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Record W2465675980 · doi:10.1109/embsisc.2016.7508621

Validation of the Empatica E4 wristband

2016· article· en· W2465675980 on OpenAlexaff
Cameron G. McCarthy, Nikhilesh Pradhan, Calum J. Redpath, Andy Adler

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversity of OttawaCarleton University
Fundersnot available
KeywordsAtrial fibrillationPhotoplethysmogramMedicineGold standard (test)Heart RhythmElectrocardiographyComputer scienceCardiologyInternal medicineComputer vision

Abstract

fetched live from OpenAlex

A preliminary study into the signal quality of the Empatica's E4 portable photoplethysmogram device as validation for further research on using the device to detect the heart arrhythmia atrial fibrillation. In order to pursue this research, it was necessary to verify the quality of the data produced by the E4 device against a device currently used by clinicians to detect atrial fibrillation. This was done by having healthy volunteers wear both the Empatica E4 and the clinician standard device while following standard clinician procedure for atrial fibrillation diagnosis. The device was compared qualitatively against General Electric's SEER Light Extend Recorder holter portable electrocardiogram. The analysis was done by non-experts at varying academic levels. The primary reviewer's results showed similar data quality between both devices 85% of the time and the holter performed better 5% of the time.

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)0.024
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.061
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.059
GPT teacher head0.326
Teacher spread0.267 · 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 designObservational
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".

Quick stats

Citations231
Published2016
Admission routes1
Has abstractyes

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