Deep learning on 3D ECG geometry predicts ischemia
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Background
Three-dimensional (3D) electrocardiography (ECG) is a recent methodological advance that extends the dimensionality of the standard ECG, enabling geometric descriptors that capture acute ischemia. Integrating these descriptors with deep learning (DL) may improve the discrimination between ischemic and non-ischemic states and promote the clinical translation of 3D ECG analysis.
Methods
ECGs from seventeen patients with acute left anterior descending (LAD) artery stenosis (>50 %) were obtained from the PTB Diagnostic ECG Database (PhysioNet). Pre- and post-catheterization recordings were analyzed in 2D and 3D (V3, V6, time) over the QRS end-T onset interval. Geometric descriptors included perimeter, curvature, three almost-curvature variants, and a newly defined torsion metric. Statistical analyses comprised univariate, bivariate, and multivariate tests (PERMANOVA), complemented by DL classification using a residual multilayer perceptron with patient-wise cross-validation, isotonic calibration, and logistic meta-blending, adopting a significance level of α = 0.01 (99 % confidence) to ensure inference stability given the limited sample size.
Results
Four descriptors changed significantly after revascularization ( P 2 D,V 6 t , κ 2 D,V 6 t , α 3 D ,2 , and τ ). Correlation analyses indicated redundancy among curvature-related metrics, whereas torsion provided independent information. PERMANOVA confirmed that torsion alone, and only metric sets including torsion, achieved significance ( p < 0.05). The torsion-based DL model provided the best discrimination, with an area under the ROC curve of 0.76 (99 % CI, 0.57-0.94; p < 0.001), specificity 0.82, and a Brier score of 0.18.
Conclusions
The integration of torsion into a DL-based 3D ECG framework enhanced the detection of acute ischemia, increasing diagnostic specificity and improving early triage and clinical decision-making in acute cardiac care.
