Detecting and quantifying T-wave alternans using the correlation method and comparison with the FFT-based method

In this study, we have introduced an open-source program that can be used for the detection and analysis of different waves in the ECG signal. The effect of noise is first reduced by applying an adaptive least-squares method to the signal using a sliding window. The maximums and minimums of the signal are determined, and the R-waves are then detected using a signal-slope test. Waves located between two consequent R-waves, are next classified based on their distance from the left R-wave. Then, using the hypothesis test the detected signal is divided into five equal segments from its peak to the base line. The presented program is capable of computing the arc length of each segment, calculating correlation coefficients and also performing other non-parametric tests. Correlation and FFT-based methods were finally applied to the TWA database of the CinC 2008 challenge and the results are represented.