Detecting instabilities of cardiac rhythm

Personalized ECG monitoring and adaptive machine learning

This non-technical review introduces key concepts in personalized ECG monitoring (pECG), which aims to optimize the detection of clinical events and their warning signs as well as the selection of alarm thresholds. We review several pECG methods, including anomaly detection and adaptive machine learning (ML), in which learning is performed sequentially as new data are collected. We describe a distributed-network multiscale pECG system to show how the computational load and time associated with adaptive ML could be optimized. In this architecture, the limited analysis of ECG waveforms is performed locally (e.g., on a smart phone) to determine a small number of clinically important ECG elements, and an adaptive ML engine is located on a remote server (Internet cloud) to determine an individual's "fingerprint" basis patterns and to detect anomalies in those patterns.

Understanding the Beat-to-Beat Variations of P-Waves Morphologies in AF Patients During Sinus Rhythm: A Scoping Review of the Atrial Simulation Studies

The remarkable advances in high-performance computing and the resulting increase of the computational power have the potential to leverage computational cardiology toward improving our understanding of the pathophysiological mechanisms of arrhythmias, such as Atrial Fibrillation (AF). In AF, a complex interaction between various triggers and the atrial substrate is considered to be the leading cause of AF initiation and perpetuation. In electrocardiography (ECG), P-wave is supposed to reflect atrial depolarization. It has been found that even during sinus rhythm (SR), multiple P-wave morphologies are present in AF patients with a history of AF, suggesting a higher dispersion of the conduction route in this population. In this scoping review, we focused on the mechanisms which modify the electrical substrate of the atria in AF patients, while investigating the existence of computational models that simulate the propagation of the electrical signal through different routes. The adopted review methodology is based on a structured analytical framework which includes the extraction of the keywords based on an initial limited bibliographic search, the extensive literature search and finally the identification of relevant articles based on the reference list of the studies. The leading mechanisms identified were classified according to their scale, spanning from mechanisms in the cell, tissue or organ level, and the produced outputs. The computational modeling approaches for each of the factors that influence the initiation and the perpetuation of AF are presented here to provide a clear overview of the existing literature. Several levels of categorization were adopted while the studies which aim to translate their findings to ECG phenotyping are highlighted. The results denote the availability of multiple models, which are appropriate under specific conditions. However, the consideration of complex scenarios taking into account multiple spatiotemporal scales, personalization of electrophysiological and anatomical models and the reproducibility in terms of ECG phenotyping has only partially been tackled so far.

Nighttime instabilities of neurophysiological, cardiovascular, and respiratory activity: integrative modeling and preliminary results

Unstable (cyclical alternating pattern, or CAP) sleep is associated with surges of sympathetic nervous system activity, increased blood pressure and vasoconstriction, heightened baroreflex sensitivity, and unstable heart rhythm and breathing. In susceptible persons, CAP sleep provokes clinically significant events, including hypertensive crises, sleep-disordered breathing, and cardiac arrhythmias. Here we explore the neurophysiology of CAP sleep and its impact on cardiovascular and respiratory functions. We show that: (i) an increase in neurophysiological recovery rate can explain the emergence of slow, self-sustained, hypersynchronized A1 CAP-sleep pattern and its transition to the faster A2-A3 CAP-sleep patterns; (ii) in a two-dimensional, continuous model of cardiac tissue with heterogeneous action potential duration (APD) distribution, heart rate accelerations during CAP sleep may encounter incompletely recovered electrical excitability in cell clusters with longer APD. If the interaction between short cycle length and incomplete, spatially heterogeneous repolarization persists over multiple cycles, irregularities and asymmetry of depolarization front may accumulate and ultimately lead to a conduction block, retrograde conduction, breakup of activation waves, reentrant activity, and arrhythmias; and (iii) these modeling results are consistent with the nighttime data obtained from patients with structural heart disease (N=13) that show clusters of atrial and ventricular premature beats occurring during the periods of unstable heart rhythm and respiration that accompany CAP sleep. In these patients, CAP sleep is also accompanied by delayed adaptation of QT intervals and T-wave alternans.

Exported oscillator competition: a concept to analyze complex rhythms

We study the interaction between two independent nonlinear oscillators competing through a neutral excitable element. The first oscillator, completely deterministic, acts as a normal pacemaker sending pulses to the neutral element which fires when it is excited by these pulses. The second oscillator, endowed with some randomness, though unable to make the excitable element to beat, leads to the occasional suppression of its firing. The missing beats or errors are registered and their statistics analyzed in terms of the noise intensity and the periods of both oscillators. This study is inspired in some complex rhythms such as a particular class of heart arrhythmia.