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dc.contributor.authorPattichis, Constantinos S.en
dc.contributor.authorPattichis, Marios S.en
dc.creatorPattichis, Constantinos S.en
dc.creatorPattichis, Marios S.en
dc.description.abstractQuantitative analysis in clinical electromyography (EMG) is very desirable because it allows a more standardized, sensitive and specific evaluation of the neurophysiological findings, especially for the assessment of neuromuscular disorders. Following the recent development of computer- aided EMG equipment, different methodologies in the time domain and frequency domain have been followed for quantitative analysis. In this study, the usefulness of the wavelet transform (WT), that provides a linear time-scale representation is investigated, for describing motor unit action potential (MUAP) morphology. The motivation behind the use of the WT is that it provides localized statistical measures (the scalogram) for nonstationary signal analysis. The following four WT's were investigated in analyzing a total of 800 MUAP's recorded from 12 normal subjects, 15 subjects suffering with motor neuron disease, and 13 from myopathy: Daubechies with four and 20 coefficients, Chui (CH), and Battle-Lemarie (BL). The results are summarized as follows: 1) most of the energy of the MUAP signal is distributed among a small number of well-localized (in time) WT coefficients in the region of the main spike, 2) for MUAP signals, we look to the low-frequency coefficients for capturing the average waveshape of the MUAP signal over long durations, and we look to the high-frequency coefficients for locating MUAP spike changes, 3) the Daubechies 4 wavelet, is effective in tracking the transient components of the MUAP signal, 4) the linear spline CH (semiorthogonal) wavelet provides the best MUAP signal approximation by capturing most of the energy in the lowest resolution approximation coefficients, and 5) neural network DY (DY) of Daubechies 4 and BL WT coefficients was in the region of 66%, whereas DY for the empirically determined time domain feature set was 78%. In conclusion, wavelet analysis provides a new way in describing MUAP morphology in the time-frequency plane. This method allows for the fast extraction of localized frequency components, which when combined with time domain analysis into a modular neural network decision support system enhances further the DY to 82.5% aiding the neurophysiologist in the early and accurate diagnosis of neuromuscular disorders.en
dc.sourceIEEE Transactions on Biomedical Engineeringen
dc.subjectcontrolled studyen
dc.subjectclinical articleen
dc.subjectTime Factorsen
dc.subjectartificial neural networken
dc.subjectNeural Networks (Computer)en
dc.subjectWavelet analysisen
dc.subjectMuscle, Skeletalen
dc.subjectMotor Neuronsen
dc.subjectmotor neuron diseaseen
dc.subjectStochastic Processesen
dc.subjectMuscular Diseasesen
dc.subjectAction Potentialsen
dc.subjectbiceps brachii muscleen
dc.subjectmotor unit potentialen
dc.subjectElectromyography (EMG)en
dc.subjectMotor unit action potentials (MUAP's)en
dc.subjectTime-scale analysisen
dc.titleTime-scale analysis of motor unit action potentialsen
dc.description.endingpage1329 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied SciencesΤμήμα Πληροφορικής / Department of Computer Science
dc.description.notes<p>Cited By :63</p>en
dc.source.abbreviationIEEE Trans.Biomed.Eng.en
dc.contributor.orcidPattichis, Constantinos S. [0000-0003-1271-8151]
dc.contributor.orcidPattichis, Marios S. [0000-0002-1574-1827]

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