@article {4492, title = {Methods for automated delineation and assessment of EMG responses evoked by peripheral nerve stimulation in diagnostic and closed-loop therapeutic applications.}, journal = {J Neural Eng}, volume = {20}, year = {2023}, month = {2023 Jul 21}, abstract = {

Surface electromyography measurements of the Hoffmann (H-) reflex are essential in a wide range of neuroscientific and clinical applications. One promising emerging therapeutic application is H-reflex operant conditioning, whereby a person is trained to modulate the H-reflex, with generalized beneficial effects on sensorimotor function in chronic neuromuscular disorders. Both traditional diagnostic and novel realtime therapeutic applications rely on accurate definitions of the H-reflex and M-wave temporal bounds, which currently depend on expert case-by-case judgment. The current study automates such judgments.Our novel wavelet-based algorithm automatically determines temporal extent and amplitude of the human soleus H-reflex and M-wave. In each of 20 participants, the algorithm was trained on data from a preliminary 3 or 4 min recruitment-curve measurement. Output was evaluated on parametric fits to subsequent sessions{\textquoteright} recruitment curves (92 curves across all participants) and on the conditioning protocol{\textquoteright}s subsequent baseline trials (\~{}1200 per participant) performed near. Results were compared against the original temporal bounds estimated at the time, and against retrospective estimates made by an expert 6 years later.Automatic bounds agreed well with manual estimates: 95\% lay within {\textpm}2.5 ms. The resulting H-reflex magnitude estimates showed excellent agreement (97.5\% average across participants) between automatic and retrospective bounds regarding which trials would be considered successful for operant conditioning. Recruitment-curve parameters also agreed well between automatic and manual methods: 95\% of the automatic estimates of the current required to elicitfell within{\textpm}1.4\%of the retrospective estimate; for the {\textquoteright}threshold{\textquoteright} current that produced an M-wave 10\% of maximum, this value was{\textpm}3.5\%.Such dependable automation of M-wave and H-reflex definition should make both established and emerging H-reflex protocols considerably less vulnerable to inter-personnel variability and human error, increasing translational potential.

}, keywords = {Electric Stimulation, Electromyography, H-Reflex, Humans, Muscle, Skeletal, Peripheral Nerves, Retrospective Studies}, issn = {1741-2552}, doi = {10.1088/1741-2552/ace6fb}, author = {McKinnon, Michael L and Hill, N Jeremy and Carp, Jonathan S and Dellenbach, Blair and Thompson, Aiko K} } @article {2164, title = {Temporal transformation of multiunit activity improves identification of single motor units.}, journal = {J Neurosci Methods}, volume = {114}, year = {2002}, month = {02/2002}, pages = {87-98}, abstract = {

This report describes a temporally based method for identifying repetitive firing of motor units. This\ approach\ is ideally suited to spike trains with negative serially correlated inter-spike intervals (ISIs). It can also be applied to spike trains in which ISIs exhibit little serial correlation if their coefficient of variation (COV) is sufficiently low. Using a novel application of the Hough transform, this method (i.e. the modified Hough transform (MHT)) maps motor unit action potential (MUAP) firing times into a feature space with ISI and offset (defined as the latency from an arbitrary starting time to the first MUAP in the train) as dimensions. Each MUAP firing time corresponds to a pattern in the feature space that represents all possible MUAP trains with a firing at that time. Trains with stable ISIs produce clusters in the feature space, whereas randomly firing trains do not. The MHT provides a direct estimate of mean firing rate and its variability for the entire data segment, even if several individual MUAPs are obscured by firings from other motor units. Addition of this method to a shape-based classification\ approach\ markedly improved rejection of false positives using simulated data and identified spike trains in whole muscle electromyographic recordings from rats. The relative independence of the MHT from the need to correctly classify individual firings permits a global description of stable repetitive firing behavior that is complementary to shape-based approaches to MUAP classification.

}, keywords = {Action Potentials, Animals, Electromyography, H-Reflex, Motor Neurons, Muscle, Skeletal, Rats, Signal Processing, Computer-Assisted}, issn = {0165-0270}, doi = {10.1016/S0165-0270(01)00517-9}, url = {http://www.ncbi.nlm.nih.gov/pubmed/11850043}, author = {Gerwin Schalk and Jonathan S. Carp and Jonathan Wolpaw} }