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Santiago Jiménez-Serrano, Jorge Sanz Sánchez, Carlos D. Martínez-Hinarejos, B, Igual Muñoz, José Millet Roig, E. Zorio Grima, F. Castells. A first approach to Arrhythmogenic Cardiomyopathy detection through ECG and Hidden Markov Models. XXXIV Congreso Anual de la Sociedad Española de Ingeniería Biomédica, 2016. pp. 38-41.

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 evaluate Hidden Markov Models (HMM) in order to assess its possible potential of classification among subjects affected by ACM and those relatives who do not suffer the disease through 12-lead ECG recordings. Database consists of 12-lead ECG recordings from 32 patients diagnosed with ACM, and 37 relatives of those affected, but without gene mutation. Using the HTK toolkit and a hold-out strategy in order to train and evaluate a set of HMM models, we performed a grid search through the number of states and Gaussians across these HMM models. Results show that two different HMM models achieved the best balance between sensibility and specificity. The first one needed 35 states and 2 Gaussians and its performance was 0.7 and 0.8 in sensibility and specificity respectively. The second one achieved a sensibility and specificity values of 0.8 and 0.7 respectively with 50 states and 4 Gaussians. The results of this study show that HMM models can achieve an acceptable level of sensibility and specificity in the classification among ECG registers between those affected by ACM and the control group. All the above suggest that this approach could help to detect the disease in a non-invasive way, especially within the context of family screening, improving sensitivity in detection by ECG.