuromuscular function was reduced with reprogramming in all except one patient based on measurements.

CONCLUSIONS

A wearable device measuring simultaneously both muscle activity and motion can provide continuous and dynamic information about patient's condition and motor fluctuations at home.

SIGNIFICANCE

The present method may help to modify pharmacologic management and DBS treatment in advanced PD.

A Multi-Sensor Wearable System for the Quantitative Assessment of Parkinson's Disease

The quantitative characterization of movement disorders and their related neurophysiological signals is important for the management of Parkinson’s disease (PD). The aim of this study is to develop a novel wearable system enabling the simultaneous measurement of both motion and other neurophysiological signals in PD patients. We designed a wearable system that consists of five motion sensors and three electrophysiology sensors to measure the motion signals of the body, electroencephalogram, electrocardiogram, and electromyography, respectively. The data captured by the sensors are transferred wirelessly in real time, and the outcomes are analyzed and uploaded to the cloud-based server automatically. We completed pilot studies to (1) test its validity by comparing outcomes to the commercialized systems, and (2) evaluate the deep brain stimulation (DBS) treatment effects in seven PD patients. Our results showed: (1) the motion and neurophysiological signals measured by this wearable system were strongly correlated with those measured by the commercialized systems (r > 0.94, p < 0.001); and (2) by completing the clinical supination and pronation frequency test, the frequency of motion as measured by this system increased when DBS was turned on. The results demonstrated that this multi-sensor wearable system can be utilized to quantitatively characterize and monitor motion and neurophysiological PD.

Discrimination of physiological tremor from pathological tremor using accelerometer and surface EMG signals

BACKGROUND AND OBJECTIVE: Although careful clinical examination and medical history are the most important steps towards a diagnostic separation between different tremors, the electro-physiological analysis of the tremor using accelerometry and electromyography (EMG) of the affected limbs are promising tools. METHODS: A soft-decision wavelet-based decomposition technique is applied with 8 decomposition stages to estimate the power spectral density of accelerometer and surface EMG signals (sEMG) sampled at 800 Hz. A discrimination factor between physiological tremor (PH) and pathological tremor, namely, essential tremor (ET) and the tremor caused by Parkinson’s disease (PD), is obtained by summing the power entropy in band 6 (B6: 7.8125–9.375 Hz) and band 11 (B11: 15.625–17.1875 Hz). RESULTS: A discrimination accuracy of 93.87% is obtained between the PH group and the ET & PD group using a voting between three results obtained from the accelerometer signal and two sEMG signals. CONCLUSION: Biomedical signal processing techniques based on high resolution wavelet spectral analysis of accelerometer and sEMG signals are implemented to efficiently perform classification between physiological tremor and pathological tremor.

Distinct cortical activity patterns in Parkinson's disease and essential tremor during a bimanual tapping task

Background

Parkinson’s disease (PD) and essential tremor (ET) are neurodegenerative diseases characterized by movement deficits. Especially in PD, maintaining cyclic movement can be significantly disturbed due to pathological changes in the basal ganglia and the cerebellum. Providing external cues improves timing of these movements in PD and also affects ET. The aim of this study is to determine differences in cortical activation patterns in PD and ET patients during externally and internally cued movements.

Methods

Eleven PD patients, twelve ET patients, OFF tremor suppressing medication, and nineteen age-matched healthy controls (HC) were included and asked to perform a bimanual tapping task at two predefined cue frequencies. The auditory cue, a metronome sound presented at 2 or 4 Hz, was alternately switched on and off every 30 s. Tapping at two different frequencies were used since it is expected that different brain networks are involved at different frequencies as has been shown in previous studies. Cortical activity was recorded using a 64-channel EEG cap. To establish the cortical activation pattern in each group, the task related power (TRP) was calculated for each subject. For inter-groups analysis, EEG electrodes for divided into 5 different areas.

Results

Inter-group analysis revealed significant differences in areas responsible for motor planning, organization and regulation and involved in initiation, maintenance, coordination and planning of complex sequences of movements. Within the area of the primary motor cortex the ET group showed a significantly lower TRP than the HC group. In the area responsible for combining somatosensory, auditory and visual information both patient groups had a higher TRP than the HC group.

