03030nas a2200265 4500008004100000022001400041245016500055210006900220260001600289490000700305520208500312653002502397653002102422653001302443653001102456653002102467653002202488653002602510100002502536700001702561700002202578700002202600700002202622856012002644 2023 eng d a1741-255200aMethods for automated delineation and assessment of EMG responses evoked by peripheral nerve stimulation in diagnostic and closed-loop therapeutic applications.0 aMethods for automated delineation and assessment of EMG response c2023 Jul 210 v203 a

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' recruitment curves (92 curves across all participants) and on the conditioning protocol'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 ±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±1.4%of the retrospective estimate; for the 'threshold' current that produced an M-wave 10% of maximum, this value was±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.

10aElectric Stimulation10aElectromyography10aH-Reflex10aHumans10aMuscle, Skeletal10aPeripheral Nerves10aRetrospective Studies1 aMcKinnon, Michael, L1 aHill, Jeremy1 aCarp, Jonathan, S1 aDellenbach, Blair1 aThompson, Aiko, K uhttps://www.neurotechcenter.org/publications/2023/methods-automated-delineation-and-assessment-emg-responses-evoked02917nas a2200265 4500008004100000022001400041245009700055210006900152260001200221300001000233490000800243520213200251653002202383653001202405653002102417653001302438653001802451653002102469653000902490653004102499100001902540700002302559700002102582856004802603 2002 eng d a0165-027000aTemporal transformation of multiunit activity improves identification of single motor units.0 aTemporal transformation of multiunit activity improves identific c02/2002 a87-980 v1143 a

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.

10aAction Potentials10aAnimals10aElectromyography10aH-Reflex10aMotor Neurons10aMuscle, Skeletal10aRats10aSignal Processing, Computer-Assisted1 aSchalk, Gerwin1 aCarp, Jonathan, S.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/11850043