Riemannian Prediction of Anatomical Diagnoses in Congenital Heart
  Disease based on 12-lead ECGs
Muhammet Alkan Gruschen Veldtman Fani Deligianni
Congenital heart disease (CHD) is a relatively rare disease that affects
patients at birth and results in extremely heterogeneous anatomical and
functional defects. 12-lead ECG signal is routinely collected in CHD patients
because it provides significant biomarkers for disease prognosis. However,
developing accurate machine learning models is challenging due to the lack of
large available datasets. Here, we suggest exploiting the Riemannian geometry
of the spatial covariance structure of the ECG signal to improve
classification. Firstly, we use covariance augmentation to mix samples across
the Riemannian geodesic between corresponding classes. Secondly, we suggest to
project the covariance matrices to their respective class Riemannian mean to
enhance the quality of feature extraction via tangent space projection. We
perform several ablation experiments and demonstrate significant improvement
compared to traditional machine learning models and deep learning on ECG time
series data.