Investigating Electrophysiological Markers of Arrhythmogenesis in a Chronic Myocardial Infarction Ovine Model

Open-source software for respiratory rate estimation using single-lead electrocardiograms

Respiratory rate (RR) is a critical vital sign used to assess pulmonary function. Currently, RR estimating instrumentation is specialized and bulky, therefore unsuitable for remote health monitoring. Previously, RR was estimated using proprietary software that extract surface electrocardiogram (ECG) waveform features obtained at several thoracic locations. However, developing a non-proprietary method that uses minimal ECG leads, generally available from mobile cardiac monitors is highly desirable. Here, we introduce an open-source and well-documented Python-based algorithm that estimates RR requiring only single-stream ECG signals. The algorithm was first developed using ECGs from awake, spontaneously breathing adult human subjects. The algorithm-estimated RRs exhibited close linear correlation to the subjects' true RR values demonstrating an R2 of 0.9092 and root mean square error of 2.2 bpm. The algorithm robustness was then tested using ECGs generated by the ischemic hearts of anesthetized, mechanically ventilated sheep. Although the ECG waveforms during ischemia exhibited severe morphologic changes, the algorithm-determined RRs exhibited high fidelity with a resolution of 1 bpm, an absolute error of 0.07 ± 0.07 bpm, and a relative error of 0.67 ± 0.64%. This optimized Python-based RR estimation technique will likely be widely adapted for remote lung function assessment in patients with cardiopulmonary disease.

Can Stochastic Pacing Restore Heart Rate Variability in Diseased Hearts? An In-vivo Ovine Case Study

Heart rate variability (HRV) is an important clinical parameter that depicts the autonomic balance. Diminished HRV has been associated with diseased hearts and incorporating stochasticity in pacing has been investigated as a potential mechanism for restoring the altered autonomic balance and preventing cardiac arrhythmias. We studied the change in HRV with the development of chronic myocardial infarction (MI) in adult sheep (n=16). Next, we investigated the utility of stochastic pacing in modulating HRV in-vivo in both sham and MI hearts. The propensity of the heart to the development of cardiac alternans, a known precursor to tachyarrhythmias, was studied under three different pacing techniques, namely periodic pacing, stochastic pacing and constant diastolic interval (DI) pacing in one sham and one MI sheep. Autonomic balance was observed to be altered after 6 weeks of chronic MI. Increased heart rate, QTc interval, standard deviation of the R-R intervals and LF/HF ratio was observed in MI hearts. Stochastic pacing was found to be proarrhythmic and increased T-wave alternans burden was observed with increase in stochasticity. Maintaining a constant DI on every beat demonstrated reduced alternans levels compared to both periodic and stochastic pacing.Clinical Relevance-Our results demonstrate that precise control of the diastolic interval may be more beneficial in inhibiting arrhythmias than stochastic pacing.