Study of new criteria based on eigenvalue decomposition to assist arrhythmogenic cardiomyopathy diagnosis

Arrhythmogenic Cardiomyopathy (ACM) is a heritable cardiac disease causing sudden cardiac death in young people. Its clinical diagnosis includes major and minor criteria based on alterations of the electrocardiogram (ECG). The aim of this study is to propose and evaluate new criteria based on Principal Component Analysis (PCA) applied to 12-lead ECG signals and find differences between individuals affected and unaffected by ACM. Datasets consist of 12-lead ECG recordings from 32 patients diagnosed with ACM, and 37 relatives of those affected, but without gene mutation. We extracted 8 eigenvalues from each recording. Next, we proposed two criteria (C1 and C2) representing the percentage of energy in eigenvalues of non-dipolar and third principal components respectively. Results show that the most significant indicator was C1, with values of 0.026±0.015 in ACM patients and 0.014±0.009 in those not affected by ACM (p <10e−3). C2 has values of 0.050±0.035 and 0.028±0.023 (p <10e−2) in the same groups. The results of this study show significant differences in the indicators proposed between those affected by ACM and the control group, indicating that the degree of information contained in the non-dipolar leads is higher in the first group. All the above suggest an increased heterogeneity of the electrical activation in patients with ACM.