Classification of phonocardiogram signals to detect heart murmurs
N8 Digital Health Community Day
What are phonocardiogram signals?
Data
5,272 sound recordings of heartbeats.
1,568 different patients.
4 recording locations.
Patient information such as height, weight, age, …, and whether or not they had been diagnosed with a heart murmur.
1J. H. Oliveira et al. (2021). The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification. IEEE Journal of Biomedical and Health Informatics.
Why classify phonocardiogram signals?
Aim: predict which time series of recordings belong to those with heart murmurs.
- Additional diagnostic tool
- can pick up sub-audible sounds
- cost-effective, first-line screening
- Longer term monitoring
Classifying time series
Calculate some features of the time series.
Use the features as input to classification algorithms instead of the raw time series data.
Time series features
Some time series features will tell us useful things…
… some won’t.
Initial results
Lasso logisitic regression
Accuracy: 0.81
ROC AUC: 0.65
Random forest
Accuracy: 0.80
ROC AUC: 0.71
Future work
Issues with bias
Multinomial classification
Exploiting location information
Feature engineering
