New methods for the analysis of heartbeat behavior in risk stratification

The Movesense Medical Sensor Chest Belt Device as Single Channel ECG for RR Interval Detection and HRV Analysis during Resting State and Incremental Exercise: A Cross-Sectional Validation Study

The value of heart rate variability (HRV) in the fields of health, disease, and exercise science has been established through numerous investigations. The typical mobile-based HRV device simply records interbeat intervals, without differentiation between noise or arrythmia as can be done with an electrocardiogram (ECG). The intent of this report is to validate a new single channel ECG device, the Movesense Medical sensor, against a conventional 12 channel ECG. A heterogeneous group of 21 participants performed an incremental cycling ramp to failure with measurements of HRV, before (PRE), during (EX), and after (POST). Results showed excellent correlations between devices for linear indexes with Pearson's r between 0.98 to 1.0 for meanRR, SDNN, RMSSD, and 0.95 to 0.97 for the non-linear index DFA a1 during PRE, EX, and POST. There was no significant difference in device specific meanRR during PRE and POST. Bland-Altman analysis showed high agreement between devices (PRE and POST: meanRR bias of 0.0 and 0.4 ms, LOA of 1.9 to -1.8 ms and 2.3 to -1.5; EX: meanRR bias of 11.2 to 6.0 ms; LOA of 29.8 to -7.4 ms during low intensity exercise and 8.5 to 3.5 ms during high intensity exercise). The Movesense Medical device can be used in lieu of a reference ECG for the calculation of HRV with the potential to differentiate noise from atrial fibrillation and represents a significant advance in both a HR and HRV recording device in a chest belt form factor for lab-based or remote field-application.

Ectopic cycle length estimation from the quantified distribution patterns of ventricular bigeminy and trigeminy

Background

Ectopic cycle length (ECL) and the distribution patterns of ventricular bigeminy and trigeminy, expressed as their postextrasystolic intervals (PEIs) and interectopic intervals (IEIs), have been poorly pursued.

Objective

Based on modulation theory, we hypothesized that the PEIs of bigeminy and trigeminy determine their IEIs and ECL.

Methods

Ambulatory electrocardiograms of 1290 patients with ventricular premature complexes (≥3000/day) were studied. To quantify their distribution pattern on the PEI vs IEI curve (PIC), we introduced the following 2 ratios: PEI of trigeminy to PEI of bigeminy ratio (T/B-PEI) and IEI of trigeminy to IEI of bigeminy ratio (T/B-IEI). Distribution patterns were divided into 3 types by T/B-PEI: standard type (<0.90), intermediate type (between 0.90 and 1.20), and reverse type (>1.20). ECL was defined as the average of the bigeminy and trigeminy intervals in the standard type, and bigeminy intervals in the other 2 types.

Results

T/B-IEI disclosed significant linear relationship with T/B-PEI (P < .0001). ECLs were longest in the standard type (1905 ± 347 ms; n = 426), followed by the intermediate type (1520 ± 239 ms; n = 607) and reverse type (1317 ± 227 ms; n = 227) (P < .0001). Trigeminy PEI/ECL in the standard type (0.450 ± 0.074) was significantly shorter than that of the other 2 types (P < .0001).

Conclusion

We confirmed that T/B-PEI determines T/B-IEI and ECL by discriminating the 3 distribution patterns. Among them, trigeminy PEI/ECL decided the 2 types of modulation by the first sinus QRS, starting at the early delay phase or the later acceleration phase.

Predicting the risk of sudden cardiac death

Sudden cardiac death (SCD) is the result of a change of cardiac activity from normal (typically sinus) rhythm to a rhythm that does not pump adequate blood to the brain. The most common rhythms leading to SCD are ventricular tachycardia (VT) or ventricular fibrillation (VF). These result from an accelerated ventricular pacemaker or ventricular reentrant waves. Despite significant efforts to develop accurate predictors for the risk of SCD, current methods for risk stratification still need to be improved. In this article we briefly review current approaches to risk stratification. Then we discuss the mathematical basis for dynamical transitions (called bifurcations) that may lead to VT and VF. One mechanism for transition to VT or VF involves a perturbation by a premature ventricular complex (PVC) during sinus rhythm. We describe the main mechanisms of PVCs (reentry, independent pacemakers and abnormal depolarizations). An emerging approach to risk stratification for SCD involves the development of individualized dynamical models of a patient based on measured anatomy and physiology. Careful analysis and modelling of dynamics of ventricular arrhythmia on an individual basis will be essential in order to improve risk stratification for SCD and to lay a foundation for personalized (precision) medicine in cardiology.