Conclusions

Different neurological networks are involved during cued and non-cued movements in ET, PD and HC. Distinct cortical activation patterns were revealed using task related power calculations. Different activation patterns were revealed during the 2 and 4 Hz tapping task indicating different strategies to execute movements at these rates. The results suggest that a including a cued/non-cued tapping task during clinical decision making could be a valuable tool in an objective diagnostic protocol.

A Power Spectral Density-Based Method to Detect Tremor and Tremor Intermittency in Movement Disorders

There is no objective gold standard to detect tremors. This concerns not only the choice of the algorithm and sensors, but methods are often designed to detect tremors in one specific group of patients during the performance of a specific task. Therefore, the aim of this study is twofold. First, an objective quantitative method to detect tremor windows (TWs) in accelerometer and electromyography recordings is introduced. Second, the tremor stability index (TSI) is determined to indicate the advantage of detecting TWs prior to analysis. Ten Parkinson’s disease (PD) patients, ten essential tremor (ET) patients, and ten healthy controls (HC) performed a resting, postural and movement task. Data was split into 3-s windows, and the power spectral density was calculated for each window. The relative power around the peak frequency with respect to the power in the tremor band was used to classify the windows as either tremor or non-tremor. The method yielded a specificity of 96.45%, sensitivity of 84.84%, and accuracy of 90.80% of tremor detection. During tremors, significant differences were found between groups in all three parameters. The results suggest that the introduced method could be used to determine under which conditions and to which extent undiagnosed patients exhibit tremors.

Long-term recording of electromyographic activity from multiple muscles to monitor physical activity of participants with or without a neurological disorder

Various portable monitors have been used to quantify physical activity but most rely on detecting limb movement with a sensor rather than measuring muscle activity. Our first goal was to design and validate a portable system for recording surface electromyographic activity (EMG) from eight muscles over 24 h. The modular system includes: (1) preamplifiers that filter and amplify signals; (2) a preprocessor unit for further filtering and amplification, signal offset and power supply modification; (3) a data-logger for analog-to-digital conversion; a flash memory card for data storage and (4) a rechargeable battery. The equipment samples EMG at 1000 Hz, has a resolution of 2.6 μV and records signals up to 10 mV. The built-in analog filters create a bandwidth appropriate for surface EMG. Our second aim was to test the system biologically by recording EMG from able-bodied and spinal cord injured participants. Modifications were made to electrodes for remote preamplifier placement, and to the battery connection after pilot testing. Thereafter, 31 consecutive 24-h EMG recordings were successful. Both the engineering and biological validation of this system establishes it as a valuable tool for measuring physical activity from different muscles in real-world environments whether individuals have an intact or damaged nervous system.

Assessment of Motor Impairments in Early Untreated Parkinson's Disease Patients: The Wearable Electronics Impact

OBJECTIVE

The complex nature of Parkinson's disease (PD) makes difficult to rate its severity, mainly based on the visual inspection of motor impairments. Wearable sensors have been demonstrated to help overcoming such a difficulty, by providing objective measures of motor abnormalities. However, up to now, those sensors have been used on advanced PD patients with evident motor impairment. As a novelty, here we report the impact of wearable sensors in the evaluation of motor abnormalities in newly diagnosed, untreated, namely de novo, patients.

METHODS

A network of wearable sensors was used to measure motor capabilities, in 30 de novo PD patients and 30 healthy subjects, while performing five motor tasks. Measurement data were used to determine motor features useful to highlight impairments and were compared with the corresponding clinical scores. Three classifiers were used to differentiate PD from healthy subjects.

RESULTS

Motor features gathered from wearable sensors showed a high degree of significance in discriminating the early untreated de novo PD patients from the healthy subjects, with 95% accuracy. The rates of severity obtained from the measured features are partially in agreement with the clinical scores, with some highlighted, though justified, exceptions.

CONCLUSION

Our findings support the feasibility of adopting wearable sensors in the detection of motor anomalies in early, untreated, PD patients.

SIGNIFICANCE

This work demonstrates that subtle motor impairments, occurring in de novo patients, can be evidenced by means of wearable sensors, providing clinicians with instrumental tools as suitable supports for early diagnosis, and subsequent management.

Tensometric tremorography in high-precision medical diagnostic systems

BACKGROUND

The objective of the study was to develop a system for the precision diagnostics of pathologies of motor brain regions based on tensometric measurement and to explore its diagnostic capabilities.

