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Record W1569045096 · doi:10.1109/wisp.2015.7139157

R-peak detection algorithm based on differentiation

2015· article· en· W1569045096 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicECG Monitoring and Analysis
Canadian institutionsUniversity of Ottawa
FundersSecretaría de Educación Superior, Ciencia, Tecnología e Innovación
KeywordsSensitivity (control systems)DetectorComputer scienceDerivative (finance)AlgorithmSecond derivativeMathematicsEngineeringElectronic engineeringTelecommunications

Abstract

fetched live from OpenAlex

This paper presents an R peak detection algorithm for ECG signals based on the second derivative. Such R peak detection techniques offer low average time error and are computationally inexpensive. However, previously proposed methods based on the second derivatives suffer from low sensitivity and positive predictivity. In this study, we introduce a new mechanism at the peaks detector stage to resolve the aforementioned issues. We compared our proposed algorithm to an existing one that is also based on second derivative. The obtained results show an improvement in terms of sensitivity and positive predictivity, while maintaining a low average time error.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score0.150

Codex and Gemma teacher scores by category

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

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 head0.026
GPT teacher head0.274
Teacher spread0.249 · 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

Quick stats

Citations31
Published2015
Admission routes1
Has abstractyes

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