Velocity of electrical conduction in cardiac tissue is a function of mechanical strain. Although strain-modulated velocity is a well established finding in experimental cardiology, its underlying mechanisms are not well understood. In this work, we summarized potential factors contributing to strain-velocity relationships and reviewed related experimental and computational studies. We presented results from our experimental studies on rabbit papillary muscle, which supported a biphasic relationship of strain and velocity under uni-axial straining conditions. In the low strain range, the strain-velocity relationship was positive. Conduction velocity peaked with 0.59 m/s at 100% strain corresponding to maximal force development. In the high strain range, the relationship was negative. Conduction was reversibly blocked at 118+/-1.8% strain. Reversible block occurred also in the presence of streptomycin. Furthermore, our studies revealed a moderate hysteresis of conduction velocity, which was reduced by streptomycin. We reconstructed several features of the strain-velocity relationship in a computational study with a myocyte strand. The modeling included strain-modulation of intracellular conductivity and stretch-activated cation non-selective ion channels. The computational study supported our hypotheses, that the positive strain-velocity relationship at low strain is caused by strain-modulation of intracellular conductivity and the negative relationship at high strain results from activity of stretch-activated channels. Conduction block was not reconstructed in our computational studies. We concluded this work by sketching a hypothesis for strain-modulation of conduction and conduction block in papillary muscle. We suggest that this hypothesis can also explain uni-axially measured strain-conduction velocity relationships in other types of cardiac tissue, but apparently necessitates adjustments to reconstruct pressure or volume related changes of velocity in atria and ventricles.

Cardio-respiratory monitoring is one of the most demanding areas in the rapidly growing, mobile-device, based health care delivery. We developed a 12-lead smartphone-based electrocardiogram (ECG) acquisition and monitoring system (called “cvrPhone”), and an application to assess underlying ischemia, and estimate the respiration rate (RR) and tidal volume (TV) from analysis of electrocardiographic (ECG) signals only. During in-vivo swine studies (n = 6), 12-lead ECG signals were recorded at baseline and following coronary artery occlusion. Ischemic indices calculated from each lead showed statistically significant (p < 0.05) increase within 2 min of occlusion compared to baseline. Following myocardial infarction, spontaneous ventricular tachycardia episodes (n = 3) were preceded by significant (p < 0.05) increase of the ischemic index ~1–4 min prior to the onset of the tachy-arrhythmias. In order to assess the respiratory status during apnea, the mechanical ventilator was paused for up to 2 min during normal breathing. We observed that the RR and TV estimation algorithms detected apnea within 7.9 ± 1.1 sec and 5.5 ± 2.2 sec, respectively, while the estimated RR and TV values were 0 breaths/min and less than 100 ml, respectively. In conclusion, the cvrPhone can be used to detect myocardial ischemia and periods of respiratory apnea using a readily available mobile platform.

Background Sleep disordered breathing manifested as sleep apnea (SA) is prevalent in the general population, and while it is associated with increased morbidity and mortality risk in some patient populations, it remains under-diagnosed. The objective of this study was to assess the accuracy of respiration-rate (RR) and tidal-volume (TV) estimation algorithms, from body-surface ECG signals, using a smartphone based ambulatory respiration monitoring system (cvrPhone). Methods Twelve lead ECG signals were collected using the cvrPhone from anesthetized and mechanically ventilated swine (n = 9). During ECG data acquisition, the mechanical ventilator tidal-volume (TV) was varied from 250 to 0 to 750 to 0 to 500 to 0 to 750 ml at respiratory rates (RR) of 6 and 14 breaths/min, respectively, and the RR and TV values were estimated from the ECG signals using custom algorithms. Results TV estimations from any two different TV settings showed statistically significant difference (p < 0.01) regardless of the RR. RRs were estimated to be 6.1±1.1 and 14.0±0.2 breaths/min at 6 and 14 breaths/min, respectively (when 250, 500 and 750 ml TV settings were combined). During apnea, the estimated TV and RR values were 11.7±54.9 ml and 0.0±3.5 breaths/min, which were significantly different (p<0.05) than TV and RR values during non-apnea breathing. In addition, the time delay from the apnea onset to the first apnea detection was 8.6±6.7 and 7.0±3.2 seconds for TV and RR respectively. Conclusions We have demonstrated that apnea can reliably be detected using ECG-derived RR and TV algorithms. These results support the concept that our algorithms can be utilized to detect SA in conjunction with ECG monitoring.

Background: Repolarization alternans (RA) has been implicated in the pathogenesis of ventricular arrhythmias and sudden cardiac death. Methods: We have developed a real-time, closed-loop system to record and analyze RA from multiple intracardiac leads, and deliver dynamically R-wave triggered pacing stimuli during the absolute refractory period. We have evaluated the ability of this system to control RA and reduce arrhythmia susceptibility, in vivo. Results: R-wave triggered pacing can induce RA, the magnitude of which can be modulated by varying the amplitude, pulse width, and size of the pacing vector. Using a swine model (n=9), we demonstrate that to induce a 1 µV change in the alternans voltage on the body surface, coronary sinus and left ventricle leads, requires a delivered charge of 0.04±0.02, 0.05±0.025, and 0.06±0.033 µC, respectively, while to induce a one unit change of the K score , requires a delivered charge of 0.93±0.73, 0.32±0.29, and 0.33±0.37 µC, respectively. For all body surface and intracardiac leads, both Δ(alternans voltage) and ΔK score between baseline and R-wave triggered paced beats increases consistently with an increase in the pacing pulse amplitude, pulse width, and vector spacing. Additionally, we show that the proposed method can be used to suppress spontaneously occurring alternans (n=7), in the presence of myocardial ischemia. Suppression of RA by pacing during the absolute refractory period results in a significant reduction in arrhythmia susceptibility, evidenced by a lower S rank score during programmed ventricular stimulation compared with baseline before ischemia. Conclusions: We have developed and evaluated a novel closed-loop method to dynamically modulate RA in a swine model. Our data suggest that suppression of RA directly reduces arrhythmia susceptibility and reinforces the concept that RA plays a critical role in the pathophysiology of arrhythmogenesis.

Repolarization alternans (RA) has been implicated in the pathogenesis of ventricular arrhythmias and sudden cardiac death. We developed a 12-lead, blue-tooth/Smart-Phone (Android) based electrocardiogram (ECG) acquisition and monitoring system (cvrPhone), and an application to estimate RA, in real-time. In in-vivo swine studies (N = 17), 12-lead ECG signals were recorded at baseline and following coronary artery occlusion. RA was estimated using the Fast Fourier Transform (FFT) method using a custom developed algorithm in JAVA. Underlying ischemia was detected using a custom developed ischemic index. RA from each lead showed a significant (p < 0.05) increase within 1 min of occlusion compared to baseline (n = 29). Following myocardial infarction, spontaneous ventricular tachycardia episodes (n = 4) were preceded by significant (p < 0.05) increase of RA prior to the onset of the tachy-arrhythmias. Similarly, the ischemic index exhibited a significant increase following myocardial infarction (p < 0.05) and preceding a tachy-arrhythmic event. In conclusion, RA can be effectively estimated using surface lead electrocardiograms by analyzing beat-to-beat variability in ECG morphology using a smartphone based platform. cvrPhone can be used to detect myocardial ischemia and arrhythmia susceptibility using a user-friendly, clinically acceptable, mobile platform.