Endocardial repolarization dispersion in BrS: A novel automatic algorithm for mapping activation recovery interval.

INTRODUCTION: Repolarization dispersion in the right ventricular outflow tract (RVOT) contributes to the type-1 electrocardiographic (ECG) phenotype of Brugada syndrome (BrS), while data on the significance and feasibility of mapping repolarization dispersion in BrS patients are scarce. Moreover, the role of endocardial repolarization dispersion in BrS is poorly investigated. We aimed to assess endocardial repolarization patterns through an automated calculation of activation recovery interval (ARI) estimated on unipolar electrograms (UEGs) in spontaneous type-1 BrS patients and controls; we also investigated the relation between ARI and right ventricle activation time (RVAT), and T-wave peak-to-end interval (Tpe) in BrS patients. METHODS: Patients underwent endocardial high-density electroanatomical mapping (HDEAM); BrS showing an overt type-1 ECG were defined as OType1, while those without (latent type-1 ECG and LType1) received ajmaline infusion. BrS patients only underwent programmed ventricular stimulation (PVS). Data were elaborated to obtain ARI corrected with the Bazett formula (ARIc), while RVAT was derived from activation maps. RESULTS: 39 BrS subjects (24 OType1 and 15 LTtype1) and 4 controls were enrolled. OType1 and post-ajmaline LType1 showed longer mean ARIc than controls (306 ± 27.3 ms and 333.3 ± 16.3 ms vs. 281.7 ± 10.3 ms, p = .05 and p < .001, respectively). Ajmaline induced a significant prolongation of ARIc compared to pre-ajmaline LTtype1 (333.3 ± 16.3 vs. 303.4 ± 20.7 ms, p < .001) and OType1 (306 ± 27.3 ms, p < .001). In patients with type-1 ECG (OTtype1 and post-ajmaline LType1) ARIc correlated with RVAT (r = .34, p = .04) and Tpec (r = .60, p < .001), especially in OType1 subjects (r = .55, p = .008 and r = .65 p < .001, respectively). CONCLUSION: ARIc mapping demonstrates increased endocardial repolarization dispersion in RVOT in BrS. Endocardial ARIc positively correlates with RVAT and Tpec, especially in OType1.

Wearable Sensors to Evaluate Autonomic Response to Olfactory Stimulation: The Influence of Short, Intensive Sensory Training.

In the last few decades, while the sensory evaluation of edible products has been leveraged to make strategic decisions about many domains, the traditional descriptive analysis performed by a skilled sensory panel has been seen to be too complex and time-consuming for the industry needs, making it largely unsustainable in most cases. In this context, the study of the effectiveness of different methods for sensory training on panel performances represents a new trend in research activity. With this purpose, wearable sensors are applied to study physiological signals (ECG and skin conductance) concerned with the emotions in a cohort of volunteers undergoing a short, two-day (16 h) sensory training period related to wine tasting. The results were compared with a previous study based on a conventional three-month (65 h) period of sensory training. According to what was previously reported for long panel training, it was seen that even short, intensive sensory training modulated the ANS activity toward a less sympathetically mediated response as soon as odorous compounds become familiar. A large-scale application of shorter formative courses in this domain appears possible without reducing the effectiveness of the training, thus leading to money saving for academia and scientific societies, and challenging dropout rates that might affect longer courses.

ECG Modeling for Simulation of Arrhythmias in Time-Varying Conditions.

The present article proposes an ECG simulator that advances modeling of arrhythmias and noise by introducing time-varying signal characteristics. The simulator is built around a discrete-time Markov chain model for simulating atrial and ventricular arrhythmias of particular relevance when analyzing atrial fibrillation (AF). Each state is associated with statistical information on episode duration and heartbeat characteristics. Statistical, time-varying modeling of muscle noise, motion artifacts, and the influence of respiration is introduced to increase the complexity of simulated ECGs, making the simulator well suited for data augmentation in machine learning. Modeling of how the PQ and QT intervals depend on heart rate is also introduced. The realism of simulated ECGs is assessed by three experienced doctors, showing that simulated ECGs are difficult to distinguish from real ECGs. Simulator usefulness is illustrated in terms of AF detection performance when either simulated or real ECGs are used to train a neural network for signal quality control. The results show that both types of training lead to similar performance.