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

Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor

BACKGROUND

Several validated clinical scales measure the severity of essential tremor (ET). Their assessments are subjective and can depend on familiarity and training with scoring systems.

METHOD

We propose a multi-modal sensing using a wearable inertial measurement unit for estimating scores on the Fahn-Tolosa-Marin tremor rating scale (FTM) and determine the classification accuracy within the tremor type. 17 ET participants and 18 healthy controls were recruited for the study. Two movement disorder neurologists who were blinded to prior clinical information viewed video recordings and scored the FTM. Participants drew a guided Archimedes spiral while wearing an inertial measurement unit placed at the mid-point between the lateral epicondyle of the humerus and the anatomical snuff box. Acceleration and gyroscope recordings were analyzed. The ratio of the power spectral density between frequency bands 0.5-4 Hz and 4-12 Hz, and the sum of power spectrum density over the entire spectrum of 2-74 Hz, for both accelerometer and gyroscope data, were computed. FTM was estimated using regression model and classification using SVM was validated using the leave-one-out method.

RESULTS

Regression analysis showed a moderate to good correlation when individual features were used, while correlation was high ([Formula: see text] = 0.818) when suitable features of the gyro and accelerometer were combined. The accuracy for two-class classification of the combined features using SVM was 91.42% while for four-class it was 68.57%.

CONCLUSION

Potential applications of this novel wearable sensing method using a wearable Inertial Measurement Unit (IMU) include monitoring of ET and clinical trials of new treatments for the disorder.

Athlete Muscular Phenotypes Identified and Compared with High-Dimensional Clustering of Lower Limb Muscle Volume Measurements

INTRODUCTION

Athletes use their skeletal muscles to demonstrate performance. Muscle force generating capacity is correlated with volume, meaning that variations in sizes of different muscles may be indicative of how athletes meet different demands in their sports. Medical imaging enables in vivo quantification of muscle volumes; however, muscle volume distribution has not been compared across athletes of different sports.

PURPOSE

The goal of this work was to define "muscular phenotypes" in athletes of different sports and compare these using hierarchical clustering.

METHODS

Muscle volumes normalized by body mass of athletes (football, baseball, basketball, or track) were compared with control participants to quantify size differences using z -scores. z -Scores of 35 muscles described the pattern of volume deviation within each athlete's lower limb, characterizing their muscular phenotype. Data-driven high-dimensional clustering analysis was used to group athletes presenting similar phenotypes. Efficacy of clustering to identify similar phenotypes was demonstrated by grouping athletes' contralateral limbs before other athletes' limbs.

RESULTS

Analyses revealed that athletes did not tend to cluster with others competing in the same sport. Basketball players with similar phenotypes grouped by clustering also demonstrated similarities in performance. Clustering also identified muscles with similar volume variation patterns across athletes, and principal component analysis revealed specific muscles that accounted for most of the variance (gluteus maximus, sartorius, semitendinosus, vastus medialis, vastus lateralis, and rectus femoris).

CONCLUSIONS

Athletes exhibit heterogeneous lower limb muscle volumes that can be characterized and compared as individual muscular phenotypes. Clustering revealed that athletes with the most similar phenotypes do not always play the same sport such that patterns of muscular heterogeneity across a group of athletes reflect factors beyond their specific sports.

Diagnosis and Treatment of Tremor in Parkinson's Disease Using Mechanical Devices

BACKGROUND

Parkinsonian tremors are sometimes confused with essential tremors or other conditions. Recently, researchers conducted several studies on tremor evaluation using wearable sensors and devices, which may support accurate diagnosis. Mechanical devices are also commonly used to treat tremors and have been actively researched and developed. Here, we aimed to review recent progress and the efficacy of the devices related to Parkinsonian tremors.

METHODS

The PubMed and Scopus databases were searched for articles. We searched for "Parkinson disease" and "tremor" and "device".

RESULTS

Eighty-six articles were selected by our systematic approach. Many studies demonstrated that the diagnosis and evaluation of tremors in patients with PD can be done accurately by machine learning algorithms. Mechanical devices for tremor suppression include deep brain stimulation (DBS), electrical muscle stimulation, and orthosis. In recent years, adaptive DBS and optimization of stimulation parameters have been studied to further improve treatment efficacy.

CONCLUSIONS

Due to developments using state-of-the-art techniques, effectiveness in diagnosing and evaluating tremor and suppressing it using these devices is satisfactorily high in many studies. However, other than DBS, no devices are in practical use. To acquire high-level evidence, large-scale studies and randomized controlled trials are needed for these devices.

Using Deep Learning for Task and Tremor Type Classification in People with Parkinson's Disease

Hand tremor is one of the dominating symptoms of Parkinson's disease (PD), which significantly limits activities of daily living. Along with medications, wearable devices have been proposed to suppress tremor. However, suppressing tremor without interfering with voluntary motion remains challenging and improvements are needed. The main goal of this work was to design algorithms for the automatic identification of the tremor type and voluntary motions, using only surface electromyography (sEMG) data. Towards this goal, a bidirectional long short-term memory (BiLSTM) algorithm was implemented that uses sEMG data to identify the motion and tremor type of people living with PD when performing a task. Moreover, in order to automate the training process, hyperparamter selection was performed using a regularized evolutionary algorithm. The results show that the accuracy of task classification among 15 people living with PD was 84±8%, and the accuracy of tremor classification was 88±5%. Both models performed significantly above chance levels (20% and 33% for task and tremor classification, respectively). Thus, it was concluded that the trained models, based on using purely sEMG signals, could successfully identify the task and tremor types.

EMG analysis across different tasks improves prevention screenings in diabetes: a cluster analysis approach

The aim of this work was twofold: on one side to determine the most suitable parameters of surface electromyography (sEMG) to classify diabetic subjects with and without neuropathy and discriminate them from healthy controls and second to assess the role of the task acquired in the classification process. For this purpose 30 subjects were examined (10 controls, 10 diabetics with and 10 without neuropathy) whilst walking and stair ascending and descending. The electrical activity of six muscles was recorded bilaterally through a 16-channel sEMG system synchronised with a stereophotogrammetric system: Rectus Femoris, Gluteus Medius, Tibialis Anterior, Peroneus Longus, Gastrocnemius Lateralis and Extensor Digitorum. Spatiotemporal parameters of gait and stair climbing and the following sEMG parameters were extracted: signal envelope, activity duration, timing of activation and deactivation. A hierarchical clustering algorithm was applied to the whole set of parameters with different distances and linkage methods. Results showed that only by applying the Ward agglomerative hierarchical clustering (Hamming distance) to the all set of parameters extracted from both tasks, 5 well-separated clusters were obtained: cluster 3 included only DS subjects, cluster 2 and 4 only controls and cluster 1 and 5 only DNS subjects. This method could be used for planning rehabilitation treatments.

