ECG Signal Quality During Arrhythmia and Its Application to False Alarm Reduction

TLDR

Accurate ECG quality assessment requires a substantially larger labeled dataset. The study presents an automated algorithm to assess ECG quality for normal and abnormal rhythms to suppress false arrhythmia alarms in ICU monitors. Using 33,000 manually labeled and 12,000 synthetically generated ECG segments from three databases, the authors derived signal quality indices and trained a Gaussian‑kernel SVM to classify segment quality, then examined its relation to ICU alarm signals. The classifier achieved up to 99% accuracy for normal sinus rhythm and 95% for arrhythmias, but performance varied by rhythm, indicating that rhythm‑specific SQIs and per‑rhythm training are needed.

Abstract

An automated algorithm to assess electrocardiogram (ECG) quality for both normal and abnormal rhythms is presented for false arrhythmia alarm suppression of intensive care unit (ICU) monitors. A particular focus is given to the quality assessment of a wide variety of arrhythmias. Data from three databases were used: the Physionet Challenge 2011 dataset, the MIT-BIH arrhythmia database, and the MIMIC II database. The quality of more than 33 000 single-lead 10 s ECG segments were manually assessed and another 12 000 bad-quality single-lead ECG segments were generated using the Physionet noise stress test database. Signal quality indices (SQIs) were derived from the ECGs segments and used as the inputs to a support vector machine classifier with a Gaussian kernel. This classifier was trained to estimate the quality of an ECG segment. Classification accuracies of up to 99% on the training and test set were obtained for normal sinus rhythm and up to 95% for arrhythmias, although performance varied greatly depending on the type of rhythm. Additionally, the association between 4050 ICU alarms from the MIMIC II database and the signal quality, as evaluated by the classifier, was studied. Results suggest that the SQIs should be rhythm specific and that the classifier should be trained for each rhythm call independently. This would require a substantially increased set of labeled data in order to train an accurate algorithm.