A novel method for analysis of transient morphological changes in quasiperiodic physiological signals and their neurogenic correlates

TL;DR

A novel method for visualizing quasiperiodic signals, enabling the transformation of time series containing repetitive patterns into intuitive visual representations, and greatly enhancing the detection of subtle disturbances and a fascinating dynamic interplay between the rhythm and the morphology of the signal.

Abstract

Frequently, transient changes in physiological signals, such as ECG morphology, precede or follow a rate change. Current methods for visualizing morphology allow only the tracking of preselected changes, severely limiting analytical capabilities. We introduce a novel method for visualizing quasiperiodic signals, enabling the transformation of time series containing repetitive patterns into intuitive visual representations. By using segmentation algorithms and color encoding, we generate two-dimensional "carpet plots" that facilitate simultaneous assessment of heart rhythm and signal features, including the morphology of QRS complexes and T waves, as well as transient changes in intervals and amplitudes. Additionally, the method supports the assessment of concomitant changes in morphology and rate. Typically, existing visualization methods, such as the standard 12-lead ECG projection, focus either on rhythm variability or on morphological analysis of a few consecutive beats. In contrast, our method integrates both aspects into a single, coherent graphical representation, greatly enhancing the detection of subtle disturbances and a fascinating dynamic interplay between the rhythm and the morphology of the signal. We illustrate the effectiveness of this approach using Holter recordings from healthy individuals and patients with arrhythmias, as well as stress test sessions. The results highlight the potential of our visualization technique to support diagnosis and long-term ECG signal analysis. The method may be applied to a broad class of repeatable quasiperiodic patterns - we demonstrate a few examples.

Figures (6)

• Figure 1: Schematic of the carpet-plot generation procedure. (a) Original ECG signal with marked R peaks; (b) segmented cycles aligned by the central R peak; (c) amplitude-to-color mapping applied within each segment; (d) final "carpet plot" formed by stacking segments in chronological order.
• Figure 2: Examples of carpet plots for different ECG signals. (a) male, 19 years old, diagnosed with LQT1 (patient #174 from THEW database E-HOL-03-0480-013 Couderc2010); (b) patient #08405 from MIT-BIH Atrial Fibrillation Database Moody1983; (c) record I51 (lead II) from INCART database Goldberger2000; (d) record #36 of stress-test session from EPHNOGRAM database Kazemnejad2021
• Figure 3: Representative 20-second raw ECG signal segments corresponding to the carpet plots in Fig.\ref{['fig:carpets']}.
• Figure 4: Carpet plots of a patient diagnosed with 2nd degree AV nodal block (record I11 from INCART database). Comparison of two different color mappings: with a top and bottom half-percentile cutoff (left) and manual adjustment to focus on the amplitude range typical to both T wave and P wave (right). Wenckebach periodicity is clearly visible.
• Figure 5: Feature maps extracted from carpet plot of LQTS patient (Fig. \ref{['fig:carp1']}) using ResNet18 model (first 3 convolutional layers).