Graphical abstract

Non-Invasive Muscular Atrophy Causes Evaluation for Limb Fracture Based on Flexible Surface Electromyography System

Muscular atrophy after limb fracture is a frequently occurring complication with multiple causes. Different treatments and targeted rehabilitation procedures should be carried out based on the causes. However, bedside evaluation methods are invasive in clinical practice nowadays, lacking reliable non-invasive methods. In this study, we propose a non-invasive flexible surface electromyography system with machine learning algorithms to distinguish nerve-injury and limb immobilization-related atrophy. First, a flexible surface electromyography sensor was designed and verified by in vitro tests for its robustness and flexibility. Then, in vivo tests on rats proved the reliability compared with the traditional invasive diagnosis method. Finally, this system was applied for the diagnosis of muscular atrophy in 10 patients. The flexible surface electromyography sensor can achieve a max strain of 12.0%, which ensures close contact with the skin. The in vivo tests on rats show great comparability with the traditional invasive diagnosis method. It can achieve a high specificity of 95.28% and sensitivity of 98.98%. Application on patients reaches a relatively high specificity of 89.44% and sensitivity of 91.94%. The proposed painless surface electromyography system can be an easy and accurate supplementary for bedside muscular atrophy causes evaluation, holding excellent contact with the body.

A Systematic Review of Wearable Sensors for Monitoring Physical Activity

This article reviews the use of wearable sensors for the monitoring of physical activity (PA) for different purposes, including assessment of gait and balance, prevention and/or detection of falls, recognition of various PAs, conduction and assessment of rehabilitation exercises and monitoring of neurological disease progression. The article provides in-depth information on the retrieved articles and discusses study shortcomings related to demographic factors, i.e., age, gender, healthy participants vs patients, and study conditions. It is well known that motion patterns change with age and the onset of illnesses, and that the risk of falling increases with age. Yet, studies including older persons are rare. Gender distribution was not even provided in several studies, and others included only, or a majority of, men. Another shortcoming is that none of the studies were conducted in real-life conditions. Hence, there is still important work to be done in order to increase the usefulness of wearable sensors in these areas. The article highlights flaws in how studies based on previously collected datasets report on study samples and the data collected, which makes the validity and generalizability of those studies low. Exceptions exist, such as the promising recently reported open dataset FallAllD, wherein a longitudinal study with older adults is ongoing.

Quantifying muscle alterations in a Parkinson's disease animal model using electromyographic biomarkers

Parkinson's disease (PD) is a neurodegenerative disease currently diagnosed based on characteristic motor dysfunctions. The most common Parkinson's disease animal model induces massive nigrostriatal degeneration by intracerebral infusion of 6-hydroxydopamine (6-OHDA). Motor deficits in rat models of Parkinson's disease were previously addressed in other works. However, an accurate quantification of muscle function in freely moving PD-lesioned rats over time has not been described until now. In this work, we address the muscular activity characterization of a 6-OHDA-lesion model of PD along 6 weeks post-lesion based on spectral and morphological analysis of the signals. Using chronic implanted EMG electrodes in a hindlimb muscle of freely moving rats, we have evaluated the effect of the PD neurotoxic model in the muscular activity during locomotion. EMG signals obtained from animals with different time post-injury were analyzed. Power spectral densities were characterized by the mean and median frequency, and the EMG burst stationarity was previously verified for all animals. Our results show that as the time post-lesion increases both frequency parameters decrease. Probability distribution function analysis was also performed. The results suggest that contractile dynamics of the biceps femoris muscle change with time post-lesion. We have also demonstrated here the usefulness of frequency parameters as biomarkers for monitoring the muscular function changes that could be used for early detection of motor dysfunction.

A Statistical Method for Exploratory Data Analysis Based on 2D and 3D Area under Curve Diagrams: Parkinson's Disease Investigation

A statistical method for exploratory data analysis based on 2D and 3D area under curve (AUC) diagrams was developed. The method was designed to analyze electroencephalogram (EEG), electromyogram (EMG), and tremorogram data collected from patients with Parkinson's disease. The idea of the method of wave train electrical activity analysis is that we consider the biomedical signal as a combination of the wave trains. The wave train is the increase in the power spectral density of the signal localized in time, frequency, and space. We detect the wave trains as the local maxima in the wavelet spectrograms. We do not consider wave trains as a special kind of signal. The wave train analysis method is different from standard signal analysis methods such as Fourier analysis and wavelet analysis in the following way. Existing methods for analyzing EEG, EMG, and tremor signals, such as wavelet analysis, focus on local time-frequency changes in the signal and therefore do not reveal the generalized properties of the signal. Other methods such as standard Fourier analysis ignore the local time-frequency changes in the characteristics of the signal and, consequently, lose a large amount of information that existed in the signal. The method of wave train electrical activity analysis resolves the contradiction between these two approaches because it addresses the generalized characteristics of the biomedical signal based on local time-frequency changes in the signal. We investigate the following wave train parameters: wave train central frequency, wave train maximal power spectral density, wave train duration in periods, and wave train bandwidth. We have developed special graphical diagrams, named AUC diagrams, to determine what wave trains are characteristic of neurodegenerative diseases. In this paper, we consider the following types of AUC diagrams: 2D and 3D diagrams. The technique of working with AUC diagrams is illustrated by examples of analysis of EMG in patients with Parkinson's disease and healthy volunteers. It is demonstrated that new regularities useful for the high-accuracy diagnosis of Parkinson's disease can be revealed using the method of analyzing the wave train electrical activity and AUC diagrams.

