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1.
Analysis of neuromuscular disorders using statistical and entropy metrics on surface EMG
Rok Istenič, Prodromos A. Kaplanis, Constantinos S. Pattichis, Damjan Zazula, 2008, izvirni znanstveni članek

Opis: This paper introduces the surface electromyogram (EMG) classification system based on statistical and entropy metrics. The system is intended for diagnostic use and enables classification of examined subject as normal, myopathic or neuropathic, regarding to the acquired EMG signals. 39 subjects in total participated in the experiment, 19 normal, 11 myopathic and 9 neuropathic. Surface EMG was recorded using 4-channel surface electrodes on the biceps brachii muscle at isometric voluntary contractions. The recording time was only 5 seconds long to avoid muscle fatigue, and contractions at fiveforce levels were performed, i.e. 10, 30, 50, 70 and 100 % of maximal voluntary contraction. The feature extraction routine deployed the wavelet transform and calculation of the Shannon entropy across all the scales in order to obtain a feature set for each subject. Subjects were classified regarding the extracted features using three machine learning techniques, i.e. decision trees, support vector machines and ensembles of support vector machines. Four 2-class classifications and a 3-class classification were performed. The scored classification rates were the following: 64+-11% for normal/abnormal, 74+-7% for normal/myopathic, 79+-8% for normal/neuropathic, 49+-20% for myopathic/neuropathic, and 63+-8% for normal/myopathic/neuropathic.
Ključne besede: surface electromyography, neuromuscular disorders, neuropathy, myopathy, EMG signals, signal processing, wavelet transform, metrics
Objavljeno: 31.05.2012; Ogledov: 877; Prenosov: 7
URL Celotno besedilo (0,00 KB)

2.
Adjustments differ among low-threshold motor units during intermittent, isometric contractions
Dario Farina, Aleš Holobar, Marco Gazzoni, Damjan Zazula, Roberto Merletti, Roger M. Enoka, 2009, izvirni znanstveni članek

Opis: We investigated the changes in muscle fiber conduction velocity, recruitment and derecruitment thresholds, and discharge rate of low-threshold motor units during a series of ramp contractions. The aim was to compare the adjustments in motor unit activity relative to the duration that each motor unit was active during the task. Multichannel surface electromyographic (EMG) signals were recorded from the abductor pollicis brevis muscle of eight healthy men during 12-s contractions (n = 25) in which the force increased and decreased linearly from 0 to 10% of the maximum. The maximal force exhibited a modest decline (8.5 +- 9.3%; P < 0.05) at the end of the task. The discharge times of 73 motor units that were active for 16-98% of the time during the first five contractions were identified throughout the task by decomposition of the EMG signals. Action potential conduction velocity decreased during the task by a greater amount for motor units that were initially active for >70% of the time compared with that of less active motor units. Moreover, recruitment and derecruitment thresholds increased for these most active motor units, whereas the thresholds decreased for the less active motor units. Another 18 motor units were recruited at an average of 171 +- 32 s after the beginning of the task. The recruitment and derecruitment thresholds of these units decreased during the task, but muscle fiber conduction velocity did not change. These results indicate that low-threshold motor units exhibit individual adjustments in muscle fiber conduction velocity and motor neuron activation that depended on the relative duration of activity during intermittent contractions.
Ključne besede: electromyography, surface electromyography, multi-channel EMG, motor units, decompostion, recruitment treshold, derecruitment treshold_
Objavljeno: 01.06.2012; Ogledov: 752; Prenosov: 7
URL Celotno besedilo (0,00 KB)

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Human-machine interfacing by decoding surface electromyogram
Dario Farina, Aleš Holobar, 2015, izvirni znanstveni članek

Ključne besede: decoding, electromyography, human computer interaction, neurons, accuracyuser interfaces
Objavljeno: 25.05.2015; Ogledov: 419; Prenosov: 0

5.
New perspectives for computer-aided discrimination of Parkinson's disease and essential tremor
Petra Povalej, J.A. Gallego, J. P. Romero, Vojko Glaser, E. Rocon, Julián Benito-León, Félix Bermejo-Pareja, Ignacio Posada, Aleš Holobar, 2017, izvirni znanstveni članek

Opis: Pathological tremor is a common but highly complex movement disorder, affecting ~5% of population older than 65 years. Different methodologies have been proposed for its quantification. Nevertheless, the discrimination between Parkinson's disease tremor and essential tremor remains a daunting clinical challenge, greatly impacting patient treatment and basic research. Here, we propose and compare several movement-based and electromyography-based tremor quantification metrics. For the latter, we identified individual motor unit discharge patterns from high-density surface electromyograms and characterized the neural drive to a single muscle and how it relates to other affected muscles in 27 Parkinson's disease and 27 essential tremor patients. We also computed several metrics from the literature. The most discriminative metrics were the symmetry of the neural drive to muscles, motor unit synchronization, and the mean log power of the tremor harmonics in movement recordings. Noteworthily, the first two most discriminative metrics were proposed in this study. We then used decision tree modelling to find the most discriminative combinations of individual metrics, which increased the accuracy of tremor type discrimination to 94%. In summary, the proposed neural drive-based metrics were the most accurate at discriminating and characterizing the two most common pathological tremor types.
Ključne besede: Parkinson's disease, essential tremor, electromyography, wrist movements, motor units, muscular excitation, decision tree
Objavljeno: 03.11.2017; Ogledov: 98; Prenosov: 3
.pdf Celotno besedilo (3,31 MB)

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