Srijit Sengupta
Early-career ML researcher focused on ECG arrhythmia classification
Summary
Work
Education
Projects
Writing
Real-Time ECG Monitoring through IoMT with Attention-Enhanced Deep Learning Enabling Smart Cardiac Healthcare
January 1, 2026Study detailing a real-time ECG monitoring approach using IoMT and attention-enhanced deep learning for cardiac healthcare applications.
ECG Arrhythmia Classification: A Comprehensive Study Using Machine Learning and Deep Learning Techniques
June 1, 2025A study evaluating traditional machine learning models and deep learning architectures for ECG arrhythmia classification using the MIT-BIH Arrhythmia Database.
Deep Learning Models for Arrhythmia Classification using Stacked Time-frequency Scalogram Images from ECG Signals
October 1, 2023Work on applying stacked time-frequency scalogram images and deep learning models for arrhythmia classification from ECG signals.
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.
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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