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Record W4367174655 · doi:10.1371/journal.pone.0284911

Surface electromyography signal processing and evaluation on respiratory muscles of critically ill patients: A systematic review

2023· review· en· W4367174655 on OpenAlexaboutno aff
Emanuel Fernandes Ferreira da Silva Júnior, Shirley Lima Campos, Wagner Souza Leite, Pedro Vanderlei de Sousa Melo, Rômulo Aquino Coelho Lins, Maria das Graças Rodrigues de Araújo, Marcelo Renato Guerino

Bibliographic record

VenuePLoS ONE · 2023
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsnot available
FundersUniversidade Federal de PernambucoFundação de Amparo à Ciência e Tecnologia do Estado de PernambucoConselho Nacional de Desenvolvimento Científico e TecnológicoUniversidade de PernambucoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsElectromyographyMedicineParasternal linePhysical medicine and rehabilitationCardiology

Abstract

fetched live from OpenAlex

BACKGROUND: Surface Electromyography (sEMG) has been used to monitor respiratory muscle function and contractility in several clinical situations, however there is the lack of standardization for the analysis and processing of the signals. OBJECTIVE: To summarize the respiratory muscles most assessed by sEMG in the critical care setting and the assessment procedure details employed on those muscles regarding electrode placement, signal acquisition, and data analysis. METHODS: A systematic review of observational studies was registered on PROSPERO (number CRD42022354469). The databases included PubMed; SCOPUS; CINAHL, Web of Science and ScienceDirect. Two independent reviewers ran the quality assessment of the studies using the Newcastle-Ottawa Scale and Downs & Black checklists. RESULTS: A total of 311 participants were involved across the 16 studies, from which 62.5% (10) assessed the diaphragm muscle and 50% (8) assessed the parasternal muscle with similar electrode placement in both of them. We did not identify common patterns for the location of the electrodes in the sternocleidomastoid and anterior scalene muscles. 12/16 reported sample rate, 10/16 reported band-pass and 9/16 reported one method of cardiac-interference filtering technique. 15/16 reported Root Mean Square (RMS) or derivatives as sEMG-obtained variables. The main applicabilities were the description of muscle activation in different settings (6/16), testing of reliability and correlation to other respiratory muscles assessment techniques (7/16), and assessment of therapy response (3/16). They found sEMG feasible and useful for prognosis purposes (2/16), treatment guidance (6/16), reliable monitoring under stable conditions (3/16), and as a surrogate measure (5/16) in mechanically ventilated patients in elective or emergency invasive procedures (5/16) or in acute health conditions (11/16). CONCLUSIONS: The diaphragm and parasternal muscles were the main muscles studied in the critical care setting, and with similar electrodes placement. However, several different methods were observed for other muscles electrodes placement, sEMG signals acquisition and data analysis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.006
Bibliometrics0.0080.009
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.145
GPT teacher head0.354
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2023
Admission routes1
Has abstractyes

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