MATERIALS AND METHODS

Tremor is a syndrome that indicates the abnormal state of the central nervous system, primarily in the motor brain regions. Analysis of tremor parameters provides significant information about the changes in the body motion control and can be used as an objective index of the central nervous system state. Existing methods are aimed at the analysis of visible tremor based on the use of different sensors. We suggest an alternative approach based on the use of a tensometric system performing tremor measurements when the tremor appears on the background of voluntary isometric efforts. The key advantage of our approach is that it allows to determine the tremor before its visible manifestation. In the article, we describe hardware implementation of our tremor analysis system.

RESULTS

In the article, we represent the new methodology and the original equipment based on the control of isometric effort. Isometric effort formed by a patient is controlled with the use of a feedback system on the patient's monitor. We evaluated the performance of our equipment with more than 400 healthy volunteers and patients with various pathologies of the central nervous system motor regions, and the results of the investigations, allowing to identify tremor parameters typical for parkinsonism, are represented in our article.

CONCLUSION

Testing of the system confirmed its high diagnostic validity and reliability, high sensitivity, simplicity and high speed of information processing. The approach based on tensometric measurements is very promising for the diagnostics of Parkinson disease and dysfunctions of a central nervous system.

A method for characterizing essential tremor from the shoulder to the wrist

BACKGROUND

Despite the pervasive and devastating effect of Essential Tremor (ET), the distribution of ET throughout the upper limb is unknown. We developed a method for characterizing the distribution of ET and performed a preliminary characterization in a small number of subjects with ET.

METHODS

Using orientation sensors and inverse kinematics, we measured tremor in each of the seven major degrees of freedom (DOF) from the shoulder to the wrist while ten patients with mild ET assumed 16 different postures. We described the tremor in each DOF in terms of power spectral density measures and investigated how tremor varied between DOF, postures, gravitational torques, and repetitions.

FINDINGS

Our method successfully resulted in tremor measures in each DOF, allowing one to compare tremor between DOF and determine the distribution of tremor throughout the upper limb, including how the distribution changes with posture. In our small number of subjects, we found that the amount of power in the frequency band associated with ET (4-12Hz) was lowest in the shoulder and greatest in the wrist. Similarly, the existence and amplitude of peaks in this band increased from proximal to distal. Although the amount of tremor differed significantly between postures, we did not find any clear patterns with changes in posture or gravitational torque.

INTERPRETATION

This method can be used to characterize the distribution of tremor throughout the upper limb. Our preliminary characterization suggests that the amount of tremor increases in a proximal-distal manner.

Hand Tremor Questionnaire: A Useful Screening Tool for Differentiating Patients with Hand Tremor between Parkinson's Disease and Essential Tremor

Background and Purpose

Hand tremor is one of the most frequent symptoms in movement disorders, and differential diagnoses for hand tremor include Parkinson's disease (PD) and essential tremor (ET). However, accurately differentiating between PD and ET in clinical practice remains challenging in patients presenting with hand tremor. We investigated whether a questionnaire-based survey could be useful as a screening tool in patients with hand tremor.

Methods

A questionnaire related to hand tremor consisting of 12 items was prospectively applied to patients with PD or ET in three movement-disorder clinics. Each question was analyzed, and a query-based scoring system was evaluated for differentiating hand tremors between PD and ET.

Results

This study enrolled 24 patients with PD and 25 patients with ET. Nine of the 12 questions differed significantly between PD and ET: 1 about resting tremor, 4 questions about action tremor, and 4 about asymmetry. A receiver operating characteristics curve analysis revealed that the 9-item questionnaire showed a good discrimination ability, with a sensitivity of 88% and a specificity of 84%.

Conclusions

The developed Hand Tremor Questionnaire might be a good screening tool for hand tremors in patients with PD and ET.

Objective identification of upper limb tremor in multiple sclerosis using a wrist-worn motion sensor: Establishing validity and reliability

Introduction

Over 25% of people with multiple sclerosis experience tremor, which may impact on activities of daily living and quality of life. Yet there is no method to objectively measure tremor and effectiveness of interventions on tremor. This study aimed to test validity and reliability of a new objective measurement for upper limb tremor in people with multiple sclerosis.