Adaptive Extreme Edge Computing for Wearable Devices

Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions toward smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g., memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.

A-WEAR Bracelet for Detection of Hand Tremor and Bradykinesia in Parkinson's Patients

Parkinson's disease patients face numerous motor symptoms that eventually make their life different from those of normal healthy controls. Out of these motor symptoms, tremor and bradykinesia, are relatively prevalent in all stages of this disease. The assessment of these symptoms is usually performed by traditional methods where the accuracy of results is still an open question. This research proposed a solution for an objective assessment of tremor and bradykinesia in subjects with PD (10 older adults aged greater than 60 years with tremor and 10 older adults aged greater than 60 years with bradykinesia) and 20 healthy older adults aged greater than 60 years. Physical movements were recorded by means of an AWEAR bracelet developed using inertial sensors, i.e., 3D accelerometer and gyroscope. Participants performed upper extremities motor activities as adopted by neurologists during the clinical assessment based on Unified Parkinson's Disease Rating Scale (UPDRS). For discriminating the patients from healthy controls, temporal and spectral features were extracted, out of which non-linear temporal and spectral features show greater difference. Both supervised and unsupervised machine learning classifiers provide good results. Out of 40 individuals, neural net clustering discriminated 34 individuals in correct classes, while the KNN approach discriminated 91.7% accurately. In a clinical environment, the doctor can use the device to comprehend the tremor and bradykinesia of patients quickly and with higher accuracy.

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.

EMG-Centered Multisensory Based Technologies for Pattern Recognition in Rehabilitation: State of the Art and Challenges

In the field of rehabilitation, the electromyography (EMG) signal plays an important role in interpreting patients' intentions and physical conditions. Nevertheless, utilizing merely the EMG signal suffers from difficulty in recognizing slight body movements, and the detection accuracy is strongly influenced by environmental factors. To address the above issues, multisensory integration-based EMG pattern recognition (PR) techniques have been developed in recent years, and fruitful results have been demonstrated in diverse rehabilitation scenarios, such as achieving high locomotion detection and prosthesis control accuracy. Owing to the importance and rapid development of the EMG centered multisensory fusion technologies in rehabilitation, this paper reviews both theories and applications in this emerging field. The principle of EMG signal generation and the current pattern recognition process are explained in detail, including signal preprocessing, feature extraction, classification algorithms, etc. Mechanisms of collaborations between two important multisensory fusion strategies (kinetic and kinematics) and EMG information are thoroughly explained; corresponding applications are studied, and the pros and cons are discussed. Finally, the main challenges in EMG centered multisensory pattern recognition are discussed, and a future research direction of this area is prospected.

A multiple motion sensors index for motor state quantification in Parkinson's disease

AIM

To construct a Treatment Response Index from Multiple Sensors (TRIMS) for quantification of motor state in patients with Parkinson's disease (PD) during a single levodopa dose. Another aim was to compare TRIMS to sensor indexes derived from individual motor tasks.

METHOD

Nineteen PD patients performed three motor tests including leg agility, pronation-supination movement of hands, and walking in a clinic while wearing inertial measurement unit sensors on their wrists and ankles. They performed the tests repeatedly before and after taking 150% of their individual oral levodopa-carbidopa equivalent morning dose.Three neurologists blinded to treatment status, viewed patients' videos and rated their motor symptoms, dyskinesia, overall motor state based on selected items of Unified PD Rating Scale (UPDRS) part III, Dyskinesia scale, and Treatment Response Scale (TRS). To build TRIMS, out of initially 178 extracted features from upper- and lower-limbs data, 39 features were selected by stepwise regression method and were used as input to support vector machines to be mapped to mean reference TRS scores using 10-fold cross-validation method. Test-retest reliability, responsiveness to medication, and correlation to TRS as well as other UPDRS items were evaluated for TRIMS.

RESULTS

The correlation of TRIMS with TRS was 0.93. TRIMS had good test-retest reliability (ICC = 0.83). Responsiveness of the TRIMS to medication was good compared to TRS indicating its power in capturing the treatment effects. TRIMS was highly correlated to dyskinesia (R = 0.85), bradykinesia (R = 0.84) and gait (R = 0.79) UPDRS items. Correlation of sensor index from the upper-limb to TRS was 0.89.

CONCLUSION

Using the fusion of upper- and lower-limbs sensor data to construct TRIMS provided accurate PD motor states estimation and responsive to treatment. In addition, quantification of upper-limb sensor data during walking test provided strong results.

Parkinson patients without tremor show changed patterns of mechanical muscle oscillations during a specific bilateral motor task compared to controls

The pathophysiology of Parkinson's disease (PD) is still not understood. There are investigations which show a changed oscillatory behaviour of brain circuits or changes in variability of, e.g., gait parameters in PD. The aim of this study was to investigate whether or not the motor output differs between PD patients and healthy controls. Thereby, patients without tremor are investigated in the medication off state performing a special bilateral isometric motor task. The force and accelerations (ACC) were recorded as well as the Mechanomyography (MMG) of the biceps brachii, the brachioradialis and of the pectoralis major muscles using piezoelectric-sensors during the bilateral motor task at 60% of the maximal isometric contraction. The frequency, a specific power ratio, the amplitude variation and the slope of amplitudes were analysed. The results indicate that the oscillatory behaviour of motor output in PD patients without tremor deviates from controls: thereby, the 95%-confidence-intervals of power ratio and of amplitude variation of all signals are disjoint between PD and controls and show significant differences in group comparisons (power ratio: p = 0.000-0.004, r = 0.441-0.579; amplitude variation: p = 0.000-0.001, r = 0.37-0.67). The mean frequency shows a significant difference for ACC (p = 0.009, r = 0.43), but not for MMG. It remains open, whether this muscular output reflects changes of brain circuits and whether the results are reproducible and specific for PD.

