Phonocardiographic Murmur Detection by Scattering-Recurrent Networks
Bibliographic record
Abstract
We describe an automatic detector of phonocardiogram murmurs.Our detector composes the scattering transform (ST) and a long short-term memory (LSTM) network.It is trained on data as part of the Heart Murmur Detection from Phonocardiogram Recordings: The George B. Moody PhysioNet Challenge 2022.The ST captures shortterm temporal ECG modulations while reducing its sampling rate to a few samples per typical heart beat.We pass the output of the ST to a depthwise-separable convolution layer which transforms responses separately for each ST coefficient and then combines resulting values across ST coefficients.At a deeper level, 2 LSTM layers integrate local variations of the input over long time scales.We train in an end-to-end fashion as a classification problem with three murmur classes: present, absent or unknown.Additionally, we use the model to classify clinical outcome as normal or abnormal.These two classifications determine whether clinical followup should occur.Our team "PAWPCG" obtained an official score on the hidden test data of 0.637 for weighted accuracy on murmur classification (rank: 27 of 40 teams) and a clinical outcome cost of 15083 (rank: 32 of 39 teams).
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 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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".