MétaCan
Menu
Back to cohort
Record W1845623949 · doi:10.1109/tfsa.1998.721365

Wavelet based bank of correlators approach for phonocardiogram signal classification

2002· article· en· W1845623949 on OpenAlexaff
Sreeraman Rajan, R. Doraiswami, Rob Stevenson, Raymond L. Watrous

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicPhonocardiography and Auscultation Techniques
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsPhonocardiogramWaveletMorlet waveletFilter bankComputer scienceSIGNAL (programming language)Pattern recognition (psychology)Artificial intelligenceWavelet transformFilter (signal processing)Speech recognitionDiscrete wavelet transformComputer vision

Abstract

fetched live from OpenAlex

This paper presents a methodology for detecting all the components of the phonocardiogram (PCG) signal based on a time-scale map obtained from a proposed wavelet based bank of correlators, without the aid of any additional reference signal to provide cardiac phase information. The cardiac phase information is obtained during the classification phase. The novel approach, namely the wavelet based bank of correlators approach uses the Morlet wavelets as the correlating filters. In this paper, the rationale for the choice of Morlet wavelets as the correlating filter is also developed. A complete scheme for detection and classification of PCG signals based on a simple perceptron is also presented.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.879
Threshold uncertainty score0.401

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 head0.047
GPT teacher head0.269
Teacher spread0.222 · 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 designSimulation or modeling
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

Citations32
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicPhonocardiography and Auscultation TechniquesFrench-language works237,207