Assessment of different methods to estimate electrocardiogram signal quality

During the process of measurement, the ECG signal suffers from several noises, artifacts and interferences, which reduce its quality. Automatic signal quality estimation could permit indentify when the level of noise is high to avoid wrong ECG interpretation. With this aim, several methods have been proposed in the literature. This work assesses eleven different methods for estimation of ECG signal quality available in literature. In addition, three new methods are proposed. These methods were evaluated in a simulated database containing ECGs with different types and levels of noise with SNR values ranging from -20 to 20 dB. From all the quality estimators studied, one novel parameter: Kurtosis, gave the best performance overall the tests. It gave high correlation with the signal SNR (0.95±0.00) and high correlation with the output of a beat detector (Positive Predictivity=0.97±0.00) and high resolution in time (10 seconds of signal length). However kurtosis did not have a high dynamic range. Some methods require some knowledge about the ECG signal (like the position of the R peak) and therefore are not suitable for applications with high levels of noise.