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Age effects on motor unit action potential properties: ADEMG analysis
S120
VIII CONGRESS OF E M G A N D R E L A T E D C L I N I ( A L N E U R O P H Y S I O L O G Y
SY. Clinical use of automatic decomposition motor unit analysis. - L.J. Dorfman (Dept. of Neurology, Stanford University School of Medicine, Palo Alto, CA 94305, U.S.A.) A fully automatic method ( A D E M G ) has been developed for decomposing E M G interference patterns (IPs) into their constituent motor unit action potentials (MUAPs) for clinical application. The initial step in A D E M G is zero-phase-shift digital prefiltering of a 10 sec epoch of stationary IP. The filtered spikes are classified using a computationally efficient algorithm and are then used as triggers to average the true M U A P wave shapes from the original IP. Finally, an interference cancellation algorithm removes from each averaged M U A P wave form any residual noise attributable to other identified MUAPs. A D E M G is fast (analysis time _< 2 min) and can accommodate IP signals from contractions up to 30% MVC. Normative data in adults currently comprise over 13000 M U A P s stratified according to muscle, age, gender and contractile force. Compared with traditional single-MUAP analysis, automatic decomposition E M G offers the advantages of (i) rapid, automatic data acquisition and measurement, (ii) analysis of M U A P s with both low and higher recruitment thresholds, and (iii) measurement of not only M U A P configurational properties, but also firing rates, which m a y prove helpful in the evaluation of neuromuscular disorders.
PS. Age effects on motor unit action potential properties: ADEMG analysis, - L~I. Dodnmn, K.C. McGill and J.E. Howard ( D e ~ of Neurology, Stanford University School of Medicine, Palo Alto, CA 94305, U.S.A.) We have measured the configurational and firing properties of 13000 motor unit action potentials (MUAPs) from the brachial biceps, triceps and anterior tibial muscles in 10 young (20-40 yr), intermediate (40-60 yr) and elderly (60-80 yr) normal individuals, using a new, automatic method for decomposition of the E M G interference pattern (ADEMG). Recordings were made during stable isometric contractions at threshold, 10% and 30% of m a x i m u m voluntary contraction (MVC) using standard concentric needle electrodes. Mean M U A P amplitudes, durations and numbers of phases all increased significantly with age. Within each age group, increasing force of contraction was associated with increase of mean M U A P amplitude and firing rate, but decrease of mean M U A P duration, most likely due to noise-dependency of the duration measurement. Mean firing rates increased with age when force was measured proportionately. These findings extend previous observations from traditional analysis of lowest-threshold single MUAPs, establish a base of normative adult data for A D E M G , and further validate the clinical applicability of automatic E M G decomposition. These findings also suggest that measurement of M U A P firing rates in relationship to contractile force may be a useful parameter in the electrodiagnosis of neuromuscular disorders.
PS. Dissociation between cervical N13 and scalp-recorded farfield P13 in SEPs recorded from brain-dead patients. E. Facco, M. Munari and F. Toffoletto (Dept. Anesthesiology and Intensive Care, University of Padua, Padua, Italy) In short-latency SEPs to median nerve stimulation a cervical N13 and a scalp-recorded, far-field P13 can be identified when a non-cephalic reference is employed. The generator sources of the cervical N13 and of the scalp P13 are still matter of debate. In this report we provide further information on the localization of N13 and P13 generator neural substrates. Twelve patients who were brain-dead due to severe head injury or subarachnoid hemorrhage were included in this investigation. In three of them the scalp-recorded P13 was missing despite the presence of the cervical NI 3, whilst in the remaining patients both peaks were identifiable. Such a dissociation between cervical N13 and scalp P13 waves is an u n c o m m o n finding. Our data further suggest that cervical N13 and scalp Pt 3 peaks recognize two distinct generator sources, namely a tangentially oriented dipole at the caudal cervical cord for wave N13 and a radially oriented dipole at the brain-stem level for the scalp P13.
PS. Knowledge-based analysis of electTomyograms. - C. Faure *, A. Touraine **, J. S e n a n t * * and J. Quignon * * * ( * E N S T / U A , CNRS 820, 75634 Paris Cedex 13, ** Service d'Explorations Nenrologiques, Hfpital Charles Nico~le, 76038 Rouen, and ** * U T C / U A , C N R S 817, 6 0 2 0 6 C ~ Cedex, France) Electromyograms (EMG) are characterized by a background on which events (motor unit potentials, MUPs) are superimposed. The visual interpretation carried out by the h u m a n expert in the field consists in detecting and identifYing M U P s belonging to the same motor unit (MU). The M U interference during the contraction are counted, and for each of them, features describing characteristics of its associated M U P wave form are extracted. The recognition system utilizes 3 types of information (structural similarity, template extraction, periodicity) in the same way that the specialist reasons. Hence the system's decision and the h u m a n expert's interpretation should be given in the same terms. The system is structured as a data base, a set of procedures and a control. The data base is an STM which contains the intermediate results of the analysis and evolves until it contains the final interpretation of the signal. The procedures perform actions on data as pattern-matching, classification, or they check the validity of conditions on the data base. The control is aimed at the cooperation of different types of information. Cooperation is achieved by a production system which is implemented as a graph.