Method

Twelve participants with multiple sclerosis who self-reported tremor were observed performing standardised tasks. Validity and reliability of a new method to detect tremor from wrist movement was established against occupational therapist observation of tremor (FAHN). Concurrent validity of severity (displacement) of tremor was assessed. Responsiveness to change in tremor characteristics was explored in a sub-set of participants using weighted wrist-cuffs.

Results

The new method correctly predicted 98.2% of tremor cases identified by the occupational therapist, with high sensitivity (0.988) and specificity (0.976). Calculated displacement of tremor correlated with FAHN tremor severity scores moderately (rs = .452, p = .004). The new measure was responsive to changes in tremor characteristics due to change in weight of wrist-cuffs.

Conclusion

The new method of characterising tremor in those with multiple sclerosis demonstrated excellent validity and reliability in relation to tremor identified by an occupational therapist, and could provide valuable objective insight into the efficacy of interventions.

Evaluation of muscular activity duration in shoulders with rotator cuff tears using inertial sensors and electromyography

Shoulder disorders, including rotator cuff tears, affect the shoulder function and result in adapted muscle activation. Although these adaptations have been studied in controlled conditions, free-living activities have not been investigated. Based on the kinematics measured with inertial sensors and portable electromyography, the objectives of this study were to quantify the duration of the muscular activation in the upper trapezius (UT), medial deltoid (MD) and biceps brachii (BB) during motion and to investigate the effect of rotator cuff tear in laboratory settings and daily conditions. The duration of movements and muscular activations were analysed separately and together using the relative time of activation (T(EMG/mov)). Laboratory measurements showed the parameter's reliability through movement repetitions (ICC > 0.74) and differences in painful shoulders compared with healthy ones (p < 0.05): longer activation for UT; longer activation for MD during abduction and tendency to shorter activation in other movements; shorter activation for BB. In daily conditions, T(EMG/mov) for UT was longer, whereas it was shorter for MD and BB (p < 0.05). Moreover, significant correlations were observed between these parameters and clinical scores. This study thus provides new insights into the rotator cuff tear effect on duration of muscular activation in daily activity.

Corticomuscular transmission of tremor signals by propriospinal neurons in Parkinson's disease

Cortical oscillatory signals of single and double tremor frequencies act together to cause tremor in the peripheral limbs of patients with Parkinson's disease (PD). But the corticospinal pathway that transmits the tremor signals has not been clarified, and how alternating bursts of antagonistic muscle activations are generated from the cortical oscillatory signals is not well understood. This paper investigates the plausible role of propriospinal neurons (PN) in C3–C4 in transmitting the cortical oscillatory signals to peripheral muscles. Kinematics data and surface electromyogram (EMG) of tremor in forearm were collected from PD patients. A PN network model was constructed based on known neurophysiological connections of PN. The cortical efferent signal of double tremor frequencies were integrated at the PN network, whose outputs drove the muscles of a virtual arm (VA) model to simulate tremor behaviors. The cortical efferent signal of single tremor frequency actuated muscle spindles. By comparing tremor data of PD patients and the results of model simulation, we examined two hypotheses regarding the corticospinal transmission of oscillatory signals in Parkinsonian tremor. Hypothesis I stated that the oscillatory cortical signals were transmitted via the mono-synaptic corticospinal pathways bypassing the PN network. The alternative hypothesis II stated that they were transmitted by way of PN multi-synaptic corticospinal pathway. Simulations indicated that without the PN network, the alternating burst patterns of antagonistic muscle EMGs could not be reliably generated, rejecting the first hypothesis. However, with the PN network, the alternating burst patterns of antagonist EMGs were naturally reproduced under all conditions of cortical oscillations. The results suggest that cortical commands of single and double tremor frequencies are further processed at PN to compute the alternating burst patterns in flexor and extensor muscles, and the neuromuscular dynamics demonstrated a frequency dependent damping on tremor, which may prevent tremor above 8 Hz to occur.

EFNS/MDS-ES/ENS [corrected] recommendations for the diagnosis of Parkinson's disease

BACKGROUND

A Task Force was convened by the EFNS/MDS-ES Scientist Panel on Parkinson's disease (PD) and other movement disorders to systemically review relevant publications on the diagnosis of PD.

METHODS

Following the EFNS instruction for the preparation of neurological diagnostic guidelines, recommendation levels have been generated for diagnostic criteria and investigations.

