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T-wave alternans (TWA) is characterized by a pattern of beat-to-beat alternations in the amplitude of the T-wave of the electrocardiogram (ECG). However, TWA is usually very small and hard to detect or measure. Our aim was to develop and compare three automated TWA measurement algorithms. 100 ECG recordings provided by the 2008 PhysioNet/Computers in Cardiology Challenge were analysed. After ECG pre-processing`moving four-beat window technique (FBW), fast Fourier transform (FFT) spectral analysis and principal component analysis (PCA) were used for TWA determination and quantification. These estimates were then ranked from 1 to 100, separately for each method, and the correlation between the results of each method analysed. The correlation of the ranking of TWA estimates between FBW and FFT was 0.65, between FFT and PCA 0.33, and between FBW and PCA 0.24. With TWA criteria applied, the number of ECGs with TWA was for FBW 29, FFT 29, and PCA 38. Only 7 of the ECG records had TWA detected by all three techniques. In conclusion, we have shown that different analysis techniques produced substantially different results.