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Record W2687994748 · doi:10.1093/ehjci/eux141.107

P382Bipolar electrograms directed across wavefronts provide greater physiological accuracy

2017· article· en· W2687994748 on OpenAlexaff
Shouvik Haldar, Karl Magtibay, Stéphane Massé, M. Kusha, MA. Azam, John Asta, PFH Lai, DC. Deno, Kumaraswamy Nanthakumar

Bibliographic record

VenueEP Europace · 2017
Typearticle
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

Background: The orientation of a bipole along or across the wavefront direction influences electrogram (EGM) appearance, thus providing ambiguous measurements both in terms of timing and frequency content that have no reliable physiological interpretation. Bipolar measurements of activation timing have been challenged. EGM measurements not influenced by wavefront direction with Omnipolar Mapping Technology (OT) could increase signal fidelity and improve discrimination timing. Objectives: We sought to: 1) Characterize the frequency content and the width of timing information contained in regular bipolar and OT EGM signals and assess whether the OT-EGMs provide more physiological information than bipolar EGMs. 2) Document how these EGMs are distinctive visually by electrophysiologists in terms of near-field and non-near field classification. Methods: A high-density custom-made electrode array (1mm electrodes, 2x2mm spacing) was deployed in 2 pig Langendorff perfused healthy ventricles with certainty of tissue contact. One hundred bipolar EGMs were collected labeled horizontal (H, n=50) and vertical (V, n=50). One hundred more perpendicular but direction-independent EGMs were computed from these same bipoles with OT in directions denoted as OT-Ea (n=50 along the travelling wave’s activation direction) and OT-Ew (perpendicular to OT-Ea, n=50). All EGMs were randomized and blindly analyzed with same scaling and filtering by 8 experienced electrophysiologists that had to discriminate them as near-field signal or non-near-field. Frequency content of the EGMs was automatically analyzed with fast Fourier transform (FFT) of the signal. Width of the absolute dV/dt of the EGMs was analyzed with custom-made MATLAB code. Results: Overall, EGMs recorded were classified as near field by electrophysiolgists 44.5% of the times. Bipolar H and V EGMs were interpreted as near field in 46.3% of recordings. Of the OT directed EGMs, OT-Ea recordings were interpreted as near field in 46.0% of cases (p=0.6 vs H and V bipoles). OT-Ew EGMs were distinctive, interpreted as near-field in 36.0% of cases, p<0.05 vs other recordings. In addition, the frequency content of OT-Ew was higher than for common bipoles and OT-Ea (69.9±21Hz vs 60.9±14Hz vs 52.0±6.7Hz respectively, p<0.001), indicating a sharper morphology of the signal. In addition, the average width of the dV/dt of the EGMs was smaller for OT-Ew compared to bipoles and to OT-Ea respectively (10.3±4ms vs 13.4±3.3ms vs 15.8±2.1ms, p<0.05) indicating less possible error of annotation if used as a timing tag. Conclusions: Despite obtaining EGMs in good contact with the tissue, visual assessment often misclassifies bipolar EGMs as non-near field depending on their position along or across the wavefront. The frequency content and the width of dV/dt of the EGMs obtained across a wavefront (OT-Ew) indicate a sharper and narrower signal, which could be used to improve physiological reliability of timing of local annotation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.101
GPT teacher head0.381
Teacher spread0.280 · 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 teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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