Evaluation of a Continuous ECG Quality Indicator Based on the Autocorrelation Function

Abstract

Most electrocardiogram (ECG) signal quality assessment algorithms focus on a two- or multi-level classification. However, it could be argued that signal quality would more naturally occupy a continuum of quality values. Therefore, in previous work we created a continuous quality assessment algorithm based on the autocorrelation function (ACF). This paper evaluates this algorithm on a simulated dataset with five noise levels and known signal-to-noise ratios (SNR). The simulated data was created by selecting clean ECG segments of a polysomnographic dataset with an in-house quality algorithm, and adding calibrated amounts of two types of realistic ECG noise from the MIT-BIH Noise Stress Test Database (NSTDB). Both Electrode Motion (EM) and Movement Artefacts (MA) were considered. Using only three features and a binary training set, we have shown significant quality decreases per noise level for both types of added noise. Despite this finding, also significant intra-level differences were observed, indicating a change in response according to the type of noise. Adding other features might help to converge the quality scores. By presenting the users with a continuous quality score, they are given the possibility to define the preferred level of quality according to the study objective.