Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms

Abstract

A completely automated algorithm to detect poor-quality electrocardiograms (ECGs) is described. The algorithm is based on both novel and previously published signal quality metrics, originally designed for intensive care monitoring. The algorithms have been adapted for use on short (5-10 s) single- and multi-lead ECGs. The metrics quantify spectral energy distribution, higher order moments and inter-channel and inter-algorithm agreement. Seven metrics were calculated for each channel (84 features in all) and presented to either a multi-layer perceptron artificial neural network or a support vector machine (SVM) for training on a multiple-annotator labelled and adjudicated training dataset. A single-lead version of the algorithm was also developed in a similar manner. Data were drawn from the PhysioNet Challenge 2011 dataset where binary labels were available, on 1500 12-lead ECGs indicating whether the entire recording was acceptable or unacceptable for clinical interpretation. We re-annotated all the leads in both the training set (1000 labelled ECGs) and test dataset (500 12-lead ECGs where labels were not publicly available) using two independent annotators, and a third for adjudication of differences. We found that low-quality data accounted for only 16% of the ECG leads. To balance the classes (between high and low quality), we created extra noisy data samples by adding noise from PhysioNet's noise stress test database to some of the clean 12-lead ECGs. No data were shared between training and test sets. A classification accuracy of 98% on the training data and 97% on the test data were achieved. Upon inspection, incorrectly classified data were found to be borderline cases which could be classified either way. If these cases were more consistently labelled, we expect our approach to achieve an accuracy closer to 100%.

Adaptive motion artefact reduction in respiration and ECG signals for wearable healthcare monitoring systems

“…Recently, the concept of signal quality index has been proposed and used to evaluate signal quality for signals acquired in intensive care unit and ambulatory monitoring [4,19]. Chen et al [3] developed a method to estimate respiratory waveform quality based on breath detection over varying baseline values.…”

“…For WHMSs, signal quality index is a useful tool to evaluate the quality of the waveforms acquired in ambulatory conditions. The concept of signal quality index was initially developed to quantify the physiological signal quality in intensive care unit to reduce false alarm rate and to improve the estimation of heart rate and blood pressure [4,19]. Currently, most of the studies are focused on the ECG signal quality evaluation.…”

Signal Quality Assessment of F-waves in Atrial Fibrillation

“…This is of particular importance when working with ambulatory or continuous long-term ECG recordings, which are commonly corrupted by the artifacts caused by muscle activity or moving electrodes. Several indices have been proposed for assessing the quality of ECG signals, including the relative power of baseline variation, signal kurtosis, and the ratio of the number of beats detected by two different QRS detectors, of which one detector is tuned to be more sensitive to noise than the other [1,2]. These SQIs are wellsuited for assessment of overall signal quality, where the aim is to exclude bad-quality segments from further analysis.…”

Performance Analysis of Multiscale Entropy for the Assessment of ECG Signal Quality

“…Thus there is an essential requirement to assess quality of ECG in order to determine whether the ECG can be used for clinical purpose. Recently, ECG quality assessment has been a focus issue [2][3][4][5][6][7][8].…”

“…Frequency based methods can use, for example, the ratio between the low-and the highfrequency content of the signals [6]. A combination of time and frequency methods has also been proposed [7,8]. The traditional wave form features and power spectrum are relatively simple metrics for assessing quality of ECG; however it is difficult to further improve classification accuracy.…”