4th Order Shannon Energy Envelope Approach for Localization of S1 and S2 for Early-Stage Detection of Heart Valve Dysfunction
Bibliographic record
Abstract
The recent reports of the World Health Organization showed that a huge extent of the population below 55 years has become much prone to cardiac disease, and the death percentage has increased caused by various cardio vascular diseases (CVD).Moreover, in the Covid-19 pandemic situation, the people suffering from heart disease were found severely vulnerable to viral infections, which proved to be a major cause of increased death percentage.The CVD could be caused by dysfunction of heart valves which could end up with cardiac arrest.The prime method for early-stage detection of the heart valve dysfunction is analysis of major heart sounds occurring in a cardiac cycle.The proposed work dealt with exploration of S1 and S2, which are supposed to be prime sounds of Phonocardiogram (PCG) signal.Here, the proposed analysis has six steps.First, signalacquisition set-up which was assembled for acquiring PCG and ECG signals from the people having age between 15 to 40 years.Second step, pre-processing: in which the samples of PCG and ECG signals were prepared and the signal was denoised using modified Butterworth worth filter.The third step was the incorporation of Empirical Mode Decomposition to get Intrinsic Mode Functions i.e., frequency components of the PCG.Further, only two appropriate IMFs were selected and recombined to generate a combined component signal (CChs).In the fourth step; a Modified Shannon Energy Envelope algorithm (MSEE) i.e., 4th order Shannon energy Envelope was implemented to frame energy envelopes.In the fifth step; an adaptive-thresholding was used for the time-lobes formation followed by peak correction algorithm i.e., correction of time-lobe peaks.In the sixth and final step; time-lobes of the PCG signal were computed and were correlated with R-peaks of ECG signal, through which localization of S1 and S2 was done.A total of 40 samples of the PCG signal consisting of 195 cardiac cycles were taken for the analysis.It came out from the analysis of the self-acquired PCG signal that the best result of localization of S1 and S2 is obtained for the PCG signal acquired from the Pulmonic position.After analyzing the confusion matrix for the findings of the proposed method; accuracy & precision were 90.20%, sensitivity 100%, and an error rate of 9.8% was obtained.The accuracy of the method was found lesser if the PCG was acquired from the remaining three auscultation areas of the human chest.The proposed method was compared with three other earlier algorithms, out of which the proposed method showed a greater improvement.Moreover, the implementation of EMD followed by choosing a few specific IMFs for the formation energy envelope reduced the computation cost and enhanced the accuracy of the method, too.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".