A New Data-Reduction Algorithm for Real-Time ECG Analysis

Abstract

Typically the ECG is sampled at a rate of 200 samples/s or more, producing a large amount of data that are difficult to store, analyze, and transmit. Data-reduction algorithms that operate in real time reduce he amount of data without losing the clinical information content. They must also leave sufficient computation time available for ECG analysis. We describe here a new algorithm called CORTES that is suited for such real-time applications. This algorithm combines the best features of two other techniques called TP and AZTEC. We present the results of a study to find optimal experimental values for the controlling variables in CORTES. We compare the computations of root-mean-square reconstruction errors for a diversity of encoded ECG signals.