Increased variability of the coupling interval of premature ventricular contractions as a predictor of cardiac mortality in patients with left ventricular dysfunction

BACKGROUND

The characteristics and prognostic value of the variability of premature ventricular contraction (PVC) coupling intervals (CIs) for cardiac mortality are not yet decisive.

METHODS AND RESULTS

In 133 consecutive patients (58±14 years old, 53 women) who had left ventricular dysfunction (LVD: ejection fraction <50%) and frequent PVCs (≥10/h) who underwent 24-h ambulatory electrocardiography (AECG) recording and (123)I-metaiodobenzylguanidine myocardial single-photon emission computed tomography simultaneously, the heart rate turbulence onset, slope, and T-wave alternans were analyzed from the 24-h AECG. The CI of the PVCs (MEANNV), standard deviation of the CI of the PVCs (SDNV) as an index of the variability of the PVC CI, and their ratio to the preceding N-N intervals (SDNV/SDNN) were calculated from constructed Poincaré plots using the annotated 24-h AECG QRS data. The primary endpoint was cardiac mortality. The mean follow-up period was 63 months. Among 133 patients, 114 survived (group 1) and 19 (14%, group 2) died during the follow-up. The MEANNVand SDNVwere higher in group 2 (539±104 vs. 599±114 ms, P=0.021; 64±34 vs. 83±37 ms, P=0.022, respectively). The SDNV, PVC count, and delayed heart/mediastinum ratio remained as significant predictors of cardiac mortality in the binary logistic regression analysis.

CONCLUSIONS

These results suggest that the SDNVcould be another adjunctive parameter for predicting cardiac mortality in LVD.

Dynamical disease: Challenges for nonlinear dynamics and medicine

Dynamical disease refers to illnesses that are associated with striking changes in the dynamics of some bodily function. There is a large literature in mathematics and physics which proposes mathematical models for the physiological systems and carries out analyses of the properties of these models using nonlinear dynamics concepts involving analyses of the stability and bifurcations of attractors. This paper discusses how these concepts can be applied to medicine.

Pacemaker interactions induce reentrant wave dynamics in engineered cardiac culture

Pacemaker interactions can lead to complex wave dynamics seen in certain types of cardiac arrhythmias. We use experimental and mathematical models of pacemakers in heterogeneous excitable media to investigate how pacemaker interactions can be a mechanism for wave break and reentrant wave dynamics. Embryonic chick ventricular cells are cultured in vitro so as to create a dominant central pacemaker site that entrains other pacemakers in the medium. Exposure of those cultures to a potassium channel blocker, E-4031, leads to emergence of peripheral pacemakers that compete with each other and with the central pacemaker. Waves emitted by faster pacemakers break up over the slower pacemaker to form reentrant waves. Similar dynamics are observed in a modified FitzHugh-Nagumo model of heterogeneous excitable media with two distinct sites of pacemaking. These findings elucidate a mechanism of pacemaker-induced reentry in excitable media.

Chaotic dynamics in cardiac aggregates induced by potassium channel block

Chaotic rhythms in deterministic models can arise as a consequence of changes in model parameters. We carried out experimental studies in which we induced a variety of complex rhythms in aggregates of embryonic chick cardiac cells using E-4031 (1.0-2.5 μM), a drug that blocks the hERG potassium channel. Following the addition of the drug, the regular rhythm evolved to display a spectrum of complex dynamics: irregular rhythms, bursting oscillations, doublets, and accelerated rhythms. The interbeat intervals of the irregular rhythms can be described by one-dimensional return maps consistent with chaotic dynamics. A Hodgkin-Huxley-style cardiac ionic model captured the different types of complex dynamics following blockage of the hERG mediated potassium current.