Wearable Accelerometer and sEMG-Based Upper Limb BSN for Tele-Rehabilitation

Assessment of human locomotion using wearable sensors is an efficient way of getting useful information about human health status, and determining human locomotion abnormalities. Wearable sensors do not only provide the opportunity to assess the behavior of patients as it happens in their daily life activities, but also provide quantitative, meaningful feedback data of patients to their therapists. This can pinpoint the cause of problems and help in maximizing their recovery rates. The popularity of using wearable sensors has received attention from a number of researchers from both the academic and industrial fields in the past few years. The different types of wearable sensors have given birth to the realization of a standard measurement model that can support different types of applications. Wireless body area networks (WBANs) are starting to replace traditional healthcare systems by enabling long-term monitoring of patients and tele-rehabilitation, especially those who suffer from chronic diseases. This paper investigates using wearable accelerometers and surface electromyography (EMG) in human locomotion monitoring for tele-rehabilitation. It proposes and investigates new positions for the proposed sensors, and compares the measured signals to similar techniques proposed in the literature. Realistic measurements show that the proposed positions of surface EMG sensors (on the forearm muscles) provide more reliable results in the classification of motion abnormality as compared to the sensor positions proposed in the literature (biceps muscles). Seven statistical features were extracted from accelerometer signals, and four time domain (TD) features are extracted from EMG signals. These features are used to construct six machine learning classifiers for automatic classification of Parkinson’s tremor. These models include; decision tree (DT), linear discriminant analysis analysis (LDA), k-nearest-neighbor (kNN), support vector machine (SVM), boosted tree and bagged tree classifiers. The performance of the applied classifiers is analyzed using accuracy, confusion matrix, and area under ROC (AUC) curve. The results are also compared to corresponding findings in the literature. The experimental results show that the highest classification accuracy is achieved when using the proposed measurement set and bagged tree classifier with a value of 99.6%.

Parameters of Surface Electromyogram Suggest That Dry Immersion Relieves Motor Symptoms in Patients With Parkinsonism

Dry immersion (DI) is acknowledged as a reliable space flight analog condition. At DI, subject is immersed in water being wrapped in a waterproof film to imitate microgravity (μG). Microgravity is known to decrease muscle tone due to deprivation of the sensory stimuli that activate the reflexes that keep up the muscle tone. In contrary, parkinsonian patients are characterized by elevated muscle tone, or rigidity, along with rest tremor and akinesia. We hypothesized that DI can diminish the elevated muscle tone and/or the tremor in parkinsonian patients. Fourteen patients with Parkinson's disease (PD, 10 males, 4 females, 47-73 years) and 5 patients with vascular parkinsonism (VP, 1 male, 4 females, 65-72 years) participated in the study. To evaluate the effect of DI on muscles' functioning, we compared parameters of surface electromyogram (sEMG) measured before and after a single 45-min long immersion session. The sEMG recordings were made from the biceps brachii muscle, bilaterally. Each recording was repeated with the following loading conditions: with arms hanging freely down, and with 0, 1, and 2 kg loading on each hand with elbows flexed to 90°. The sEMG parameters comprised of amplitude, median frequency, time of decay of mutual information, sample entropy, correlation dimension, recurrence rate, and determinism of sEMG. These parameters have earlier been proved to be sensitive to PD severity. We used the Wilcoxon test to decide which parameters were statistically significantly different before and after the dry immersion. Accepting the p < 0.05 significance level, amplitude, time of decay of mutual information, recurrence rate, and determinism tended to decrease, while median frequency and sample entropy of sEMG tended to increase after the DI. The most statistically significant change was for the determinism of sEMG from the left biceps with 1 kg loading, which decreased for 84% of the patients. The results suggest that DI can promptly relieve motor symptoms of parkinsonism. We conclude that DI has strong potential as a rehabilitation method for parkinsonian patients.

Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities

Traditional laboratory experiments, rehabilitation clinics, and wearable sensors offer biomechanists a wealth of data on healthy and pathological movement. To harness the power of these data and make research more efficient, modern machine learning techniques are starting to complement traditional statistical tools. This survey summarizes the current usage of machine learning methods in human movement biomechanics and highlights best practices that will enable critical evaluation of the literature. We carried out a PubMed/Medline database search for original research articles that used machine learning to study movement biomechanics in patients with musculoskeletal and neuromuscular diseases. Most studies that met our inclusion criteria focused on classifying pathological movement, predicting risk of developing a disease, estimating the effect of an intervention, or automatically recognizing activities to facilitate out-of-clinic patient monitoring. We found that research studies build and evaluate models inconsistently, which motivated our discussion of best practices. We provide recommendations for training and evaluating machine learning models and discuss the potential of several underutilized approaches, such as deep learning, to generate new knowledge about human movement. We believe that cross-training biomechanists in data science and a cultural shift toward sharing of data and tools are essential to maximize the impact of biomechanics research.

Levodopa-Induced Changes in Electromyographic Patterns in Patients with Advanced Parkinson's Disease

Levodopa medication is the most efficient treatment for motor symptoms of Parkinson's disease (PD). Levodopa significantly alleviates rigidity, rest tremor, and bradykinesia in PD. The severity of motor symptoms can be graded with UPDRS-III scale. Levodopa challenge test is routinely used to assess patients' eligibility to deep-brain stimulation (DBS) in PD. Feasible and objective measurements to assess motor symptoms of PD during levodopa challenge test would be helpful in unifying the treatment. Twelve patients with advanced PD who were candidates for DBS treatment were recruited to the study. Measurements were done in four phases before and after levodopa challenge test. Rest tremor and rigidity were evaluated using UPDRS-III score. Electromyographic (EMG) signals from biceps brachii and kinematic signals from forearm were recorded with wireless measurement setup. The patients performed two different tasks: arm isometric tension and arm passive flexion-extension. The electromyographic and the kinematic signals were analyzed with parametric, principal component, and spectrum-based approaches. The principal component approach for isometric tension EMG signals showed significant decline in characteristics related to PD during levodopa challenge test. The spectral approach on passive flexion-extension EMG signals showed a significant decrease on involuntary muscle activity during the levodopa challenge test. Both effects were stronger during the levodopa challenge test compared to that of patients' personal medication. There were no significant changes in the parametric approach for EMG and kinematic signals during the measurement. The results show that a wireless and wearable measurement and analysis can be used to study the effect of levodopa medication in advanced Parkinson's disease.