VIII CONGRESS OF E M G A N D R E L A T E D C L I N I ( A L N E U R O P H Y S I O L O G Y
SY. Clinical use of automatic decomposition motor unit analysis. - L.J. Dorfman (Dept. of Neurology, Stanford University School of Medicine, Palo Alto, CA 94305, U.S.A.) A fully automatic method ( A D E M G ) has been developed for decomposing E M G interference patterns (IPs) into their constituent motor unit action potentials (MUAPs) for clinical application. The initial step in A D E M G is zero-phase-shift digital prefiltering of a 10 sec epoch of stationary IP. The filtered spikes are classified using a computationally efficient algorithm and are then used as triggers to average the true M U A P wave shapes from the original IP. Finally, an interference cancellation algorithm removes from each averaged M U A P wave form any residual noise attributable to other identified MUAPs. A D E M G is fast (analysis time _< 2 min) and can accommodate IP signals from contractions up to 30% MVC. Normative data in adults currently comprise over 13000 M U A P s stratified according to muscle, age, gender and contractile force. Compared with traditional single-MUAP analysis, automatic decomposition E M G offers the advantages of (i) rapid, automatic data acquisition and measurement, (ii) analysis of M U A P s with both low and higher recruitment thresholds, and (iii) measurement of not only M U A P configurational properties, but also firing rates, which m a y prove helpful in the evaluation of neuromuscular disorders.
PS. Age effects on motor unit action potential properties: ADEMG analysis, - L~I. Dodnmn, K.C. McGill and J.E. Howard ( D e ~ of Neurology, Stanford University School of Medicine, Palo Alto, CA 94305, U.S.A.) We have measured the configurational and firing properties of 13000 motor unit action potentials (MUAPs) from the brachial biceps, triceps and anterior tibial muscles in 10 young (20-40 yr), intermediate (40-60 yr) and elderly (60-80 yr) normal individuals, using a new, automatic method for decomposition of the E M G interference pattern (ADEMG). Recordings were made during stable isometric contractions at threshold, 10% and 30% of m a x i m u m voluntary contraction (MVC) using standard concentric needle electrodes. Mean M U A P amplitudes, durations and numbers of phases all increased significantly with age. Within each age group, increasing force of contraction was associated with increase of mean M U A P amplitude and firing rate, but decrease of mean M U A P duration, most likely due to noise-dependency of the duration measurement. Mean firing rates increased with age when force was measured proportionately. These findings extend previous observations from traditional analysis of lowest-threshold single MUAPs, establish a base of normative adult data for A D E M G , and further validate the clinical applicability of automatic E M G decomposition. These findings also suggest that measurement of M U A P firing rates in relationship to contractile force may be a useful parameter in the electrodiagnosis of neuromuscular disorders.
PS. Dissociation between cervical N13 and scalp-recorded farfield P13 in SEPs recorded from brain-dead patients. E. Facco, M. Munari and F. Toffoletto (Dept. Anesthesiology and Intensive Care, University of Padua, Padua, Italy) In short-latency SEPs to median nerve stimulation a cervical N13 and a scalp-recorded, far-field P13 can be identified when a non-cephalic reference is employed. The generator sources of the cervical N13 and of the scalp P13 are still matter of debate. In this report we provide further information on the localization of N13 and P13 generator neural substrates. Twelve patients who were brain-dead due to severe head injury or subarachnoid hemorrhage were included in this investigation. In three of them the scalp-recorded P13 was missing despite the presence of the cervical NI 3, whilst in the remaining patients both peaks were identifiable. Such a dissociation between cervical N13 and scalp P13 waves is an u n c o m m o n finding. Our data further suggest that cervical N13 and scalp Pt 3 peaks recognize two distinct generator sources, namely a tangentially oriented dipole at the caudal cervical cord for wave N13 and a radially oriented dipole at the brain-stem level for the scalp P13.
PS. Knowledge-based analysis of electTomyograms. - C. Faure *, A. Touraine **, J. S e n a n t * * and J. Quignon * * * ( * E N S T / U A , CNRS 820, 75634 Paris Cedex 13, ** Service d'Explorations Nenrologiques, Hfpital Charles Nico~le, 76038 Rouen, and ** * U T C / U A , C N R S 817, 6 0 2 0 6 C ~ Cedex, France) Electromyograms (EMG) are characterized by a background on which events (motor unit potentials, MUPs) are superimposed. The visual interpretation carried out by the h u m a n expert in the field consists in detecting and identifYing M U P s belonging to the same motor unit (MU). The M U interference during the contraction are counted, and for each of them, features describing characteristics of its associated M U P wave form are extracted. The recognition system utilizes 3 types of information (structural similarity, template extraction, periodicity) in the same way that the specialist reasons. Hence the system's decision and the h u m a n expert's interpretation should be given in the same terms. The system is structured as a data base, a set of procedures and a control. The data base is an STM which contains the intermediate results of the analysis and evolves until it contains the final interpretation of the signal. The procedures perform actions on data as pattern-matching, classification, or they check the validity of conditions on the data base. The control is aimed at the cooperation of different types of information. Cooperation is achieved by a production system which is implemented as a graph.