Automatic noise detection for ambulatory electrocardiogram in presence of ventricular arrhythmias through a machine learning approach

Noise detection in ambulatory electrocardiography is investigated as a machine learning binary classification problem on a set of twelve noise indices. Ten of these noise indices are replicated from relevant scientific literature. Two novel noise indices are also introduced: the electrocardiogram derivative pattern similarity index (edp) and the number of inversions of the signal's slope over a minimum amplitude threshold (inv). Noise indices were computed on ex-novo labeled data, which included ventricular arrhythmias such as ventricular tachycardia, flutter, and fibrillation. Signal quality labels were assigned on windows of 2 seconds, from which a set of labels on 10-second windows was also created. Six classification models were trained on 2 s and 10 s window data with a multi-stage optimization procedure. A feature selection stage was performed for each model, probing the usefulness of different noise indices, including noise and ventricular arrhythmias. Hyperparameter selection and model assessment were conducted through nested cross-validation. Final test performance metrics reached values as high as Matthew's correlation coefficient of 0.854 and balanced accuracy of 0.923. Particular attention was placed on the correct quality classification of records with ventricular arrhythmias, and test specificity on records with ventricular arrhythmias was as high as 0.976. This study provided new insights into the role of different noise indices in a challenging context with ventricular arrhythmias.