Near Real-Time Data Labeling Using a Depth Sensor for EMG Based Prosthetic Arms

Recognizing sEMG (Surface Electromyography) signals belonging to a particular action (e.g., lateral arm raise) automatically is a challenging task as EMG signals themselves have a lot of variation even for the same action due to several factors. To overcome this issue, there should be a proper separation which indicates similar patterns repetitively for a particular action in raw signals. A repetitive pattern is not always matched because the same action can be carried out with different time duration. Thus, a depth sensor (Kinect) was used for pattern identification where three joint angles were recording continuously which is clearly separable for a particular action while recording sEMG signals. To Segment out a repetitive pattern in angle data, MDTW (Moving Dynamic Time Warping) approach is introduced. This technique is allowed to retrieve suspected motion of interest from raw signals. MDTW based on DTW algorithm, but it will be moving through the whole dataset in a pre-defined manner which is capable of picking up almost all the suspected segments inside a given dataset an optimal way. Elevated bicep curl and lateral arm raise movements are taken as motions of interest to show how the proposed technique can be employed to achieve auto identification and labelling. The full implementation is available at https://github.com/GPrathap/OpenBCIPytho

Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms

We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals. Briefly, the raw data is passed through 50 convolutional layers before subsequent classification into one of five sleep stages. Three model configurations were trained on 1850 polysomnogram recordings and subsequently tested on 230 independent recordings. Our best performing model yielded an accuracy of 84.1% and a Cohen's kappa of 0.746, improving on previous reported results by other groups also using only raw polysomnogram data. Most errors were made on non-REM stage 1 and 3 decisions, errors likely resulting from the definition of these stages. Further testing on independent cohorts is needed to verify performance for clinical use

Automated design of robust discriminant analysis classifier for foot pressure lesions using kinematic data

In the recent years, the use of motion tracking systems for acquisition of functional biomechanical gait data, has received increasing interest due to the richness and accuracy of the measured kinematic information. However, costs frequently restrict the number of subjects employed, and this makes the dimensionality of the collected data far higher than the available samples. This paper applies discriminant analysis algorithms to the classification of patients with different types of foot lesions, in order to establish an association between foot motion and lesion formation. With primary attention to small sample size situations, we compare different types of Bayesian classifiers and evaluate their performance with various dimensionality reduction techniques for feature extraction, as well as search methods for selection of raw kinematic variables. Finally, we propose a novel integrated method which fine-tunes the classifier parameters and selects the most relevant kinematic variables simultaneously. Performance comparisons are using robust resampling techniques such as Bootstrap$632+$and k-fold cross-validation. Results from experimentations with lesion subjects suffering from pathological plantar hyperkeratosis, show that the proposed method can lead to$sim 96$correct classification rates with less than 10% of the original features

Actions speak louder than words: comparing automatic imitation and verbal command

Automatic imitation – copying observed actions without intention – is known to occur, not only in neurological patients and those with developmental disorders, but also in healthy, typically-developing adults and children. Previous research has shown that a variety of actions are automatically imitated, and that automatic imitation promotes social affiliation and rapport. We assessed the power of automatic imitation by comparing it with the strength of the tendency to obey verbal commands. In a Stroop interference paradigm, the stimuli were compatible, incompatible and neutral compounds of hand postures and verbal commands. When imitative responses were required, the impact of irrelevant action images on responding to words was greater than the effect of irrelevant words on responding to actions. Control group performance showed that this asymmetry was not due to modality effects or differential salience of action and word stimuli. These results indicate that automatic imitation was more powerful than verbal command

EMG signal statistical features extraction combination performance benchmark using unsupervised neural network for arm rehab device

The paper presents important research to select the best features extraction for designing Arm Rehabilitation Device (ARD) for patient who had failure of their limb that highly beneficial towards rehab program. The device used to facilitate the tasks of the program is proved to improve the electrical activity in the motor unit and minimize the mental effort of the user. Electromyography (EMG) is the techniques to analyze the presence of electrical activity in musculoskeletal systems related to muscle movement. To prevent from the muscles paralyzed, it is becoming spasticity that the force of movements should minimize the mental efforts needed. To achieve this, the rehab device should analyze the surface EMG signal of normal people to be implemented to the rehab device. The EMG signal collected using non-invasive method is implemented to set the movements’ pattern of the arm rehab device. The signal are filtered and extracted for three time domain features of Standard Deviation (STD), Mean Absolute Value (MAV) and Root Mean Square (RMS). The features combinations are important to produce best classification result with reduced error. The best combination features for any movements, several trials of movements are used by determining the right combination using Self-Organizing Maps (SOM) for the classification process and this paper proved a proper combination will help to determine the best features combination in designing the best ARD

