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Record W2199985122 · doi:10.1109/jcn.2011.6157410

Wireless three-pad ECG system: Challenges, design, and evaluations

2011· article· en· W2199985122 on OpenAlexafffund
Huasong Cao, Haoming Li, Leo Stocco, Victor C. M. Leung

Bibliographic record

VenueJournal of Communications and Networks · 2011
Typearticle
Languageen
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsUniversity of British Columbia
FundersDalhousie University
KeywordsComputer scienceElectrocardiographyLead (geology)WirelessData miningArtificial intelligenceMedicineTelecommunicationsCardiology

Abstract

fetched live from OpenAlex

Electrocardiography (ECG) is a widely accepted approach for monitoring of cardiac activity and clinical diagnosis of heart diseases. Since cardiologists have been well-trained to accept 12-lead ECG information, a huge number of ECG systems are using such number of electrodes and placement configuration to facilitate fast interpretation. Our goal is to design a wireless ECG system which renders conventional 12-lead ECG information. We pro pose the three-pad ECG system (W3ECG). W3ECG furthers the pad design idea of the single-pad approach. Signals obtained from these three pads, plus their placement information, make it possible to synthesize conventional 12-lead ECG signals. We provide one example of pad placement and evaluate its performance by examining ECG data of four patients available from online database. Feasibility test of our selected pad placement positions show comparable results with respect to the EASI lead system. Experimental results also exhibit high correlations between synthesized and directly observed 12-lead signals (9 out of 12 cross-correlation coefficients higher than 0.75).

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.078
GPT teacher head0.255
Teacher spread0.177 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations31
Published2011
Admission routes2
Has abstractyes

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