A unified framework for multi-lead ECG characterization using Laplacian Eigenmaps

BACKGROUND

The analysis of multi-lead electrocardiographic (ECG) signals requires integrating the information derived from each lead to reach clinically relevant conclusions. This analysis could benefit from methods compacting the information in those leads into lower-dimensional representations (i.e., 2 or 3 dimensions instead of 12).

OBJECTIVE

We propose Laplacian Eigenmaps (LE) to create a unified framework where ECGs from different subjects can be compared and their abnormalities are enhanced.

APPROACH

We conceive a normal reference ECG space based on LE, calculated using signals of healthy subjects in sinus rhythm. Signals from new subjects can be mapped onto this reference space creating a loop per heartbeat that captures ECG abnormalities. A set of parameters, based on distance metrics and on the shape of loops, are proposed to quantify the differences between subjects.

MAIN RESULTS

This methodology was applied to find structural and arrhythmogenic changes in the ECG. The LE framework consistently captured the characteristics of healthy ECGs, confirming that normal signals behaved similarly in the LE space. Significant differences between normal signals and those from patients with ischemic heart disease or dilated cardiomyopathy were detected. In contrast, LE biomarkers did not identify differences between patients with cardiomyopathy and a history of ventricular arrhythmia and their matched controls.

SIGNIFICANCE

This LE unified framework offers a new representation of multi-lead signals, reducing dimensionality while enhancing imperceptible abnormalities and enabling the comparison of signals of different subjects.

A comparative study of the performance of methods for f-wave extraction

Objective.This study proposes a novel technique for atrial fibrillatory waves (f-waves) extraction and investigates the performance of the proposed method comparing with different f-wave extraction methods.Approach.We propose a novel technique combining a periodic component analysis (PiCA) and echo state network (ESN) for f-waves extraction, denoted PiCA-ESN. PiCA-ESN benefits from the advantages of using both source separation and nonlinear adaptive filtering. PiCA-ESN is evaluated by comparing with other state-of-the-art approaches, which include template subtraction technique based on principal component analysis, spatiotemporal cancellation, nonlinear adaptive filtering using an echo state neural network, and a source separation technique based on PiCA. Quality assessment is performed on a recently published reference database including a large number of simulated ECG signals in atrial fibrillation (AF). The performance of the f-wave extraction methods is evaluated in terms of signal quality metrics (SNR, ΔSNR) and robustness of f-wave features.Main results.The proposed method offers the best signal quality performance, with a ΔSNR of approximately 22 dB across all 8 sets of the reference database, as well as the most robust extraction of f-wave features, with 75% of all estimates of dominant atrial frequency well below 1 Hz.

VPNET: Variable Projection Networks

In this paper, we introduce VPNet, a novel model-driven neural network architecture based on variable projection (VP). Applying VP operators to neural networks results in learnable features, interpretable parameters, and compact network structures. This paper discusses the motivation and mathematical background of VPNet and presents experiments. The VPNet approach was evaluated in the context of signal processing, where we classified a synthetic dataset and real electrocardiogram (ECG) signals. Compared to fully connected and one-dimensional convolutional networks, VPNet offers fast learning ability and good accuracy at a low computational cost of both training and inference. Based on these advantages and the promising results obtained, we anticipate a profound impact on the broader field of signal processing, in particular on classification, regression and clustering problems.

The effect of head-up tilt upon markers of heart rate variability in patients with atrial fibrillation

BACKGROUND

Heart rate variability (HRV) analysis is uncommonly undertaken in patients with atrial fibrillation (AF) due to an assumption that ventricular response is random. We sought to determine the effects of head-up tilt (HUT), a stimulus known to elicit an autonomic response, on HRV in patients with AF; we contrasted the findings with those of patients in sinus rhythm (SR).

METHODS

Consecutive, clinically indicated tilt tests were examined for 207 patients: 176 in SR, 31 in AF. Patients in AF were compared to an age-matched SR cohort (n = 69). Five minute windows immediately before and after tilting were analyzed using time-domain, frequency-domain and nonlinear HRV parameters. Continuous, noninvasive assessment of blood pressure, heart rate and stroke volume were available in the majority of patients.

RESULTS

There were significant differences at baseline in all HRV parameters between AF and age matched SR. HUT produced significant hemodynamic changes, regardless of cardiac rhythm. Coincident with these hemodynamic changes, patients in AF had a significant increase in median [quartile 1, 2] DFA-α2 (+0.14 [-0.03, 0.32], p < .005) and a decrease in sample entropy (-0.17 [-0.50, -0.01], p < .005).

CONCLUSION

In the SR cohort, increasing age was associated with fewer HRV changes on tilting. Patients with AF had blunted HRV responses to tilting, mirroring those seen in an age matched SR group. It is feasible to measure HRV in patients with AF and the changes observed on HUT are comparable to those seen in patients in sinus rhythm.

Broadband noise suppression and feature identification of ECG waveforms using mathematical morphology and embedding theorem

The paper presents an adaptive morphological filter developed using multiscale mathematical morphology (MM) to reject broadband noise from ECG signals without affecting the feature waveforms. As a pre-processing procedure, the adaptive morphological filter cleans an ECG signal to prepare it for further analysis. The noiseless ECG signal is embedded within a two-dimensional phase space to form a binary image and the identification of the feature waveforms is carried out based on the information presented by the image. The classification of the feature waveforms is implemented by an adaptive clustering technique according to the geometric information represented by the image in the phase space. Simulation studies on ECG records from the MIT-BIH and BIDMC databases have demonstrated the effectiveness and accuracy of the proposed methods.