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Record W2475893125 · doi:10.1109/bsn.2016.7516240

ECG compression for mobile sensor platforms

2016· article· en· W2475893125 on OpenAlexaff
Yi Ding, Željko Žilić

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicECG Monitoring and Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceCompression ratioData compressionContext (archaeology)Compression (physics)Scheme (mathematics)Transmission (telecommunications)Wearable technologyWearable computerReal-time computingWavelet transformWaveletArtificial intelligenceEmbedded systemEngineeringTelecommunicationsMathematicsMaterials science

Abstract

fetched live from OpenAlex

This paper presents a low-complexity compression scheme of electrocardiogram (ECG) signals based on the Haar wavelet transform (HWT) for use on mobile devices. An experimental, wearable, multi-lead ECG monitor was also developed and served as a testing platform for the proposed compression scheme. The proposed scheme was applied to all 48 recordings of the MIT-BIH arrhythmia database, where a percent root mean square difference (PRD) of 3.11 along with a compression ratio (CR) of 21.38:1 was achieved on average. The proposed scheme was also tested using raw multi-lead captures from the experimental device where an average PRD and CR of 9.77 and 24.95:1 was achieved respectively. The proposed HWT based compression scheme was efficiently implemented on a mobile platform and is capable of compressing multi-lead ECG signals, allowing for efficient data management in the context of data storage or data transmission.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.388
Threshold uncertainty score0.228

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 head0.024
GPT teacher head0.316
Teacher spread0.293 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

Quick stats

Citations3
Published2016
Admission routes1
Has abstractyes

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