RESULTS

For the clinical diagnosis, we recommend the use of the Queen Square Brain Bank criteria (Level B). Genetic testing for specific mutations is recommended on an individual basis (Level B), taking into account specific features (i.e. family history and age of onset). We recommend olfactory testing to differentiate PD from other parkinsonian disorders including recessive forms (Level A). Screening for pre-motor PD with olfactory testing requires additional tests due to limited specificity. Drug challenge tests are not recommended for the diagnosis in de novo parkinsonian patients. There is an insufficient evidence to support their role in the differential diagnosis between PD and other parkinsonian syndromes. We recommend an assessment of cognition and a screening for REM sleep behaviour disorder, psychotic manifestations and severe depression in the initial evaluation of suspected PD cases (Level A). Transcranial sonography is recommended for the differentiation of PD from atypical and secondary parkinsonian disorders (Level A), for the early diagnosis of PD and in the detection of subjects at risk for PD (Level A), although the technique is so far not universally used and requires some expertise. Because specificity of TCS for the development of PD is limited, TCS should be used in conjunction with other screening tests. Conventional magnetic resonance imaging and diffusion-weighted imaging at 1.5 T are recommended as neuroimaging tools that can support a diagnosis of multiple system atrophy (MSA) or progressive supranuclear palsy versus PD on the basis of regional atrophy and signal change as well as diffusivity patterns (Level A). DaTscan SPECT is registered in Europe and the United States for the differential diagnosis between degenerative parkinsonisms and essential tremor (Level A). More specifically, DaTscan is indicated in the presence of significant diagnostic uncertainty such as parkinsonism associated with neuroleptic exposure and atypical tremor manifestations such as isolated unilateral postural tremor. Studies of [(123) I]MIBG/SPECT cardiac uptake may be used to identify patients with PD versus controls and MSA patients (Level A). All other SPECT imaging studies do not fulfil registration standards and cannot be recommended for routine clinical use. At the moment, no conclusion can be drawn as to diagnostic efficacy of autonomic function tests, neurophysiological tests and positron emission tomography imaging in PD.

CONCLUSIONS

The diagnosis of PD is still largely based on the correct identification of its clinical features. Selected investigations (genetic, olfactory, and neuroimaging studies) have an ancillary role in confirming the diagnosis, and some of them could be possibly used in the near future to identify subjects in a pre-symptomatic phase of the disease.

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 Parkinson's disease. The electromyographic signal from the biceps brachii muscle was measured during isometric tension from 17 patients with essential tremor, 35 patients with Parkinson's 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 Parkinson's disease groups. With this method, it was possible to discriminate 13/17 patients with essential tremor from 26/35 patients with Parkinson's disease and 14/17 patients with essential tremor from 29/40 healthy controls.

How to tackle tremor - systematic review of the literature and diagnostic work-up

Background: Tremor is the most prevalent movement disorder in clinical practice. It is defined as involuntary, rhythmic, oscillatory movements. The diagnostic process of patients with tremor can be laborious and challenging, and a clear, systematic overview of available diagnostic techniques is lacking. Tremor can be a symptom of many diseases, but can also represent a distinct disease entity. Objective: The objective of this review is to give a clear, systematic and step-wise overview of the diagnostic work-up of a patient with tremor. The clinical relevance and value of available laboratory tests in patients with tremor will be explored. Methods: We systematically searched through EMBASE. The retrieved articles were supplemented by articles containing relevant data or provided important background information. Studies that were included investigated the value and/or usability of diagnostic tests for tremor. Results: In most patients, history and clinical examination by an experienced movement disorders neurologist are sufficient to establish a correct diagnosis, and further ancillary examinations will not be needed. Ancillary investigation should always be guided by tremor type(s) present and other associated signs and symptoms. The main ancillary examination techniques currently are electromyography and SPECT imaging. Unfortunately, many techniques have not been studied in large prospective, diagnostic studies to be able to determine important variables like sensitivity and specificity. Conclusion: When encountering a patient with tremor, history, and careful clinical examination should guide the diagnostic process. Adherence to the diagnostic work-up provided in this review will help the diagnostic process of these patients.

