Sawon Pratiher

Indian Institute of Technology Kharagpur

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Nirmalya Ghosh

Indian Institute of Science Education and Research Kolkata

Sabyasachi Mukhopadhyay

Indian Institute of Technology Kharagpur

Prasanta K. Panigrahi

Indian Institute of Science Education and Research Kolkata

Amit Patra

Indian Institute of Technology Kharagpur

Subhankar Chattoraj

University of Maryland, Baltimore

Nirmalya Ghosh

Indian Institute of Technology Kharagpur

Ritwik Barman

Indian Institute of Science Education and Research Kolkata

Nilanjan Banerjee

University of Maryland, Baltimore County

Karuna P. Sahoo

Indian Institute of Technology Kharagpur

Asima Pradhan

Indian Institute of Technology Kanpur

Cooperative Institutions

Indian Institute of Technology Kharagpur

Jadavpur University

University of Maryland, Baltimore County

United States Army Combat Capabilities Development Command

DEVCOM Army Research Laboratory

Indian Institute of Science Education and Research Kolkata

Indian Institute of Technology Indore

University of Engineering & Management

University of Kalyani

Indian Institute of Technology Kanpur

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Trends in wind velocity distribution using phase space trajectory

Sawon Pratiher Sayan Patra Paritosh Bhattacharya

10.1049/cp.2016.1464

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A Dilated Residual Vision Transformer for Atrial Fibrillation Detection from Stacked Time-Frequency ECG Representations

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2022)

Sawon Pratiher Apoorva Srivastava Yedla Bindu Priyatha Nirmalya Ghosh Amit Patra

Atrial fibrillation (AF), the most frequent type of cardiac arrhythmia, has no apparent clinical symptoms in most cases for patients, making it more challenging to diagnose. However, the alterations in regular heart rhythm with an absence of visible P-waves in an Electrocardiogram (ECG) are often characterized as AF symptoms. ECG signals are often employed for AF prognosis to minimize the risk of stroke, coronary artery disease, and other cardiovascular diseases. This work proposes a new vision transformer (ViT) variant, namely, Dilated Residual ViT (DiResViT), by replacing the original patchify stem in ViT with dilated convolutional stem having residual connections for improved AF detection from an ensemble of ECG time-frequency representations. Dilated convolutions facilitate dense feature representation by comprehending the multi-scale contextual information and allowing the receptive field to expand exponentially without losing resolution. The introduction of residual connections alleviates the vanishing and exploding gradients problem by enabling the gradients to bypass some of the non-linear activation functions with improved training convergence. DiResViT's exhaustive experimental validation outperforms the prior art in ECG-based AF detection, while the ablation study evinces enhanced performance compared to the existing ViT.

10.1109/icassp43922.2022.9747258

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SolarisNet: A Deep Regression Network for Solar Radiation Prediction

arXiv (Cornell University) (2017)

Subhadip Dey Sawon Pratiher Saon Banerjee C. K. Mukherjee

Effective utilization of photovoltaic (PV) plants requires weather variability robust global solar radiation (GSR) forecasting models. Random weather turbulence phenomena coupled with assumptions of clear sky model as suggested by Hottel pose significant challenges to parametric & non-parametric models in GSR conversion rate estimation. Also, a decent GSR estimate requires costly high-tech radiometer and expert dependent instrument handling and measurements, which are subjective. As such, a computer aided monitoring (CAM) system to evaluate PV plant operation feasibility by employing smart grid past data analytics and deep learning is developed. Our algorithm, SolarisNet is a 6-layer deep neural network trained on data collected at two weather stations located near Kalyani metrological site, West Bengal, India. The daily GSR prediction performance using SolarisNet outperforms the existing state of art and its efficacy in inferring past GSR data insights to comprehend daily and seasonal GSR variability along with its competence for short term forecasting is discussed.

Weather prediction

10.48550/arxiv.1711.08413

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Hydrophobicity-Based Grading of Industrial Composite Insulators Images Using Cross Attention Vision Transformer With Knowledge Distillation

IEEE Transactions on Dielectrics and Electrical Insulation (2023)

Samiran Das Sujoy Chatterjee Sawon Pratiher Subhankar Chattoraj Mainak Basu

The hydrophobic property of composite insulators (CIs) is a crucial attribute that prevents the accumulation of significant water content on the surface and ensures smooth operation and prevention of power line surges. However, since the weathering effect gradually reduces the hydrophobicity, periodic monitoring is necessary to ensure the smooth operation of the powerline. Efficient machine learning-based analysis of the CI images collected by the spray method can potentially achieve very high efficacy. However, inconsistency in the shape and size of droplets, the intricate pattern of the droplets, and the lack of color variation of the images, pose a challenge to the prevalent computer vision techniques for categorizing the CIs. This treatise explored a novel, efficient deep-learning paradigm, termed cross-attention vision transformer (CA-ViT) for grading the CI images. The CA-ViT uses both small and large patches to blend spatial features corresponding to a different scale. The network uses a token fusion module to effectively combine the tokens obtained from small and large patches and understand the visual pattern, and characterize droplets of different shapes and sizes by exchanging cross-attentions, which demonstrates its ability to precisely detect small and large droplets, and accurately classify the droplet images. Further, the work also introduces a knowledge distillation (KD) strategy to reduce the computational run time of the model. Exhaustive experimental results have confirmed that our proposed work surpasses state-of-the-art methods.

