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Objective: Fragmented QRS (fQRS) is an accessible biomarker and indication of myocardial scarring that can be detected from the electrocardiogram (ECG). Nowadays, fQRS scoring is done on a visual basis, which is time consuming and leads to subjective results. This study proposes an automated method to detect and quantify fQRS in a continuous way using features extracted from variational mode decomposition (VMD) and phase-rectified signal averaging (PRSA). Methods: In the proposed framework, QRS complexes in the ECG signals were first segmented using VMD. Then, ten VMD- and PRSA-based features were computed and fed into well-known classifiers such as support vector machine (SVM), K-nearest neighbors (KNN), Naive Bayesian (NB), and TreeBagger (TB) in order to compare their performance. The proposed method was evaluated with 12-lead ECG data of 616 patients from the University Hospitals Leuven. The presence of fQRS in each ECG lead was scored by five raters. Both detection and quantification of fQRS could be achieved in this way. Results: The experimental results indicated that the proposed method achieved AUC values of 0.95, 0.94, 0.90, and 0.89 using SVM, KNN, NB, and TB classifiers, respectively, for detecting QRS fragmentation. Assessment of quantification performance was done by comparing the fQRS score with the total score, obtained by summing the scores from the individual raters. Results showed that the fQRS score clearly correlated with this estimate of fQRS certainty. Conclusion: The proposed method obtained good results in both fQRS detection and quantification, and is a novel way of assessing the certainty of QRS fragmentation in the ECG signal. [ABSTRACT FROM AUTHOR]

Background: Fragmented QRS (fQRS) on a 12-lead ECG has been linked with adverse outcome. However, the visual scoring of ECGs is prone to inter- and intra-observer variability.Methods: Five observers, two experienced and three novel, assessed fQRS in 712 digital ECGs, 100 were re-evaluated to assess intra-observer variability. Fleiss and Cohen's Kappa were calculated and compared between subgroups.Results: The inter-observer variability for assessing fQRS in all leads combined was substantial with a Kappa of 0.651. Experienced observers only had a better agreement with a Kappa of 0.823. Intra-observer variability ranged from 0.736 to 0.880. In the subgroup with ventricular pacing the inter-observer variability was even significantly larger when compared to ECGs with normal QRS duration (Kappa 0.493 vs 0.664, p<0.001).Conclusion: The visual assessment of QRS fragmentation is prone to inter- and intra-observer variability, mainly influenced by the experience of the observers, the underlying rhythm and QRS morphology. [ABSTRACT FROM AUTHOR]

Objectives: Longer-term electrocardiographic effects of multiple inappropriate ICD shocks were investigated to study their hypothesized pro-arrhythmic potential.Design: Thirteen male patients with ischemic cardiomyopathy who received ≥2 inappropriate shocks within 24 h and for whom 12-lead ECGs were available both before and within 72h after the inappropriate shocks were analyzed. Exclusion criteria included continuous ventricular pacing, underlying AF, events within 6 weeks after lead implantation and concomitant acute medical problems.Results: A total of 149 inappropriate shocks (mean 11 ± 19) were received. There were no significant differences in any of the measured intervals or morphological indices, nor was there a correlation between the "before-after" differences and the number of shocks received. Non-significant changes showed Percentage of Loop Area increase and relative T-wave Residuum decrease while the opposite changes have previously been associated with arrhythmic risk.Conclusions: No potentially pro-arrhythmic electrocardiographic changes were found 19 h after multiple inappropriate shocks. [ABSTRACT FROM AUTHOR]

Background: There are conflicting data on the influence of meal intake on the QT interval.Methods: Ten healthy subjects were studied before and after a standardized breakfast and lunch with a sequence of supine resting, standing and exercise. Data collection was performed using a 12-lead Holter with semi-automated analysis. QT correction was performed using Fridericia (QTcF) correction formula and a subject-specific method based on individual QT/RR-regression (QTcI).Results: Meal intake induced significant changes in HR (p<0.001), but not in QTcF (p=0.512) or QTcI (p=0.739). Postural analysis showed only significant differences in supine position for HR (p=0.010), not when standing or during exercise.Conclusion: Food intake induced an increase in heart rate limited to supine position. Using QTcF and QTcI no QTc changes were found. [ABSTRACT FROM AUTHOR]

Objective: T wave alternans (TWA) is a promising non-invasive risk stratification tool for sudden cardiac death which can be detected from surface ECG. This paper proposes a novel method to automatically detect TWA based on tensor decomposition methods., Approach: Two different tensor decomposition approaches are examined and compared, namely canonical polyadic decomposition and the more generalized variation PARAFAC2 which allows the T waves to shift in time., Results and Significance: Results on different artificial and clinical signals show that the presented methods are a robust and reliable way for TWA detection, and show the potential benefit of tensors in ECG signal processing.

Cardiac arrhythmia or irregular heartbeats are an important feature to assess the risk on sudden cardiac death and other cardiac disorders. Automatic classification of irregular heartbeats is therefore an important part of ECG analysis. We propose a tensor-based method for single- and multi-channel irregular heartbeat classification. The method tensorizes the ECG data matrix by segmenting each signal beat-by-beat and then stacking the result into a third-order tensor with dimensions channel × time × heartbeat. We use the multilinear singular value decomposition to model the obtained tensor. Next, we formulate the classification task as the computation of a Kronecker Product Equation. We apply our method on the INCART dataset, illustrating promising results.

Accurate R peak detection in the electrocardiogram (ECG) is a well-known and highly explored problem in biomedical signal processing. Although a lot of progress has been made in this area, current methods are still insufficient in the presence of extreme noise and/or artifacts such as loose electrodes. Often, however, not only the ECG is recorded, but multiple signals are simultaneously acquired from the patient. Several of these signals, such as blood pressure, can help to improve the heart beat detection. These signals of interest can be detected automatically by analyzing their power spectral density or by using the available signal type identifiers. Individual peaks from the signals of interest are combined using majority voting, heart beat location estimation and Hjorth's mobility of the resulting RR intervals. Both multimodal algorithms showed significant increases in performance of up to 8.65% for noisy multimodal datasets compared to when only the ECG signal is used. A maximal performance of 90.02% was obtained on the hidden test set of the Physionet/Computing in Cardiology Challenge 2014: Robust Detection of Heart Beats in Multimodal Data.

T wave alternans is defined as changes in the T wave amplitude in an ABABAB-pattern. It can be found in ECG signals of patients with heart diseases and is a possible indicator to predict the risk on sudden cardiac death. Due to its low amplitude, robust automatic T wave alternans detection is a difficult task. We present a new method to detect T wave alternans in multichannel ECG signals. The use of tensors (multidimensional matrices) permits the combination of the information present in different channels, making detection more reliable. The possibility of decomposition of incomplete tensors is exploited to deal with noisy ECG segments. Using a sliding window of 128 heartbeats, a tensor is constructed of the T waves of all channels. Canonical Polyadic Decomposition is applied to this tensor and the resulting loading vectors are examined for information about the T wave behavior in three dimensions. T wave alternans is detected using a sign change counting method that is able to extract both the T wave alternans length and magnitude. When applying this novel method to a database of patients with multiple positive T wave alternans tests using the clinically available spectral method tests, both the length and the magnitude of the detected T wave alternans is larger for these subjects than for subjects in a control group.