A Review of EMG Techniques for Detection of Gait Disorders

Electromyography (EMG) is a commonly used technique to record myoelectric signals, i.e., motor neuron signals that originate from the central nervous system (CNS) and synergistically activate groups of muscles resulting in movement. EMG patterns underlying movement, recorded using surface or needle electrodes, can be used to detect movement and gait abnormalities. In this review article, we examine EMG signal processing techniques that have been applied for diagnosing gait disorders. These techniques span from traditional statistical tests to complex machine learning algorithms. We particularly emphasize those techniques are promising for clinical applications. This study is pertinent to both medical and engineering research communities and is potentially helpful in advancing diagnostics and designing rehabilitation devices

Multisystem proteinopathy due to a homozygous p.Arg159His VCP mutation : a tale of the unexpected

ObjectiveTo assess the clinical, radiologic, myopathologic, and proteomic findings in a patient manifesting a multisystem proteinopathy due to a homozygous valosin-containing protein gene (VCP) mutation previously reported to be pathogenic in the heterozygous state.MethodsWe studied a 36-year-old male index patient and his father, both presenting with progressive limb-girdle weakness. Muscle involvement was assessed by MRI and muscle biopsies. We performed whole-exome sequencing and Sanger sequencing for segregation analysis of the identified p.Arg159His VCP mutation. To dissect biological disease signatures, we applied state-of-the-art quantitative proteomics on muscle tissue of the index case, his father, 3 additional patients with VCP-related myopathy, and 3 control individuals.ResultsThe index patient, homozygous for the known p.Arg159His mutation in VCP, manifested a typical VCP-related myopathy phenotype, although with a markedly high creatine kinase value and a relatively early disease onset, and Paget disease of bone. The father exhibited a myopathy phenotype and discrete parkinsonism, and multiple deceased family members on the maternal side of the pedigree displayed a dementia, parkinsonism, or myopathy phenotype. Bioinformatic analysis of quantitative proteomic data revealed the degenerative nature of the disease, with evidence suggesting selective failure of muscle regeneration and stress granule dyshomeostasis.ConclusionWe report a patient showing a multisystem proteinopathy due to a homozygous VCP mutation. The patient manifests a severe phenotype, yet fundamental disease characteristics are preserved. Proteomic findings provide further insights into VCP-related pathomechanisms

Classification of EMG Signal Based on Human Percentile using SOM

Electromyography (EMG) is a bio signal that is formed by physiological variations in the state of muscle fibre membranes. Pattern recognition is one of the fields in the bio-signal processing which classified the signal into certain desired categories with subject to their area of application. This study described the classification of the EMG signal based on human body percentile using Self Organizing Mapping (SOM) technique. Different human percentile definitively varies the arm circumference size. Variation of arm circumference is due to fatty tissue that lay between active muscle and skin. Generally the fatty tissue would decrease the overall amplitude of the EMG signal. Data collection is conducted randomly with fifteen subjects that have numerous percentiles using non-invasive technique at Biceps Brachii muscle. The signals are then going through filtering process to prepare them for the next stage. Then, five well known time domain feature extraction methods are applied to the signal before the classification process. Self Organizing Map (SOM) technique is used as a classifier to discriminate between the human percentiles. Result shows that SOM is capable in clustering the EMG signal to the desired human percentile categories by optimizing the neurons of the technique

Introduction to this Special Issue: Intelligent Data Analysis on Electromyography and Electroneurography

Computer-aided electromyography (EMG) and elec- troneurography (ENG) have become indispensable tools in the daily activities of neurophysiology laboratories in facilitating quantitative analysis and decision making in clinical neurophysiology, rehabilitation, sports medicine, and studies of human physiology. These tools form the basis of a new era in the practice of neurophysiology facilitating the: (i) Standardization . Diagnoses obtained with similar criteria in different laboratories can be veri- fied. (ii) Sensitivity . Neurophysiological findings in a particular subject under investigation may be compared with a database of normal values to determine whether abnormality exists or not. (iii) Specificity . Findings may be compared with databases derived from patients with known diseases, to evaluate whether they fit a specific diagnosis. (iv) Equivalence . Results from serial examin- ations on the same patient may be compared to decide whether there is evidence of disease progression or of response to treatment. Also, findings obtained from dif- ferent quantitative methods may be contrasted to deter- mine which are most sensitive and specific. Different methodologies have been developed in com- puter-aided EMG and ENG analysis ranging from simple quantitative measures of the recorded potentials, to more complex knowledge-based and neural network systems that enable the automated assessment of neuromuscular disorders. However, the need still exists for the further advancement and standardization of these method- ologies, especially nowadays with the emerging health telematics technologies which will enable their wider application in the neurophysiological laboratory. The main objective of this Special Issue of Medical Engin- eering & Physics is to provide a snapshot of current activities and methodologies in intelligent data analysis in peripheral neurophysiology. A total of 12 papers are published in this Special Issue under the following topics: Motor Unit Action Potential (MUAP) Analysis, Surface EMG (SEMG) Analysis, Electroneurography, and Decision Systems. In this intro- duction, the papers are briefly introduced, following a brief review of the major achievements in quantitative electromyography and electroneuropathy