A power-efficient bio-potential acquisition device with DS-MDE sensors for long-term healthcare monitoring applications

This work describes a power-efficient bio-potential acquisition device for long-term healthcare applications that is implemented using novel microelectromechanical dry electrodes (MDE) and a low power bio-potential processing chip. Using micromachining technology, an attempt is also made to enhance the sensing reliability and stability by fabricating a diamond-shaped MDE (DS-MDE) that has a satisfactory self-stability capability and superior electric conductivity when attached onto skin without any extra skin tissue injury technology. To acquire differential bio-potentials such as ECG signals, the proposed processing chip fabricated in a standard CMOS process has a high common mode rejection ratio (C.M.R.R.) differential amplifier and a 12-bit analog-to-digital converter (ADC). Use of the proposed system and integrate simple peripheral commercial devices can obtain the ECG signal efficiently without additional skin tissue injury and ensure continuous monitoring more than 70 hours with a 400 mAh battery.

Neurological tremor: sensors, signal processing and emerging applications

Neurological tremor is the most common movement disorder, affecting more than 4% of elderly people. Tremor is a non linear and non stationary phenomenon, which is increasingly recognized. The issue of selection of sensors is central in the characterization of tremor. This paper reviews the state-of-the-art instrumentation and methods of signal processing for tremor occurring in humans. We describe the advantages and disadvantages of the most commonly used sensors, as well as the emerging wearable sensors being developed to assess tremor instantaneously. We discuss the current limitations and the future applications such as the integration of tremor sensors in BCIs (brain-computer interfaces) and the need for sensor fusion approaches for wearable solutions.

Tremor

PURPOSE OF REVIEW

Tremor continuously attracts the attention of clinicians and basic researchers in search of pathophysiological, molecular and genetic mechanisms of the oscillatory activity.

RECENT FINDINGS

A widespread dynamic network of cortical and subcortical oscillators taking part in tremor generation intermittently has been postulated. Essential tremor is accompanied by functional deficits but may also occur along with subtle cerebellar changes. According to recent epidemiological studies there may be a link of essential tremor with Parkinson's disease. Many of the epidemiologic studies suffer from small cohorts, small effects or the lack of a definite test for essential tremor leaving the diagnosis a pure clinical one. A very recent large genome-wide association study has revealed that the LINGO1 gene is associated with an increased risk for essential tremor. Topiramate is becoming the best-established second line treatment for essential tremor. Targets for deep brain stimulation in the grey matter below the ventral intermediate nucleus of the thalamus seem to be most effective.

SUMMARY

New concepts of the central origin of tremors stimulate the search for new therapeutic targets for tremor suppression outside the basal ganglia and thalamus (e.g. cortex). The role of structural neurodegenerative changes in essential tremor remains an open question. Further studies on specific subgroups of patients are necessary.

Effects of inertia and wrist oscillations on contralateral neurological postural tremor using the wristalyzer, a new myohaptic device

Upper limb postural tremor consists of mechanical-reflex and central-neurogenic oscillations, superimposed upon a background of irregular fluctuations in muscle force. Muscle spindles play key-roles in the information flow to supra-spinal and spinal generators. Oscillations were delivered using a new generation portable myohaptic device, called ldquowristalyzer,rdquo taking into account the ergonomy of upper limbs and allowing a fine adjustment to each configuration of upper limb segments. The nominal torque of the first generation device is 4 Nm, with a maximal rotation velocity of 300 degrees/s and a range of motion of plusmn45 degrees. Reliability was assessed in basal condition and during loading conditions. We assessed the effects of the addition of inertia on postural tremor of the finger in a group of 26 neurological patients and the effects of wrist oscillations upon contralateral postural tremor in 6 control subjects and in 7 neurological patients exhibiting a postural tremor. Patients showed two different behaviors in response to inertia and exhibited an increased variability of postural tremor during fast oscillations (13.3 Hz). One patient with overactivity of the olivocerebellar pathways exhibited a drop in the peak frequency of more than 20%. The relative power of the 8-12 Hz subband was significantly higher in controls both in basal condition and during oscillations (p = 0.028 and p = 0.015, respectively). The second generation wristalyzer allows to investigate the effects of mechanical oscillations up to frequency of 50 Hz. This mechatronic device can assess the responsiveness of tremor generators to stimulation of muscle spindles and biomechanical loading. Potential applications are the monitoring of dysmetria under various inertial or damping conditions, the assessment of rigidity in Parkinson's disease and the characterization of voluntary muscle force.