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Cited by 105Muscle activation and electromyography studiesPeripheral Nerve DisordersMachine Fault Diagnosis Techniques

Time-scale analysis of motor unit action potentials

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Pattichis, C.S., Pattichis, M.S.·1999·IEEE Transactions on Biomedical Engineering
DOI 10.1109/10.797992
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Abstract

Quantitative 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.

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  • Features extracted in time and frequency domains increase the possibility of controlling more than two degrees of freedom when combined with advanced classification algorithms and on-line learning.

    “Several features can be extracted in both time and frequency domains (Zardoshti-Kermani et al., 1995; Englehart, 1998; Pattichis and Pattichis, 1999; Sparto et al., 2000) that increase the possibility of controlling more than two degrees of freedom of the prosthesis when used in combination with advanced classification algorithms (Kelly et al., 1990; Christodoulou and Pattichis, 1999; Micera et al”

    From A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems (2005) · cites this paper as Pattichis and Pattichis, 1999

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References 25 · Cited by 105

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  23. [29][29] C. N. Schizas, C. S. Pattichis, and C. A. Bonsett, 'Medical diagnostic systems: A case for neural networks,' Technol. Health Care, vol. 2, p. 1, 1994. <!-- image --> Constantinos S. Pattichis (S'88-M'88) was born in Nicosia, Cyprus, on June 30, 1959. He received the B.Sc. degree in electrical engineering from the University of New Brunswick, Fredericton, N.B., Canada, in 1983, the M.Sc. degree in biomedical engineering from the University of Texas at Austin, in 1984, the S.Sc. degree in neurophysiology from the University of Newcastle upon Tyne, Newcastle upon Tyne, U.K, in 1988, and the Ph.D. degree in electronic engineering from the University of London, London, U.K, in 1992. From 1985-1992, he was also working with the Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus, where he is now a Visiting Senior Scientist. He is currently an Assistant Professor with the Department of Computer Science at the University of Cyprus. He is actively participating in a number of European projects related to medical imaging and health telematics. His current research interests include medical imaging, biosignal analysis, artificial neural networks, and genetic algorithms. He has published more than 50 refereed journal and conference papers in these areas. Dr. Pattichis was the recipient of the 1994 European Community 'MarieCurie' Fellowship in image processing in histopathology. He has been involved in numerous professional society activities, including President of the Cyprus Association of Medical Physics and Biomedical Engineering, 1996-1998; President of the IEEE Cyprus Section, 1998-1999; General CoChairman, Medical and Biological Engineering and Computing Conference'98 (Medican'98) June 14-17, 1998. <!-- image --> Marios S. Pattichis (S'90-A'95) was born in Nicosia in 1967. He received the B.S. (Hons.) degree in computer science and the B.A. (Hons.) degree in mathematics in 1991. He also received the M.Sc. and Ph.D. degrees in electrical engineering in 1993 and 1998, respectively, all from the University of Texas at Austin. He is currently a Visiting Assistant Professor in the Department of Electrical and Computer Engineering and the Center for High-Performance Computing, at the University of New Mexico. His research interests are in the broad area of signal and image processing, with an emphasis on AM-FM transforms and their applications, his dissertation topic. For his undergraduate studies, Dr. Pattichis received a scholarship from the Cyprus-America Scholarship program.

Authors
Pattichis, C. S., & Pattichis, M. S.
Affiliations
  • University of Cyprus (CY)
  • University of New Mexico (US)
ORCID
Venue
IEEE Transactions on Biomedical Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
1999
Volume
46
Issue
11
Pages
1320-1329
ISSN
0018-9294 (ISSN-L)
DOI
10.1109/10.797992
OpenAlex
W2148154913
Cited by
105
References
25
Citations by year
2026: 1 · 2025: 2 · 2024: 3 · 2023: 4 · 2022: 2 · 2021: 2 · 2020: 4 · 2019: 3 · 2018: 3 · 2017: 3 · 2016: 6 · 2015: 8 · 2014: 5 · 2013: 9 · 2012: 6
FWCI
1.07
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Biomedical Engineering (publishedVersion)
  • PubMed (publishedVersion)
  • Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Peripheral Nerve Disorders
  • Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
Keywords
motor unit action potentials, wavelet transform, scalogram, neuromuscular disorders, motor neuron disease, myopathy, Daubechies wavelet, Battle-Lemarie wavelets, time-frequency analysis, neural network classification, quantitative electromyography
MeSH terms
Action Potentials, Electromyography, Humans, Motor Neurons, Muscular Diseases, Stochastic Processes, Time Factors, Motor Neuron Disease, Neural Networks, Computer, Muscle, Skeletal

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