A time domain based classifier for ECG pattern recognition

Pattern recognition, and in particular dynamic time warping has been applied to the ECG for many different purposes over the last decade. Significant research on creating adaptive, feature based, and more complex forms of the algorithm in order to increase its ability to classify an ECG signal accurately has been performed. Despite this increase in complexity and in the number of variations of the dynamic time warping algorithm there has been less focus on actually using the results of dynamic time warping to relate the reference and test signals to each other as accurately as possible. The majority of dynamic time warping algorithms published in the literature, even the most complex of them, classify the most accurate match to a reference signal based only on resulting Euclidean distance or slope difference between samples of the known reference and unknown query signal. This article demonstrates how a combination of measurements including heart-rate, amplitude and required warping time alignment can be used to reduce the resulting error in the classification of a query signal after the query and reference signals have been warped together. Its benefits are verified with significant testing.