{"doi":"10.1088/1741-2552/ace6fb","title":"Methods for automated delineation and assessment of EMG responses evoked by peripheral nerve stimulation in diagnostic and closed-loop therapeutic applications","abstract":"Abstract Objective. 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. Approach. 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 H max . Results were compared against the original temporal bounds estimated at the time, and against retrospective estimates made by an expert 6 years later. Main results. 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 elicit H max fell within <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" overflow=\"scroll\"> <mml:mo>±</mml:mo> <mml:mn>1.4</mml:mn> <mml:mi mathvariant=\"normal\">%</mml:mi> </mml:math> of the retrospective estimate; for the ‘threshold’ current that produced an M-wave 10% of maximum, this value was <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" overflow=\"scroll\"> <mml:mo>±</mml:mo> <mml:mn>3.5</mml:mn> <mml:mi mathvariant=\"normal\">%</mml:mi> </mml:math> . Significance. 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.","journal":"Journal of Neural Engineering","year":2023,"id":368100,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9537,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":764450,"name":"N. Jeremy Hill","orcid":"0000-0002-4253-6976","position":1,"is_corresponding":false},{"id":610887,"name":"Jonathan S. Carp","orcid":"0000-0002-9430-0711","position":2,"is_corresponding":false},{"id":1126045,"name":"Blair Dellenbach","orcid":"0000-0002-7033-3877","position":3,"is_corresponding":false},{"id":548722,"name":"Aiko K. Thompson","orcid":"0000-0001-9486-8537","position":4,"is_corresponding":false},{"id":954102,"name":"Michael McKinnon","orcid":"0000-0002-7677-725X","position":0,"is_corresponding":true}],"reference_count":51,"raw_metadata":null,"created_at":"2026-07-19T01:15:20.453499Z","pmid":"37437593","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}