Papers › Phonocardiogram Classification Using 1-Dimensional Inception Time Convolutional Neural Networks
Phonocardiogram Classification Using 1-Dimensional Inception Time Convolutional Neural Networks
Bjørn-Jostein Singstad, Antony M. Gitau, Markus Kreutzer Johnsen, Johan Ravn, Lars Ailo Bongo, Henrik Schirmer
Murmurs are sounds caused by turbulent blood flow that are often the first sign of structural heart disease. These sounds are detected by auscultating the heart using a stethoscope, or more recently by a phonocardiogram (PCG). We aim to identify the presence, absence, or unclear cases of murmurs, as well as predict normal or abnormal clinical outcome from PCG recordings using machine learning. We trained and tested two 1-dimensional convolutional neural networks (CNN) on a PCG data set from a pediatric population of 1568 individuals. One model predicted murmurs, while the other model predicted clinical outcomes. Both models were trained to give recording-wise predictions, while the final predictions were given for every patient (patient-wise predictions). This paper describes our participation in the George B. Moody PhysioNet Challenge 2022 whose objective was to identify heart murmurs and clinical outcome from PCGs. Our team, Simulab, trained a clinical outcome classifier that achieved a challenge cost score of 12419 (ranked 14th out of 39 teams) and the murmur classifier achieved a weighted accuracy of 0.593 (ranked 30th out of 40 teams) on the test set.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Classify murmurs | CirCor DigiScope | Inception Time | Weighted Accuracy | 0.593 | #3 of 3 | Archive leaderboard | report |
| Classify murmurs | CirCor DigiScope | Inception Time | Weighted accuracy (cross-val) | 0.497±0.083 | #3 of 3 | Archive leaderboard | report |
| Classify murmurs | CirCor DigiScope | Inception Time | Weighted accuracy (validation) | 0.522 | #3 of 3 | Archive leaderboard | report |
| Predict clinical outcome | CirCor DigiScope | Inception Time | Clinical cost score | 12419 | #1 of 1 | Archive leaderboard | report |
| Predict clinical outcome | CirCor DigiScope | Inception Time | Clinical cost score (cross-val) | 12315±903 | #1 of 1 | Archive leaderboard | report |
| Predict clinical outcome | CirCor DigiScope | Inception Time | Clinical cost score (validation data) | 8720 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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