Classification of Heart Sounds Using Grey Level Co-occurrence Matrix and Logistic Regression
Bibliographic record
Abstract
The heart is a person's fundamental organ.Heart sounds can help support healthcare workers by aiding in the early diagnosis of irregular heart rhythms.This study developed a system to categorize heart sounds by employing logistic regression as the classifier and the grey-level co-occurrence matrix as the classifier.For this reason, the GLCM technique was assessed in this work for feature extraction in the heart sound categorization.Moreover, the diagnostic heart sound analysis and classification procedure can be greatly improved by visualizing heart sounds using the Grey Level Co-occurrence Matrix (GLCM).The three data classifications for heart sounds are artifact, murmurs, and normal.Moreover, the heart sound is converted into the timefrequency domain using the short-time Fourier transform (STFT).The gray-level cooccurrence matrix approach is a useful tool for extracting the energy distribution in STFT.Dissimilarity, correlation, homogeneity, contrast, energy, and angular second moment (ASM) are the characteristics of the GLCM extraction.With dissimilarity offering the most feature extraction, logistic regression yields an 82% classification accuracy.The AUC value of 0.7 for the murmur class indicated that the feature and classification model had reduced sensitivity, but it performed well for the normal and artifact classes.This is because there are too few datasets for the murmur class.More abnormal class datasets are hoped to be contributed in the future in order to improve the classifier model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".