A complexity efficient penta-diagonal quantum smoothing filter for bio-medical signal denoising: a study on ECG.

Extracting information-bearing signal from a noisy environment has been a practical challenge in both classical and quantum computing formalism, especially in critical signal processing applications. To filter out the effect of noise, we propose a quantum smoothing filter built upon quantum formalism-based circuits applied for electrocardiogram signal denoising. The proposed quantum filter is a conceptually novel framework with an advantage in computational complexity as compared to the existing classical filters, such as discrete wavelet transform and empirical mode decomposition, whereas it achieves similar performance metrics for the accuracy of the filter. Further, we exploit the penta-diagonal Toeplitz structure of the smoothing filter, which gives approximately 48 % gate cost reduction for 10 qubit circuit compared to the standard Hamiltonian simulation without structure. The run-time complexity using the quantum matrix inversion technique for the structured matrix is given by O ~ κ 2 poly ( log N ) ε P for condition number κ of the N × N filter matrix within precision ε P . Embedding fixed sparsity of the banded matrix, the quantum filter shows potentially better run-time complexity than classical filtering techniques. For the quantifiable research results of our work, we have shown several performance metrics, such as mean-square error and peak signal-to-noise ratio analysis, with a bound of error due to observation noise, simulation error and quantum measurement uncertainty.

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.

Multifractal Alterations in Oral Sub-Epithelial Connective Tissue During Progression of Pre-Cancer and Cancer.

Bright-field microscopy (BFM) encrypts the optical transillumination profile of the transmitted light attenuated by the complex micro-structural tissue convolutions, manifested by the dense and compact regions of the specimen under examination. The connotations of idiosyncratic tissue interaction dynamics with the onset of pre-cancerous activity are encoded in the BFM acquired oral mucosa histopathological images (OMHI). In the present study, our analysis is focused on the sub-epithelium region of the oral mucosa, which has high clinical significance but sparsely explored in the literature from the textural domain. Histopathology being the gold-standard technique till date, we have used the light microscopic histopathology images for tissue characterization. The tissue-index transmission patches (TITP) from the sub-epithelium region are cropped under the guidance of oral onco-pathologists. After that, the TITPs are characterized for its multi-scale spatial-deformation dynamics, while keeping the intrinsic anisotropic geometry, and local contour connectivity within tolerable limits. With recent studies exhibiting multifractal's potency in diverse biological system analysis, here, we exploit the 2D multifractal detrended fluctuation analysis (2D-MFDFA) on TITPs for exploring a discriminative set of multifractal signatures for healthy, oral potentially malignant disorders and oral cancer tissue sample. The predictive model's competency is validated on an experimentally collected corpus of TITP samples and substantiated via confirmatory data statistics and analysis, showing its inter-class segregation efficacy. Moreover, the 2D-MFDFA analysis evinces the complex multifractal patterns in TITPs, which is due to the presence of composite long-range correlations in the oral mucosa tissue fabric.