Anticipatory cardiac deceleration estimates cognitive performance in virtual reality beyond tonic heart period and heart period variability.

Anticipatory cardiac deceleration is the lengthening of heart period before an expected event. It appears to reflect preparation that supports rapid action. The current study sought to bolster anticipatory deceleration as a practical and unique estimator of performance efficiency. To this end, we examined relationships between deceleration and virtual reality performance under low and high time pressure. Importantly, we investigated whether deceleration separately estimates performance beyond basal heart period and basal high-frequency heart rate variability (other vagally influenced metrics related to cognition). Thirty participants completed an immersive virtual reality (VR) cognitive performance task across six longitudinal sessions. Anticipatory deceleration and basal heart period/heart period variability were quantified from electrocardiography collected during pre-task anticipatory countdowns and baseline periods, respectively. At the between-person level, we found that greater anticipatory declaration was related to superior accuracy and faster response times (RT). The relation between deceleration and accuracy was stronger under high relative to low time pressure, when good performance requires greater efficiency. Findings for heart period and heart period variability largely converge with the prior literature, but importantly, were statistically separate from deceleration effects on performance. Lastly, deceleration effects were detected using anticipatory periods that are more practical (shorter and more intermittent) than those typically employed. Taken together, findings suggest that anticipatory deceleration is a unique and practical correlate of cognitive-motor efficiency apart from heart period and heart period variability in virtual reality.

Multi-granular Analysis and Physiological Interpretations of Heart Rate Variability Metrics During VR-Shooting Difficulty Induced Stress.

Physiological sensing of virtual reality (VR)-induced stressors are increasingly utilized to improve human training and assess the impact of gaming difficulty-induced stress on a person's health and well-being. However, the prior art sparsely explores the multi-level cardiovascular dynamics for psychophysiological demands in a VR environment. This treatise discusses the experimental findings and physiological interpretations of various heart rate variability (HRV) metrics extracted from 31 participants during a Go/No-Go VR-based shooting task across multiple timeframes. The VR-shooting exercise consists of firing at the enemy targets while sparing the friendly ones for different shooting difficulty levels: low-difficulty and high-difficulty with in-between baselines. Ex-perimental results demonstrate consistent shooting difficulty-induced stress patterns at multi-granular levels in response to the heterogeneous inputs (exogenous and endogenous factors). The physiological interpretations highlight the intricate inter-play between cardio-physiological components: sympathetic and parasympathetic response across multiple timescales (sessions and blocks) and shooting difficulty levels.

A deep residual inception network with channel attention modules for multi-label cardiac abnormality detection from reduced-lead ECG.

Objective.Most arrhythmias due to cardiovascular diseases alter the heart's electrical activity, resulting in morphological alterations in electrocardiogram (ECG) recordings. ECG acquisition is a low-cost, non-invasive process and is commonly used for continuous monitoring as a diagnostic tool for cardiac abnormality identification. Our objective is to diagnose twenty-nine cardiac abnormalities and sinus rhythm using varied lead ECG signals.Approach.This work proposes a deep residual inception network with channel attention mechanism (RINCA) for twenty-nine cardiac arrhythmia classification along with normal ECG from multi-label ECG signal with different lead combinations. TheRINCAarchitecture employing the inception-based convolutional neural network backbone uses residual skip connections with the channel attention mechanism. The inception model facilitates efficient computation and prevents overfitting while exploring deeper networks through dimensionality reduction and stacked 1-dimensional convolutions. The residual skip connections alleviate the vanishing gradient problem. The attention modules selectively leverage the temporally significant segments in a sequence and predominant channels for multi-lead ECG signals, contributing to the decision-making.Main results.Exhaustive experimental evaluation on the large-scale 'PhysioNet/Computing in Cardiology Challenge (2021)' dataset demonstratesRINCA's efficacy. On the hidden test data set,RINCAachieves the challenge metric score of 0.55, 0.51, 0.53, 0.51, and 0.53 (ranked 2nd, 5th, 4th, 5th and 4th) for the twelve-lead, six-lead, four-lead, three-lead, and two-lead combination cases, respectively.Significance.The proposedRINCAmodel is more robust against varied sampling frequency, recording time, and data with heterogeneous demographics than the existing art. The explainability analysis showsRINCA's potential in clinical interpretations.

Alterations in Multi-channel EEG Dynamics During a Stressful Shooting Task in Virtual Reality Systems.

This paper explores power spectrum-based features extracted from the 64-channel electroencephalogram (EEG) signals to analyze brain activity alterations during a virtual reality (VR)-based stressful shooting task, with low and high difficulty levels, from an initial resting baseline. This paper also investigates the variations in EEG across several experimental sessions performed over multiple days. Results indicate that patterns of changes in different power bands of the EEG are consistent with high mental stress levels during the shooting task compared to baseline. Although there is one inconsistency, overall, the brain patterns indicate higher stress levels during high difficulty tasks than low difficulty tasks and in the first session compared to the last session.