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

TL;DR

This paper introduces a new visualization method for quasiperiodic physiological signals that combines rhythm and morphological analysis into intuitive, comprehensive visual representations, improving detection of subtle changes and aiding diagnosis.

Contribution

The novel visualization technique transforms time series into carpet plots using segmentation and color encoding, enabling simultaneous assessment of rhythm and morphology in physiological signals.

Findings

Effective visualization of ECG morphology and rhythm changes

Enhanced detection of transient disturbances in signals

Demonstrated on Holter and stress test data

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.