Design of a noninvasive and smart hand tremor attenuation system with active control: a simulation study

This paper presents the design and simulation of a handheld device for people with hand tremor, such as Parkinson's and essential tremor patients. This device can be used as a pen for smartphones or as a spoon. The designed system includes two links, which are connected to two servomotors, which are mounted in orthogonal directions. To attenuate the effect of hand tremor on the tip of device, PID and computed torque methods are used to actively control the system. These controllers are used to control the rotation of the motors for moving the links in opposite directions of the hand tremor. Performance of the device with mentioned controllers is studied for different applications and finally, the results of both controllers are discussed and compared. Based on the presented results in this study, the designed device is able to suppress the hand tremor up to 75% during eating and 65% during following a spiral pattern. Graphical abstract Design of a noninvasive and smart hand tremor attenuation system: a simulation study.

Automatic Classification of Tremor Severity in Parkinson's Disease Using a Wearable Device

Although there is clinical demand for new technology that can accurately measure Parkinsonian tremors, automatic scoring of Parkinsonian tremors using machine-learning approaches has not yet been employed. This study aims to fill this gap by proposing machine-learning algorithms as a way to predict the Unified Parkinson’s Disease Rating Scale (UPDRS), which are similar to how neurologists rate scores in actual clinical practice. In this study, the tremor signals of 85 patients with Parkinson’s disease (PD) were measured using a wrist-watch-type wearable device consisting of an accelerometer and a gyroscope. The displacement and angle signals were calculated from the measured acceleration and angular velocity, and the acceleration, angular velocity, displacement, and angle signals were used for analysis. Nineteen features were extracted from each signal, and the pairwise correlation strategy was used to reduce the number of feature dimensions. With the selected features, a decision tree (DT), support vector machine (SVM), discriminant analysis (DA), random forest (RF), and k-nearest-neighbor (kNN) algorithm were explored for automatic scoring of the Parkinsonian tremor severity. The performance of the employed classifiers was analyzed using accuracy, recall, and precision, and compared to other findings in similar studies. Finally, the limitations and plans for further study are discussed.

Support vector machine classification of Parkinson's disease and essential tremor subjects based on temporal fluctuation

Tremor is a common symptom shared in both Parkinson's disease (PD) and Essential tremor (ET) subjects. The differential diagnosis of PD and ET tremor is important since the realization of treatment depends on specific medication. A novel feature is developed based on a hypothesis that tremor of PD subject has a larger fluctuation during resting than action task. Tremor signal is collected using a triaxial gyroscope sensor attached to subject's finger during kinetic and resting task. The angular velocity signal is analyzed by transforming a one-dimensional to two-dimensional signal using a relation of signal and its delay versions. Tremor fluctuation is defined as the area of 95% confidence ellipse covering the two-dimensional signal. The tremor fluctuation during kinetic and resting task is used as classification features. The support vector machine is used as a classifier and tested with 10-fold cross-validation. This novel feature provides a perfect PD/ET classification with 100% accuracy, sensitivity and specificity.

Intraoperative acceleration measurements to quantify improvement in tremor during deep brain stimulation surgery

Deep brain stimulation (DBS) surgery is extensively used in the treatment of movement disorders. Nevertheless, methods to evaluate the clinical response during intraoperative stimulation tests to identify the optimal position for the implantation of the chronic DBS lead remain subjective. In this paper, we describe a new, versatile method for quantitative intraoperative evaluation of improvement in tremor with an acceleration sensor that is mounted on the patient's wrist during surgery. At each anatomical test position, the improvement in tremor compared to the initial tremor is estimated on the basis of extracted outcome measures. This method was tested on 15 tremor patients undergoing DBS surgery in two centers. Data from 359 stimulation tests were acquired. Our results suggest that accelerometric evaluation detects tremor changes more sensitively than subjective visual ratings. The effective stimulation current amplitudes identified from the quantitative data (1.1 ± 0.8 mA) are lower than those identified by visual evaluation (1.7 ± 0.8 mA) for similar improvement in tremor. Additionally, if these data had been used to choose the chronic implant position of the DBS lead, 15 of the 26 choices would have been different. These results show that our method of accelerometric evaluation can potentially improve DBS targeting.

Age-related motor unit remodeling in the Tibialis Anterior

Limited studies exist on the use of surface electromyogram (EMG) signal features to detect age-related motor unit remodeling in the Tibialis Anterior. Motor unit remodeling leads to declined muscle strength and force steadiness during submaximal contractions which are factors for risk of falls in the elderly. This study investigated the remodeling phenomena in the Tibialis Anterior using sample entropy and higher order statistics. Eighteen young (26.1 ± 2.9 years) and twelve elderly (68.7 ± 9.0 years) participants performed isometric dorsiflexion of the ankle at 20% maximal voluntary contraction (MVC) and their Tibialis Anterior (TA) EMG was recorded. Sample entropy, Gaussianity and Linearity Test statistics were calculated from the recorded EMG for each MVC. Shapiro-Wilk test was used to determine normality, and either a two-tail student t-test or Wilcoxon rank sum test was performed to determine significant difference in the EMG features between the young and old cohorts. Results show age-related motor unit remodeling to be depicted by decreased sample entropy (p <; 0.1), increased non-Gaussianity (p <; 0.05) and lesser degree of linearity in the elderly. This is due to the increased sparsity of the MUAPs as a result of the denervation-reinnervation process, and the decrease in total number of motor units.

A novel approach for SEMG signal classification with adaptive local binary patterns

Feature extraction plays a major role in the pattern recognition process, and this paper presents a novel feature extraction approach, adaptive local binary pattern (aLBP). aLBP is built on the local binary pattern (LBP), which is an image processing method, and one-dimensional local binary pattern (1D-LBP). In LBP, each pixel is compared with its neighbors. Similarly, in 1D-LBP, each data in the raw is judged against its neighbors. 1D-LBP extracts feature based on local changes in the signal. Therefore, it has high a potential to be employed in medical purposes. Since, each action or abnormality, which is recorded in SEMG signals, has its own pattern, and via the 1D-LBP these (hidden) patterns may be detected. But, the positions of the neighbors in 1D-LBP are constant depending on the position of the data in the raw. Also, both LBP and 1D-LBP are very sensitive to noise. Therefore, its capacity in detecting hidden patterns is limited. To overcome these drawbacks, aLBP was proposed. In aLBP, the positions of the neighbors and their values can be assigned adaptively via the down-sampling and the smoothing coefficients. Therefore, the potential to detect (hidden) patterns, which may express an illness or an action, is really increased. To validate the proposed feature extraction approach, two different datasets were employed. Achieved accuracies by the proposed approach were higher than obtained results by employed popular feature extraction approaches and the reported results in the literature. Obtained accuracy results were brought out that the proposed method can be employed to investigate SEMG signals. In summary, this work attempts to develop an adaptive feature extraction scheme that can be utilized for extracting features from local changes in different categories of time-varying signals.

