A Novel Approach for the Detection of Cardiovascular Abnormalities from Electrocardiogram and Phonocardiogram Signals Using Combined CNN-LSTM Techniques
Bibliographic record
Abstract
Heart diseases account for 30 percent of the fatalities worldwide.Early intervention and detection of cardiovascular abnormalities can prevent such fatalities.The current research proposes a novel approach combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for the prediction of abnormalities in the functioning of the human heart.The machine learning model is used to detect abnormalities from ECG and PCG signals.Two prominent datasets namely Physionet 2016 and Physionet 2017 have been used in this research for training and testing the developed machine learning model.Empirical Mode Decomposition has been used for preprocessing the heart sound signals and ECG signals.A signal can be broken down into its fundamental oscillatory components, known as intrinsic mode functions (IMFs), using EMD.By comparing the signal to noise ratio value to the raw and filtered PCG signal, one may evaluate the method's effectiveness in reducing noise.Feature extraction is done through the generation of Scalograms of the denoised signals.The scalogram is obtained by continuous wavelet transform (CWT).After this, a hybrid deep learning technique called CNN-LSTM is used for classifying and training the model.The proposed model renders an accuracy of 86% in terms of classifying and detecting abnormalities in the functioning of the human heart.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".