A Novel Approach for Robust Detection of Heart Beats in Multimodal Data using Neural Networks and Boosted Trees
Bibliographic record
Abstract
This work describes a novel approach designed for Physionet 2014 Challenge, Robust Detection of Heart Beats in Multimodal Data [5].The objective here is to detect the location of R peaks from QRS complex of an electrocardiogram (ECG) excerpt.Robust detection of heart beats in a noisy ECG signal is an extremely difficult task.To overcome the challenge in such situations, besides ECG, blood pressure (BP) signal is also recorded at the same time; hence the idea here is that, if a segment of one of the signals is noisy, the peaks in that segment can be better estimated by peaks found in the corresponding segment of other signal, if good.The approach uses Machine Learning (ML) methods to identify locations of R-peaks in a given segment of ECG or BP signal.Peaks from both ECG and BP signal are found separately using a novel feature representation and subsequent ML approaches that renders R peaks in the signal, easier to be detected, by a simple windowing technique.Individually detected peaks, from both ECG and BP are further analyzed in chunks of equal short time periods, and the best result of the two is chosen in final peak prediction based on variance comparison techniques.The performance of system on the training dataset [11] provided in the competition is 99.95%.The performance on test datasets which are hidden for phase I, phase II and phase III of the competition respectively are 93.27%,90.28% and 89.74%.The submission resulted in 1 st place in all three phases of the competition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".