Temporal fluctuations of tremor signals from inertial sensor: a preliminary study in differentiating Parkinson's disease from essential tremor

BACKGROUND

Parkinson's disease (PD) and essential tremor (ET) are the two most common movement disorders but the rate of misdiagnosis rate in these disorders is high due to similar characteristics of tremor. The purpose of the study is to present: (a) a solution to identify PD and ET patients by using the novel measurement of tremor signal variations while performing the resting task, (b) the improvement of the differentiation of PD from ET patients can be obtained by using the ratio of the novel measurement while performing two specific tasks.

METHODS

35 PD and 22 ET patients were asked to participate in the study. They were asked to wear a 6-axis inertial sensor on his/her index finger of the tremor dominant hand and perform three tasks including kinetic, postural and resting tasks. Each task required 10 s to complete. The angular rate signal measured during the performance of these tasks was band-pass filtered and transformed into a two-dimensional representation. The ratio of the ellipse area covering 95 % of this two-dimensional representation of different tasks was investigated and the two best tasks were selected for the purpose of differentiation.

RESULTS

The ellipse area of two-dimensional representation of the resting task of PD and ET subjects are statistically significantly different (p < 0.05). Furthermore, the fluctuation ratio, defined as a ratio of the ellipse area of two-dimensional representation of resting to kinetic tremor, of PD subjects were statistically significantly higher than ET subjects in all axes (p = 0.0014, 0.0011 and 0.0001 for x, y and z-axis, respectively). The validation shows that the proposed method provides 100 % sensitivity, specificity and accuracy of the discrimination in the 5 subjects in the validation group. While the method would have to be validated with a larger number of subjects, these preliminary results show the feasibility of the approach.

CONCLUSIONS

This study provides the novel measurement of tremor variation in time domain termed 'temporal fluctuation'. The temporal fluctuation of the resting task can be used to discriminate PD from ET subjects. The ratio of the temporal fluctuation of the resting task to the kinetic task improves the reliability of the discrimination. While the method is powerful, it is also simple so it could be applied on low resource platforms such as smart phones and watches which are commonly equipped with inertial sensors.

Technologies for Assessment of Motor Disorders in Parkinson's Disease: A Review

Parkinson’s Disease (PD) is characterized as the commonest neurodegenerative illness that gradually degenerates the central nervous system. The goal of this review is to come out with a summary of the recent progress of numerous forms of sensors and systems that are related to diagnosis of PD in the past decades. The paper reviews the substantial researches on the application of technological tools (objective techniques) in the PD field applying different types of sensors proposed by previous researchers. In addition, this also includes the use of clinical tools (subjective techniques) for PD assessments, for instance, patient self-reports, patient diaries and the international gold standard reference scale, Unified Parkinson Disease Rating Scale (UPDRS). Comparative studies and critical descriptions of these approaches have been highlighted in this paper, giving an insight on the current state of the art. It is followed by explaining the merits of the multiple sensor fusion platform compared to single sensor platform for better monitoring progression of PD, and ends with thoughts about the future direction towards the need of multimodal sensor integration platform for the assessment of PD.

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

INTRODUCTION

Assessment of movement dysfunctions commonly comprises trunk range of motion (ROM), movement or control impairment (MCI), repetitive movements (RM), and reposition error (RE). Inertial measurement unit (IMU)-systems could be used to quantify these movement dysfunctions in clinical settings. The aim of this study was to evaluate a novel IMU-system when assessing movement dysfunctions in terms of concurrent validity and reliability.

METHODS

The concurrent validity of the IMU-system was tested against an optoelectronic system with 22 participants. The reliability of 14 movement dysfunction tests were analysed using generalizability theory and coefficient of variation, measuring 24 participants in seven trials on two days.

RESULTS

The IMU-system provided valid estimates of trunk movement in the primary movement direction when compared to the optoelectronic system. Reliability varied across tests and variables. On average, ROM and RM were more reliable, compared to MCI and RE tests.

DISCUSSION

When compared to the optoelectronic system, the IMU-system is valid for estimates of trunk movement in the primary movement direction. Four ROM, two MCI, one RM, and one RE test were identified as reliable and should be studied further for inter-subject comparisons and monitoring changes after an intervention.

Dynamic tension EMG to characterize the effects of DBS treatment of advanced Parkinson's disease

Deep brain stimulation (DBS) is an effective treatment method for motor symptoms of advanced Parkinson's disease. DBS-electrode is implanted to subthalamic nucleus to give precisely allocated electrical stimuli to brain. The optimal stimulus type has to be adjusted individually. Disease severity, main symptoms and biological factors play a role in correctly setting up the device. Currently there are no objective methods to assess the efficacy of DBS, hence the adjustment is based solely on clinical assessment. In optimal case an objectively measurable feature would point the right settings of DBS. Surface electromyographic and kinematic measurements have been used in Parkinson's disease research. As Parkinson's disease symptoms are known to change the EMG signal properties, these methods could be helpful aid in the clinical adjustment of DBS. In this study, 13 patients with advanced Parkinson's disease who received DBS treatment were measured. The patients were measured with seven different settings of the DBS in clinical range including changes in stimulation amplitude, frequency and pulse width. The EMG analysis was based on parameters that characterize EMG signal morphology. Correlation dimension and recurrence rate made the most significant difference in relation to optimal settings. In conclusion, EMG analysis is able to detect differences between the DBS setups, and can help in finding the correct parameters.

