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Record W2536837960 · doi:10.1109/iembs.1991.684191

A Data Acquisition System For Real-time Activation Detection Of Cardiac Electrograms II: Software

2005· article· en· W2536837960 on OpenAlexaff
Stéphane Massé, Elias Sevaptsidis, Ian Parson, Shane Kimber, Eugene Downar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicHealthcare Technology and Patient Monitoring
Canadian institutionsToronto General HospitalToronto Metropolitan UniversityUniversity of Toronto
Fundersnot available
KeywordsComputer scienceData acquisitionSoftwareDetectorSampling (signal processing)Real-time computingMultiplexingInterface (matter)ThroughputComputer hardwareChannel (broadcasting)Process (computing)SIGNAL (programming language)WirelessTelecommunicationsOperating system

Abstract

fetched live from OpenAlex

A 672 channel digital data acquisition system with real-time activation detection and automatic gain setting has been designed for use in the operating room. In part I [l], the front end hardware has been described. This second part describes the software for the automatic activation detection and the automatic channel gain setting. Real-time activation detection is performed in parallel by 21 local DSPs using a 5 point FIR filter and a three-threshold detector. Time of execution is 20 ws, which is fast enough to process a 48 KHz multiplexed signal (16 unipolar channels at lKHz sampling rate + 16 bipolar channels at 2KHz sampling rate) on each data acquisition board, giving a total throughput of 1 MSamples/s (21 boards at 48 KH4board each).

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.068
Threshold uncertainty score0.229

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0680.033

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 head0.036
GPT teacher head0.317
Teacher spread0.282 · 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 designNot applicable
Domainnot available
GenreSoftware

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

Citations6
Published2005
Admission routes1
Has abstractyes

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