@MASTERSTHESIS\{IMM2013-06536,
    author       = "A. Seliger and L. B. Hansen",
    title        = "Characterization and Discrimination of Pathological Electrocardiograms using Advanced Machine Learning Methods",
    year         = "2013",
    school       = "Technical University of Denmark, {DTU} Compute, {E-}mail: compute@compute.dtu.dk",
    address      = "Matematiktorvet, Building 303{-B,} {DK-}2800 Kgs. Lyngby, Denmark",
    type         = "",
    note         = "{DTU} supervisor: Ole Winther, olwi@dtu.dk, {DTU} Compute",
    url          = "http://www.compute.dtu.dk/English.aspx",
    abstract     = "Cardiac arrhythmia and other heart related conditions are potentially life-threatening, making fast and accurate diagnosis vital. This thesis describes an approach to characterize and discriminate ECGs by applying machine learning methods. The investigation concerns the discrimination of subjects suffering from the inherited genetic disorder Long {QT} type 2 (LQT2) from a normal population. Applying 10-second raw ECGs as input, various hidden Markov models are trained for each group. The generative properties of the models are assessed and the log-likelihoods of the test ECGs are applied in an initial classification scheme. Further, the Support Vector Machine is included to improve the classification using the log-likelihoods of multiple hidden Markov models.
{ECG} simulations from the trained hidden Markov models produced recognizable waveforms and some of the expected morphological changes, seen in LQT2 subjects, were observable in the simulated ECGs. The best classification result observed was a classification accuracy of 78.1\% with a corresponding specificity of 78.2\% and a sensitivity of 78.2\%. Experience showed, however, that biological noise and power line interference in the {ECG} affected the classification, but it appears that the application of hidden Markov models using raw {ECG} data is well suited for the purpose of {ECG} characterization and discrimination."
}