Temporal fluctuation analysis of tremor signal in Parkinson's disease and Essential tremor subjects

Tremor is a common symptom shared in both Parkinson's disease (PD) and Essential tremor (ET) subjects. The differential diagnosis of PD and ET tremor is important since the treatment depends on specific medication. A novel feature was developed based on a hypothesis stating that the tremor of PD subject has a larger fluctuation while performing resting task than action task. Tremor signal was collected using a gyroscope sensor attached to subject's finger. The angular velocity signal was analyzed by transforming a one-dimensional to two-dimensional signal based on relation of different units of time-delay. The tremor fluctuation was defined as the area of 95% confidence ellipse covering the two-dimensional signal. Experimenting with 32 PD and 20 ET subjects, a ratio of fluctuation of resting to kinetic task can be a sensitive feature to discriminate PD from ET with 100% accuracy.

Subspace based adaptive denoising of surface EMG from neurological injury patients

OBJECTIVE

After neurological injuries such as spinal cord injury, voluntary surface electromyogram (EMG) signals recorded from affected muscles are often corrupted by interferences, such as spurious involuntary spikes and background noises produced by physiological and extrinsic/accidental origins, imposing difficulties for signal processing. Conventional methods did not well address the problem caused by interferences. It is difficult to mitigate such interferences using conventional methods. The aim of this study was to develop a subspace-based denoising method to suppress involuntary background spikes contaminating voluntary surface EMG recordings.

APPROACH

The Karhunen-Loeve transform was utilized to decompose a noisy signal into a signal subspace and a noise subspace. An optimal estimate of EMG signal is derived from the signal subspace and the noise power. Specifically, this estimator is capable of making a tradeoff between interference reduction and signal distortion. Since the estimator partially relies on the estimate of noise power, an adaptive method was presented to sequentially track the variation of interference power. The proposed method was evaluated using both semi-synthetic and real surface EMG signals.

MAIN RESULTS

The experiments confirmed that the proposed method can effectively suppress interferences while keep the distortion of voluntary EMG signal in a low level. The proposed method can greatly facilitate further signal processing, such as onset detection of voluntary muscle activity.

SIGNIFICANCE

The proposed method can provide a powerful tool for suppressing background spikes and noise contaminating voluntary surface EMG signals of paretic muscles after neurological injuries, which is of great importance for their multi-purpose applications.

Effect of glenohumeral forward flexion on upper limb myoelectric activity during simulated mills manipulation; relations to peripheral nerve biomechanics

BACKGROUND

It is generally accepted that muscles may activate via the common nociceptive flexion reflex (NFR) in response to painful stimuli associated with tensile or compressive forces on peripheral nerves. Following the basic assumption that the radial nerve may be stressed around the elbow during the execution of the Mills manipulation, two positions considered to have different mechanical effects on the radial nerve and the brachial plexus were tested in order to i) explore whether muscles are activated in certain patterns with concomitant changes in nerve tension, ii) establish whether muscle responses can be modified with mechanical unloading of the brachial plexus.

METHODS

Muscle responses were quantified bilaterally in eight subjects (N = 16) during Mills Manipulation (MM) pre-manipulative positioning and a Varied position that putatively produces less mechanical tension in the brachial plexus. End range pre-manipulative stretch was used in order to simulate the effects of Mills manipulation. Electromyographic signals were recorded with a 16 channel portable EMG unit and correlated with kinematic data from three charge-coupled device adjustable cameras which allowed for precise movement tracking.

RESULTS

Compared with the Standard Mills manipulation position, the Varied position produced significantly reduced myoelectric activity (P ≤ .001) in all test muscles. Additional subjective data support the notion that certain muscle activity patterns were protective.

CONCLUSION

It seems that protective muscles are selectively activated in a specific pattern in order to protect the radial nerve from mechanical tension by shortening its pathway, suggesting integration of muscle and neural mechanisms. Furthermore, the significantly decreased myoelectric activity with reduced mechanical tension in the brachial plexus may help controlling collateral effects of the Mills manipulation itself, making it potentially safer and more specific.

Unbiased and mobile gait analysis detects motor impairment in Parkinson's disease

Motor impairments are the prerequisite for the diagnosis in Parkinson's disease (PD). The cardinal symptoms (bradykinesia, rigor, tremor, and postural instability) are used for disease staging and assessment of progression. They serve as primary outcome measures for clinical studies aiming at symptomatic and disease modifying interventions. One major caveat of clinical scores such as the Unified Parkinson Disease Rating Scale (UPDRS) or Hoehn&Yahr (H&Y) staging is its rater and time-of-assessment dependency. Thus, we aimed to objectively and automatically classify specific stages and motor signs in PD using a mobile, biosensor based Embedded Gait Analysis using Intelligent Technology (eGaIT). eGaIT consist of accelerometers and gyroscopes attached to shoes that record motion signals during standardized gait and leg function. From sensor signals 694 features were calculated and pattern recognition algorithms were applied to classify PD, H&Y stages, and motor signs correlating to the UPDRS-III motor score in a training cohort of 50 PD patients and 42 age matched controls. Classification results were confirmed in a second independent validation cohort (42 patients, 39 controls). eGaIT was able to successfully distinguish PD patients from controls with an overall classification rate of 81%. Classification accuracy increased with higher levels of motor impairment (91% for more severely affected patients) or more advanced stages of PD (91% for H&Y III patients compared to controls), supporting the PD-specific type of analysis by eGaIT. In addition, eGaIT was able to classify different H&Y stages, or different levels of motor impairment (UPDRS-III). In conclusion, eGaIT as an unbiased, mobile, and automated assessment tool is able to identify PD patients and characterize their motor impairment. It may serve as a complementary mean for the daily clinical workup and support therapeutic decisions throughout the course of the disease.

Biometric and mobile gait analysis for early diagnosis and therapy monitoring in Parkinson's disease

Parkinson's disease (PD) is the most frequent neurodegenerative movement disorder. Early diagnosis and effective therapy monitoring is an important prerequisite to treat patients and reduce health care costs. Objective and non-invasive assessment strategies are an urgent need in order to achieve this goal. In this study we apply a mobile, lightweight and easy applicable sensor based gait analysis system to measure gait patterns in PD and to distinguish mild and severe impairment of gait. Examinations of 16 healthy controls, 14 PD patients in an early stage, and 13 PD patients in an intermediate stage were included. Subjects performed standardized gait tests while wearing sport shoes equipped with inertial sensors (gyroscopes and accelerometers). Signals were recorded wirelessly, features were extracted, and distinct subpopulations classified using different classification algorithms. The presented system is able to classify patients and controls (for early diagnosis) with a sensitivity of 88% and a specificity of 86%. In addition it is possible to distinguish mild from severe gait impairment (for therapy monitoring) with 100% sensitivity and 100% specificity. This system may be able to objectively classify PD gait patterns providing important and complementary information for patients, caregivers and therapists.

