Browsing by Author "Ghosh, Nirmalya"
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Item Affective Physiological State Analysis and Interpretable Predictive Modeling of Marksmanship in Go/NoGo VR ShootingDifficulty Task(2022-12-11) Pratiher, Sawon; Sahoo, Karuna P.; Acharya, Mrinal; Radhakrishnan, Ananth; Alam, Sazedul; Kerick, Scott E.; Banerjee, Nilanjan; Ghosh, Nirmalya; Amit PatraOverburdening an individual’s limited cognitive resources, especially when engaged in critical operations, may result in disastrous mishaps. Regular assessments of individuals’ physiological states and associated performance become vital to improving their mission readiness in such scenarios. As a key step towards a field-ready system, this treatise discusses the experimental findings pertinent to affective physiological state modulation and predictive modeling of marksmanship during a Go/NoGo shooting task in an immersive virtual reality environment. The shooting exercise requires the participants to hit the enemy targets and spare the friendly targets. The shooting difficulty levels (SDLs) are introduced by modulating the subjectspecific target exposure time. The physiological signals used for analysis comprise electrocardiogram (ECG), 64-channel electroencephalogram (EEG), and standard shooting performance scores from 31 subjects. Experimental results with ECG features encompass involuntary physiologic process regulation and the interplay between the autonomic nervous system (ANS) components varying with SDL. Similarly, EEG features highlight the variations in brain region activations with SDLs. Predictive modeling of shooting performance (enemy hit, friendly spare, overall score) and behavioral response (mean enemy reaction time) from physiological (ECG and EEG) features evince the potency of physiological sensing for marksmanship estimation in operational contexts. Moreover, interpretable Shapley value analysis of the predictive models comprehend the (positive/negative) marginal impact of the underlying physiological features on marksmanship. This multimodal physiological sensing framework may assess the alterations in psychophysiological affective states and cognitive effects for performance analysis in operational contexts.Item Alterations in Multi-channel EEG Dynamics During a Stressful Shooting Task in Virtual Reality Systems(IEEE, 2021-12-09) Sahoo, Karuna P.; Radhakrishnan, Ananth; Pratiher, Sawon; Alam, Sazedul; Kerick, Scott; Ghosh, Nirmalya; Chhan, David; Banerjee, Nilanjan; Patra, AmitThis 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.Item A Bio-toolkit for Multi-Cardiac Abnormality Diagnosis using ECG Signal and Deep Learning(Computing in Cardiology) Kirodiwal, Akash; Srivastava, Apoorva; Dash, Ashutosh; Saha, Ayantika; Penaganti, Gopi Vamsi; Pratiher, Sawon; Alam, Sazedul; Patra, Amit; Ghosh, Nirmalya; Banerjee, NilanjanAutomated cardiac abnormality detection from an everexpanding number of electrocardiogram (ECG) records has been widely used to assist physicians in the clinical diagnosis of a variety of cardiovascular diseases. Over the last few years, deep learning (DL) architectures have achieved state-of-the-art performances in various biomedical applications. In this work, we propose a bio-toolkit based on the DL framework comprising of stacked convolutional and long short term memory neural network blocks for multi-label ECG signal classification. Our team participated under the name ”Cardio-Challengers” in the ”PhysioNet/Computing in Cardiology Challenge 2020” and obtained a challenge metric score of 0.337.Item Classification of VR-Gaming Difficulty Induced Stress Levels using Physiological (EEG & ECG) Signals and Machine Learning(IEEE, 2021-11-08) Pratiher, Sawon; Radhakrishnan, Ananth; Sahoo, Karuna P.; Alam, Sazedul; Kerick, Scott E.; Banerjee, Nilanjan; Ghosh, Nirmalya; Patra, AmitPhysiological sensing has long been an indispensable fixture for virtual reality (VR) gaming studies. Moreover, VR induced stressors are increasingly being used to assess the impact of stress on an individual’s health and well-being. This study discusses the results of experimental research comprising multimodal physiological signal acquisition from 31 participants during a Go/No-Go VR-based shooting exercise where participants had to shoot the enemy and spare the friendly targets. The study encompasses multiple sessions, including orientation, thresholding, and shooting. The shooting sessions consist of tasks under low & high difficulty induced stress conditions with in-between baseline segments. Machine learning (ML) performance with heart rate variability (HRV) from electrocardiogram (ECG) and electroencephalogram (EEG) features outperform the prevalent methods for four different VR gaming difficulty-induced stress (GDIS) classification problems (CPs). Further, the significance of the HRV predictors and different brain region activations from EEG is deciphered using statistical hypothesis testing (SHT). The ablation study shows the efficacy of multimodal physiological sensing for different gaming difficulty-induced stress classification problems (GDISCPs) in a VR shooting task.Item A deep residual inception network with channel attention modules for multi-label cardiac abnormality detection from reduced-lead ECG(IOP, 2022-05-12) Srivastava, Apoorva; Pratiher, Sawon; Alam, Sazedul; Hari, Ajith; Banerjee, Nilanjan; Ghosh, Nirmalya; Patra, AmitObjective. Most arrhythmias due to cardiovascular diseases alter the 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 (CAC) along with normal ECG from multi-label ECG signal with different lead combinations. The RINCA architecture 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 demonstrates RINCA efficacy. On the hidden test data set, RINCA achieves 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 proposed RINCA model is more robust against varied sampling frequency, recording time, and data with heterogeneous demographics than the existing art. The explainability analysis shows RINCA potential in clinical interpretations.