Aim: Brugada syndrome (BrS) is a heritable syndrome predisposing individuals to fatal arrhythmias, cardiogenic syncope and cardiac arrest. Mathematical modelling of QRS morphology combined with unsupervised clustering may identify distinct electrophysiological phenotypes and improve the understanding of conduction abnormalities in BrS.
Methods: We analyzed 24-hour high-precordial Holter ECG recordings from 118 BrS patients and 44 healthy controls. Median QRS complexes from consecutive 30-minute windows were mathematically modelled through four Hermite functions. The resulting morphological features (Hermite base width and the four coefficients) were spatially reduced by principal component analysis across leads and clustered using K-means. Cluster temporal stability was evaluated using cluster dominance, transition rate, entropy and feature variance.
Results: Clustering identified two QRS phenotypes in BrS. Cluster 2 showed slower conduction when quantified by the propagation progression time. Although, QRS-based clustering alone did not stratify risk (high-risk BrS in Cluster-1 27% vs Cluster-2 37%). When assigning healthy controls to clusters, they fitted into Cluster 1, except for one subject. Compared to BrS patients, controls presented a higher temporal stability with lower transition rate among clusters, lower entropy, and higher persistence. Notably, BrS patients presented the highest feature variance (1.74[0.76;3.34] vs 6.05[2.46;11.06], p-value <0.001), and thus an increased intra-subject morphological variability of the QRS-complex during the 24 hours.
Conclusion: The presented clustering strategy identified two QRS phenotypes in BrS syndrome, one corresponding with healthy QRS morphology and the other probably linked to QRS variability and transient changes potentially linked to type-I pattern manifestation. Although, QRS-based clustering alone did not stratify risk, indicating that integration with ST-segment and T-wave analysis may better capture phenotypic expression and support subgroup-specific modeling