Antonis A. Armoundas

Background: We investigated the ability of a novel stand-alone, smartphone-based system, the cvrPhone, in estimating the minute ventilation (MV) from body surface electrocardiographic (ECG) signals. Methods: Twelve lead ECG signals were collected from anesthetized and mechanically ventilated swine (n = 9) using standard surface electrodes and the cvrPhone. The tidal volume delivered to the animals was varied between 0, 250, 500, and 750 mL at respiration rates of 6 and 14 breaths/min. MV estimates were determined by the cvrPhone and were compared with the delivered ones. Results: The median relative estimation errors were 17%, −4%, 35%, −3%, −9%, and 1%, for true MVs of 1,500, 3,000, 3,500, 4,500, 7,000, and 10,500 breaths*mL/min, respectively. The MV estimates at each of the settings were significantly different from each other (p < 0.05). Conclusions: We have demonstrated that accurate MV estimations can be derived from standard body surface ECG signals, using a smartphone.

Aims: This work attempts to develop a standalone heart rhythm alerting system for the intensive care unit (ICU), where life-threatening arrhythmias have to be identified/alerted more precisely and more instantaneously (i.e. with lower latency) than existing bedside monitors. Methods and results: We use the dataset from the PhysioNet 2015 Challenge, which contains records that led to true and false arrhythmic alarms in the ICU. These records have been re-annotated as one of eight classes, namely (i) asystole, (ii) extreme bradycardia, (iii) extreme tachycardia, (iv) ventricular fibrillation (VF), (v) ventricular tachycardia (VT), (vi) normal sinus rhythm, (vii) sinus tachycardia, and (viii) noise/artefacts. Arrhythmia-specific features and features that measure the signal quality were extracted from all the records. To improve VF detection, an improved, over an existing, single-lead R-wave detection was developed that takes into account the R-waves detected in all electrocardiographic (ECG) leads. To avoid false R-wave detection due to pacing spikes, ECG signals were filtered with a low pass filter prior to R-wave detection, while the raw signals were used for feature extraction. Random forest was used as the classifier, and 10-time five-fold cross-validation, resulted in a macro-average sensitivity of 81.54%. Conclusions: In conclusion, comparing with the bedside monitors used in the PhysioNet 2015 competition, we find that our method achieves higher positive predictive values for asystole, extreme bradycardia, VT, and VF; furthermore, our method is able to alert the presence of arrhythmia instantaneously, i.e. up to 4 s earlier.

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.

This work attempts to reduce the number of false alarms generated by bedside monitors in the intensive care unit (ICU), as a majority of current alarms are false. In this study, we applied methods that can be categorized into three stages: signal processing, feature extraction, and optimized machine learning. At the stage of signal processing, we ensured that the heartbeats were properly annotated. During feature extraction, besides extracting features that are relevant to the arrhythmic alarms, we also extracted a set of signal quality indices (SQIs), which we used to distinguish noise/artifact from normal physiological signals. When applying a machine learning algorithm (Random Forest), we performed feature selection in order to reduce the complexity of the models and improve the efficiency of the algorithm. The dataset used is from Reducing False Arrhythmia Alarms in the ICU: the PhysioNet/Computing in Cardiology Challenge 2015. Using the performance metric “score” from the Challenge, we achieved a score of 83.08 in the real-time category on the hidden test set, which is the highest in all published work.

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). 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. 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. 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.

The spatio-temporal organization of mitochondria in cardiac myocytes facilitates myocyte-wide, cluster-bound, mitochondrial inner membrane potential oscillatory depolarizations, commonly triggered by metabolic or oxidative stressors. Local intermitochondrial coupling can be mediated by reactive oxygen species (ROS) that activate inner membrane pores to initiate a ROS-induced-ROS-release process that produces synchronized limit cycle oscillations of mitochondrial clusters within the whole mitochondrial network. The network’s dynamic organization, structure and function can be assessed by quantifying dynamic local coupling constants and dynamic functional clustering coefficients, both providing information about the network’s response to external stimuli. In addition to its special organization, the mitochondrial network of cardiac myocytes exhibits substrate-sensitive coupling constants and clustering coefficients. The myocyte’s ability to form functional clusters of synchronously oscillating mitochondria is sensitive to conditions such as substrate availability (e.g., glucose, pyruvate, β-hydroxybutyrate), antioxidant status, respiratory chain activity, or history of oxidative challenge (e.g., ischemia-reperfusion). This underscores the relevance of quantitative methods to characterize the network’s functional status as a way to assess the myocyte’s resilience to pathological stressors.