Grading (engineering)

10.1109/tdei.2023.3347377

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Affective Physiological State Analysis and Interpretable Predictive Modeling of Marksmanship in Go/NoGo VR Shooting Difficulty Task

Sawon Pratiher Karuna P. Sahoo Mrinal Acharya Ananth Radhakrishnan SAZEDUL ALAM

This work has been submitted to the IEEE Transactions on Affective Computing for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Overburdening 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 subject-specific 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.

Spare part

10.36227/techrxiv.21669248

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Manifold Learning & Stacked Sparse Autoencoder for Robust Breast Cancer Classification from Histopathological Images.

arXiv (Cornell University) (2018)

Sawon Pratiher Subhankar Chattoraj

Histopathological images (HI) encrypt resolution dependent heterogeneous textures & diverse color distribution variability, manifesting in micro-structural surface tissue convolutions. Also, inherently high coherency of cancerous cells poses significant challenges to breast cancer (BC) multi-classification. As such, multi-class stratification is sparsely explored & prior work mainly focus on benign & malignant tissue characterization only, which forestalls further quantitative analysis of subordinate classes like adenosis, mucinous carcinoma & fibroadenoma etc, for diagnostic competence. In this work, a fully-automated, near-real-time & computationally inexpensive robust multi-classification deep framework from HI is presented. The proposed scheme employs deep neural network (DNN) aided discriminative ensemble of holistic class-specific manifold learning (CSML) for underlying HI sub-space embedding & HI hashing based local shallow signatures. The model achieves 95.8% accuracy pertinent to multi-classification & 2.8% overall performance improvement & 38.2% enhancement for Lobular carcinoma (LC) sub-class recognition rate as compared to the existing state-of-the-art on well known BreakHis dataset is achieved. Also, 99.3% recognition rate at 200X & a sensitivity of 100% for binary grading at all magnification validates its suitability for clinical deployment in hand-held smart devices.

Autoencoder

Discriminative model

Source

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Multifractality in Parasympathetic-Sympathetic Responses and their Interactions: From 1/f Noise to Long-range Correlations

Sawon Pratiher Karuna P. Sahoo Pravallika Lavanuru Mrinal Acharya Nirmalya Ghosh

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

10.36227/techrxiv.22685032.v1

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Identification of ${S}_{\text{1}}$ and ${S}_{\text{2}}$ Heart Sounds Using Spectral and Convex Hull Features

IEEE Sensors Journal (2020)

Madhusudhan Mishra Sawon Pratiher Hrishikesh Menon Anirban Mukherjee

A new set of morphological characteristics of Phonocardiogram (PCG) signal is presented for recognition of first (S ₁ ) and second (S ₂ ) heart sounds (HSs). Initially, variational mode decomposition on PCG signal generates a set of amplitude and frequency modulated narrow band-limited components (NBCs) and Hilbert transformation of these NBCs comprehends its complex plane analytic signal representation (ASR). Instantaneous spectral attributes encompassing amplitude modulation bandwidths and convex hull area measure from the ASRs are concatenated to form the feature set. Experimental results on both publicly available and experimentally recorded HSs signals outperform the existing state-of-the-art. Also, the proposed technique does not require any timing information between S1 and S2 and electrocardiogram (ECG) signal reference and is highly robust to noisy real-world PCGs as shown by noise analysis.

Phonocardiogram

SIGNAL (programming language)

Complex plane

10.1109/jsen.2019.2962364

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Novel HRV Metrics using Analytic Signal Representation of Interbeat Intervals for New Insights on Cardiac Dynamics

Sawon Pratiher Apoorva Srivastava Mrinal Acharya Nirmalya Ghosh Amit Patra

This work has been submitted to the IEEE Journal of Biomedical and Health Informatics for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

Representation

SIGNAL (programming language)

Dynamics

10.36227/techrxiv.22350799

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

Physiological Measurement (2022)

Apoorva Srivastava Sawon Pratiher Sazedul Alam Ajith Hari Nilanjan Banerjee

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.

Overfitting

Lead (geology)

Abnormality

10.1088/1361-6579/ac6f40

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Citations (17)