Heart disease detection using ECG lead I and multiple pattern recognition classifiers

ECG is an important tool to assist in heart diseases diagnosis. The works found in the literature have the common goal of discriminating between binary study groups, one pathological and one control, even when ECG records from patients diagnosed with several pathologies are available in the databases. This work proposes a method to detect ECG morphological features and to analyze the capacity of this ECG features to discriminate 28 pairs of study groups, combining 7 pathological groups and 1 control group, presented in the PTB Diagnostic ECG Database. For each pair, it was achieved an accuracy between 77.4% and 100%, with an average of 94%, using several pattern recognition classifiers.