ECG

Myocardial Scar on Surface ECG: Selvester Score, but Not Fragmentation, Predicts Response to CRT

Purpose. Myocardial scar is directly related to the response to CRT after implantation. The extent of myocardial scar can be detected not only by cardiac magnetic resonance but also by two electrocardiographic scores: fragmented QRS (fQRS) and Selvester score (SSc). The aim of our study is to compare the role of baseline SSc and fQRS in predicting response to CRT in a cohort of heart failure patients with true left bundle branch block (LBBB).

Obesity is associated with lack of inhibitory control and impaired heart rate variability reactivity and recovery in response to food stimuli

Recent theories compare obesity with addiction in terms of lack of inhibitory control in both clinical populations. The present study hypothesized impaired inhibition in obese patients reflected both in executive functions and reduced vagal tone (indexed by a decrease in heart rate variability; HRV) in response to food stimuli. Twenty-four inpatients with obesity (19 women) and 37 controls (24 women) underwent ECG monitoring during baseline, food stimuli viewing, and a recovery phase.

How neurophysiological measures can be used to enhance the evaluation of remote tower solutions

New solutions in operational environments are often, among objective measurements, evaluated by using subjective assessment and judgment from experts. Anyhow, it has been demonstrated that subjective measures suffer from poor resolution due to a high intra and inter-operator variability. Also, performance measures, if available, could provide just partial information, since an operator could achieve the same performance but experiencing a different workload.

Fog-computing-based heartbeat detection and arrhythmia classification using machine learning

Designing advanced health monitoring systems is still an active research topic. Wearable and remote monitoring devices enable monitoring of physiological and clinical parameters (heart rate, respiration rate, temperature, etc.) and analysis using cloud-centric machine-learning applications and decision-support systems to predict critical clinical states. This paper moves from a totally cloud-centric concept to a more distributed one, by transferring sensor data processing and analysis tasks to the edges of the network.

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