Diagnosis of Parkinson's disease using electrovestibulography

In this paper, a new method for diagnosis of Parkinson's disease (PD) based on the analysis of electrovestibulography (EVestG) signals is introduced. EVestG signals are in fact the vestibular response modulated by more cortical brain signals; they are recorded from the ear canal. EVestG data of 20 individuals with PD and 28 healthy controls were adopted from a previous study. The field potentials and their firing pattern in response to whole body tilt stimuli from both left and right ears were extracted. We investigated several statistical and fractal features of the field potentials and also their firing interval histograms followed by one-way analysis of variance to select pairs of features showing the most significant differences between individuals with Parkinson disease and the age-matched controls. Linear discriminant analysis classification was applied to every selected feature using a leave-one-out routine. The result of each feature's classifier was used in a heuristic average voting system to diagnose PD patients. The results show more than 95% accuracy for PD diagnosis. Given that the patients were at different stage of disease, the high accuracy of the results is encouraging for continuing exploration of the EVestG application to PD diagnosis as it may provide a quick and non-invasive screening tool.

Effect of cervical spine position on upper limb myoelectric activity during pre-manipulative stretch for Mills manipulation: a new model, relations to peripheral nerve biomechanics and specificity of Mills manipulation

OBJECTIVES

(A) Describe a new method of investigation of the possible muscular effects of the commonly practiced Mills manipulation for lateral elbow pain (epicondylalgia), (B) ascertain if myoelectric activity is influenced during the pre-manipulative stretch for Mills manipulation, (C) establish whether muscle responses are influenced by ipsilateral lateral flexion of the cervical spine which reduces mechanical tension in the peripheral nerves of the upper limb.

SAMPLE

Eight asymptomatic subjects were tested bilaterally (N=16).

METHODS

Myoelectric measurements - EMG signals were recorded with a 16 channel pocket EMG patient unit and processed off-line. Measurement of joint positions-three CCD adjustable cameras sensitive to 10mm reflective passive markers applied at specific locations on the subjects' bodies were used to reconstruct and verify accuracy of body movements and were correlated with EMG recordings.

RESULTS

Compared with the standard (anatomical) position of the cervical spine in which Mills manipulation is typically performed, cervical spine ipsilateral lateral flexion produced significantly reduced activity in muscles that restrain the manipulation movement (elbow extension), namely biceps brachii (P=0.018) and brachioradialis (P=0.000). The affected muscles may therefore produce protective effects during the manipulation.

CONCLUSIONS

Changes in myoelectric activity were in a pattern that suggests that muscle and neural mechanisms may be an integral part of the Mills manipulation. Cervical spine ipsilateral lateral flexion may be used to reduce mechanical stresses in the peripheral nerves and extraneous muscle activity, making Mills' manipulation potentially safer and more specific.

Biomechanical analysis of the muscular power of martial arts athletes

This study analyzes the performance of knee extension and flexion of Taekwondo and Kickboxing athletes. The power values were extracted through electromyography obtained by an isokinetic dynamometer at 60 degrees per second. These values are resulted from the square of the electromyography signal. The analysis of kick power was made using a modified wavelet algorithm considering values with 95% significance. Both groups presented equivalent power and torque capacity with different training times and experience, on the other hand, the wavelet analysis showed better results in muscular recruitment performance in athletes with more experience, in other words, power is not only performance but also power plus recruitment produces better results. This study uniquely showed that muscular enhancement capacity is not only related to the power capacity of contraction but also to motor coordination.

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.

EMG and acceleration signal analysis for quantifying the effects of medication in Parkinson's disease

Parkinson's disease (PD) is characterized by motor disabilities that can be alleviated reasonably with appropriate medication. However, there is a lack of objective methods for quantifying the efficacy of treatment in PD. We applied here an objective method for quantifying the effects of medication in PD using EMG and acceleration measurements and analysis. In the method, four signal features were calculated from the EMG and acceleration recordings of both sides of the body: the kurtosis and recurrence rate of EMG, and the amplitude and sample entropy of acceleration. Principal component approach was used for reducing the number of variables. EMG and acceleration data measured from nine PD patients were used for analysis. The patients were measured in four different medication conditions: with medication off, and two and three and four hours after taking the medication. The results showed that in eight patients the EMG recordings changed into less spiky and the acceleration recordings into more complex after taking the medication. A reverse phenomenon in the signal characteristics was observed in seven patients 3-4 hours after taking the medication. The results indicate that the presented method is potentially useful for quantifying objectively the effects of medication on the neuromuscular function in PD.

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

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

Cardiogoniometric parameters for detection of coronary artery disease at rest as a function of stenosis localization and distribution

Cardiogoniometry (CGM), a spatiotemporal electrocardiologic 5-lead method with automated analysis, may be useful in primary healthcare for detecting coronary artery disease (CAD) at rest. Our aim was to systematically develop a stenosis-specific parameter set for global CAD detection. In 793 consecutively admitted patients with presumed non-acute CAD, CGM data were collected prior to elective coronary angiography and analyzed retrospectively. 658 patients fulfilled the inclusion criteria, 405 had CAD verified by coronary angiography; the 253 patients with normal coronary angiograms served as the non-CAD controls. Study patients--matched for age, BMI, and gender--were angiographically assigned to 8 stenosis-specific CAD categories or to the controls. One CGM parameter possessing significance (P < .05) and the best diagnostic accuracy was matched to one CAD category. The area under the ROC curve was .80 (global CAD versus controls). A set containing 8 stenosis-specific CGM parameters described variability of R vectors and R-T angles, spatial position and potential distribution of R/T vectors, and ST/T segment alterations. Our parameter set systematically combines CAD categories into an algorithm that detects CAD globally. Prospective validation in clinical studies is ongoing.