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Record W2594690106 · doi:10.22489/cinc.2016.172-318

Heart Sound Classification from Wavelet Decomposed Signal Using Morphological and Statistical Features

2016· article· en· W2594690106 on OpenAlexaff
Tamanna T. K. Munia, Kouhyar Tavakolian, Ajay Verma, Vahid Zakeri, Farzad Khosrow-Khavar, Reza Fazel-Rezai, Alireza Akhbardeh

Bibliographic record

VenueComputing in cardiology · 2016
Typearticle
Languageen
FieldMedicine
TopicPhonocardiography and Auscultation Techniques
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
Fundersnot available
KeywordsWaveletComputer sciencePattern recognition (psychology)Speech recognitionSound (geography)Artificial intelligenceSIGNAL (programming language)Wavelet transformAcousticsPhysics

Abstract

fetched live from OpenAlex

PhysioNet. Overall classification accuracies of 82% during Phase I submissions and 77% during Phase II submissions were achieved of the challenge. The final score on the blind test set was 74.65%. Based on the current result, the proposed prototype could be a potential solution for a robust and automatic classification technique of normal and abnormal heart sound recordings.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score0.330

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 head0.050
GPT teacher head0.335
Teacher spread0.285 · 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 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

Citations14
Published2016
Admission routes1
Has abstractyes

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