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Computer pattern recognition of motor unit potentials
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I) Paresis due to reduced neural input into the paretic muscle; 2) loss of s e l e c ti v e innervation; 3) d i s t o r t i o n of the temporal ordering of the spread of e x c i t a t i o n along a limb; 4) the d i s t r i b u t i o n of the paretic muscle groups. Correlation with the site of the lesion in the CT-scan showed that d i f f e r e n t patterns of disturbance are due to lesions in d i f f e r e n t c o r t i c a l areas. This w i l l be i l l u s t r a t e d by comparing lesions in motor and premotor cortex.
$I0
ENTRAPMENTNEUROPATHIES
GILLIATT,R.W.,
I n s t i t u t e of Neurology, Queen Square, London, UK.
In current research on mechanism of nerve damage in entrapment neuropathy, the r e l a t i v e importance of d i r e c t mechanical damage to nerve fibres and of nerve ischaemia continues to be debated. Of equal importance is the question as to why recovery of a chronic lesion is often poor in spite of adequate surgical decompression of the entrapped nerve. Recent studies of slowly progressive axonal atrophy occurring proximal to the s i t e of the lesion or d i s t a l to i t , may throw l i g h t on this aspect. The extent to which loss of f a s c i c u l a r architecture and endoneurial f i b r o s i s may also act as barriers to axons attempting to regenerate through these lesions, w i l l also be discussed.
S11
COMPUTERPATTERN RECOGNITION OF MOTOR UNIT POTENTIALS
GUIHENEUC,P., Laboratoire de Physiologie, U.E.R. de M~decine, 44o35 Nantes Cedex, France Several methods of data processing have been developed in recent years, which provide an automatic MUP analysis from EMG signals recorded with a needle electrode during muscle s l i g h t voluntary contraction. A few of them include a true pattern recognition process, whose i n t e r e s t is pointed out. Anew method of f i l t e r i n g and detection is described, carried out on l i n e by recursive computation of the signal variance through receeding horizon f i l t e r s . The boundaries of each s i g n i f i c a n t p o t e n tia l are set up using an algorithm based on an automated and permanently adjusted measurement of the s i g n a l - t o noise r a t i o . A complete pattern recognition process allows to analyze superimposed waveforms and to recognize a l l the p o t e n t i a l s of each MUP t r a i n contained in an EMG record. Two types of methods are used mainly based e i t h e r on time i n f o r mation ( s t a t i s t i c a l analysis of interpulse i n t e r v a l s ) or on shape c o r r e l a t i o n (computation of a difference between compared p o t e n t i a l s ) . V a l i d a t i o n of these techniques are possible using computer generated EMGtraces. Advantages and l i m i t a t i o n s of each one are emphasized, and t h e i r pract i c a l i n t e r e s t discussed according to d i f f e r e n t needs and goals.
I) Paresis due to reduced neural input into the paretic muscle; 2) loss of s e l e c ti v e innervation; 3) d i s t o r t i o n of the temporal ordering of the spread of e x c i t a t i o n along a limb; 4) the d i s t r i b u t i o n of the paretic muscle groups. Correlation with the site of the lesion in the CT-scan showed that d i f f e r e n t patterns of disturbance are due to lesions in d i f f e r e n t c o r t i c a l areas. This w i l l be i l l u s t r a t e d by comparing lesions in motor and premotor cortex.
$I0
ENTRAPMENTNEUROPATHIES
GILLIATT,R.W.,
I n s t i t u t e of Neurology, Queen Square, London, UK.
In current research on mechanism of nerve damage in entrapment neuropathy, the r e l a t i v e importance of d i r e c t mechanical damage to nerve fibres and of nerve ischaemia continues to be debated. Of equal importance is the question as to why recovery of a chronic lesion is often poor in spite of adequate surgical decompression of the entrapped nerve. Recent studies of slowly progressive axonal atrophy occurring proximal to the s i t e of the lesion or d i s t a l to i t , may throw l i g h t on this aspect. The extent to which loss of f a s c i c u l a r architecture and endoneurial f i b r o s i s may also act as barriers to axons attempting to regenerate through these lesions, w i l l also be discussed.
S11
COMPUTERPATTERN RECOGNITION OF MOTOR UNIT POTENTIALS
GUIHENEUC,P., Laboratoire de Physiologie, U.E.R. de M~decine, 44o35 Nantes Cedex, France Several methods of data processing have been developed in recent years, which provide an automatic MUP analysis from EMG signals recorded with a needle electrode during muscle s l i g h t voluntary contraction. A few of them include a true pattern recognition process, whose i n t e r e s t is pointed out. Anew method of f i l t e r i n g and detection is described, carried out on l i n e by recursive computation of the signal variance through receeding horizon f i l t e r s . The boundaries of each s i g n i f i c a n t p o t e n tia l are set up using an algorithm based on an automated and permanently adjusted measurement of the s i g n a l - t o noise r a t i o . A complete pattern recognition process allows to analyze superimposed waveforms and to recognize a l l the p o t e n t i a l s of each MUP t r a i n contained in an EMG record. Two types of methods are used mainly based e i t h e r on time i n f o r mation ( s t a t i s t i c a l analysis of interpulse i n t e r v a l s ) or on shape c o r r e l a t i o n (computation of a difference between compared p o t e n t i a l s ) . V a l i d a t i o n of these techniques are possible using computer generated EMGtraces. Advantages and l i m i t a t i o n s of each one are emphasized, and t h e i r pract i c a l i n t e r e s t discussed according to d i f f e r e n t needs and goals.