IntroductionDue to the disconnections between clinical scientists and applications of the available diagnosing methods, the current methods for detecting ischemic heart disease are not as well developed as therapeutic interventions despite the knowledge regarding the underlying pathophysiology. Thus, the 2007 Physionet/Computers in Cardiology Challenge embraces the disciplines of both electrocardiography and Magnetic Resonance Imaging (MRI). However in this work we propose the use of the 12 leads ECG signals to locate, according to the standard 17 segments [1], the infarcted segments, the centroid of the infarction, and the extent of the infarct. The results of the challenge are compared to gold standards taken from MRI with gadolinium enhanced images. A Reconstructed Phase Space (RPS)/ Gaussian Mixture Model (GMM) [2] signal modeling approach is used to identify the infarcted segments.The paper is organized in the following sections: background, data set, method, results, discussion and conclusion. BackgroundThe American Heart Association [3] shows how the 12 ordinary leads can be related back to infarction locations. The drawback in the provided rules is that they do not relate to the 17 segment locations provided in this challenge. In [1], de Luna and Wagner provided myocardial infarction localization rules that relate the Q wave, R wave, and S wave to the 17 wall segments using MRI.Several research groups have used ECG measurements to locate infarcts. These include the work by Reddy et al. In [4], they developed an approach based on QRS measurement and neural networks to classify healthy and patients with myocardial infarction. Their accuracy was 79% with a specificity of 97%.Lu et al's neural-fuzzy approach for classifying myocardial infarction uses ST elevation as an input parameter. Their approach identified whether a lead is infracted, and from the infarcted lead set inferred the infarct location. Their accuracy for detecting healthy patients was 89.4% and for detecting infarcted patients was 95.0% [5]. DatasetsThe challenge dataset consists of two training and two testing patient records. One record has a moderate to large infarct, while the second has a relatively compact infarct. This data is labor-intensive to produce. The Body Surface Potential Map data, consisting of ECG data for 352 torsosurface sites, is provided for an averaged PQRST complex signal sampled at 2kHz. The 12 ordinary leads and the Frank leads are also provided [6].However, this dataset is not sufficient for building a classification method. Therefore, we supplement the current challenge dataset with the PTB Diagnostic ECG Database, which provides a dataset of healthy and infarcted patients that is used as a training set for learner our classifier. The PTB database contains 549 records from 294 subjects. From the 549 records, 367 records taken from 148 patients had myocardial infarction [7]. MethodThe RPS/GMM approach has been applied previously in the detection of myocardial ischemia [2]. Here, we apply the RPS/GMM approac...

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