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Srijit Sengupta

Srijit Sengupta

Early-career ML researcher focused on ECG arrhythmia classification

srijit
India
47 connections
Joined February 2026
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Summary

Focused early-career researcher in machine learning for biomedical signal processing, especially automated ECG arrhythmia classification. researchgate+2
Active in both academic research and student-led technical organizations, combining institutional research roles with leadership/curation positions. signalhire
Maintains an academic publication record and public researcher profiles (ResearchGate, Google Scholar) tied to Vellore Institute of Technology student affiliation. researchgate+1
Has a mix of internship and early professional research experiences, including a summer internship at the Indian Institute of Remote Sensing and research affiliation with Lehigh University. signalhire

Work

Education

Projects

Writing

Real-Time ECG Monitoring through IoMT with Attention-Enhanced Deep Learning Enabling Smart Cardiac Healthcare

January 1, 2026

Study detailing a real-time ECG monitoring approach using IoMT and attention-enhanced deep learning for cardiac healthcare applications.

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ECG Arrhythmia Classification: A Comprehensive Study Using Machine Learning and Deep Learning Techniques

June 1, 2025

A study evaluating traditional machine learning models and deep learning architectures for ECG arrhythmia classification using the MIT-BIH Arrhythmia Database.

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Deep Learning Models for Arrhythmia Classification using Stacked Time-frequency Scalogram Images from ECG Signals

October 1, 2023

Work on applying stacked time-frequency scalogram images and deep learning models for arrhythmia classification from ECG signals.

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ECG Arrhythmia Classification: A Comprehensive Analysis Using Traditional Machine Learning and Deep Neural Networks

Analysis comparing traditional machine learning methods and deep neural networks for ECG arrhythmia classification.

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Multiclass Arrhythmia Classification from Imbalanced ECG Data Using Encoded Transformer-based CNN-LSTM Hybrid Model

Research on improving multiclass arrhythmia classification from imbalanced ECG datasets using hybrid transformer and CNN-LSTM models.

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