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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
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Posts
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Blog Post number 4
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Blog Post number 1
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portfolio
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publications
A Novel Deep Learning-Based Approach for Hypertension Level Detection Using PPG
Published in IEEE SILCON 2023, 2023
LSTM regression models and an attention-based TLSTM classifier predict blood pressure and hypertension stage from PPG readings, reaching 96% classification accuracy.
Recommended citation: De, S., Mukherjee, P., & Halder Roy, A. (2023). "A Novel Deep Learning-Based Approach for Hypertension Level Detection Using PPG." IEEE SILCON.
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A Hybrid Pain Assessment Approach with Stacked Autoencoders and Attention-Based CP-LSTM
Published in IEEE AIKIIE 2023, 2023
A Stacked Autoencoder plus attention-based Chebyshev polynomial LSTM (CP-LSTM) classifies pain versus no-pain states from EEG signals with over 99% accuracy.
Recommended citation: De, S., Mukherjee, P., & Halder Roy, A. (2023). "A Hybrid Pain Assessment Approach with Stacked Autoencoders and Attention-Based CP-LSTM." IEEE AIKIIE.
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A Novel Human Stress Level Detection Technique Using EEG
Published in IEEE NMITCON 2023, 2023
A majority-voting ensemble of SVM, KNN, and Naive Bayes classifiers detects four levels of mental stress from frontal-lobe EEG features with 93.85% accuracy.
Recommended citation: Konar, D., De, S., Mukherjee, P., & Halder Roy, A. (2023). "A Novel Human Stress Level Detection Technique Using EEG." IEEE NMITCON.
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SLiTRANet: An EEG-Based Automated Diagnosis Framework for Major Depressive Disorder Monitoring Using a Novel LGCN and Transformer-Based Hybrid Deep Learning Approach
Published in IEEE Access, 2024
Recommended citation: S. De, A. Singh, V. Tiwari, H. Patel, G. N. Vivekananda and D. Singh Rajput, "SLiTRANet: An EEG-Based Automated Diagnosis Framework for Major Depressive Disorder Monitoring Using a Novel LGCN and Transformer-Based Hybrid Deep Learning Approach," IEEE Access, vol. 12, pp. 173109-173126, 2024
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Maestro: A Robust Multi-Head Attention Enhanced CNN Architecture for Heat-Induced Stress Recognition Using EEG Signals
Published in IEEE CSITSS 2024, 2024
MAESTRO combines convolutional and multi-head attention blocks to classify heat-induced stress from EEG into Acute, Chronic, and Control categories with 98.88% accuracy.
Recommended citation: De, S., Pavuluri, S., Sayyad, A., & Gupta, A. K. (2024). "Maestro: A Robust Multi-Head Attention Enhanced CNN Architecture for Heat-Induced Stress Recognition Using EEG Signals." IEEE CSITSS.
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ParViT: A modified Vision Transformer architecture for Parkinson’s Disease identification using EEG signals
Published in IEEE ICSSES 2024, 2024
ParViT applies STFT-based time-frequency features to a modified Vision Transformer for Parkinson’s Disease identification from EEG, reaching 98.25% accuracy.
Recommended citation: De, S., Sayyad, A., Kotian, H., & Gupta, A. K. (2024). "ParViT: A modified Vision Transformer architecture for Parkinson's Disease identification using EEG signals." IEEE ICSSES.
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A Quantum Machine Learning framework for Driver Drowsiness Detection using Biopotential Signals and Head Movement Analysis
Published in IEEE ICWITE 2024, 2024
A CNN + Attention-based Quantum LSTM hybrid model that detects driver drowsiness levels from EEG, facial EMG, pulse rate, and head movement with up to 99% accuracy.
Recommended citation: De, S., & Gupta, A. K. (2024). "A Quantum Machine Learning framework for Driver Drowsiness Detection using Biopotential Signals and Head Movement Analysis." IEEE ICWITE.
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A Novel Vision Transformer based Multimodal Fusion Approach for Clinical MDD Diagnosis Using EEG and Audio Signals
Published in IEEE Transactions on Computational Biology and Bioinformatics, 2025
Recommended citation: S. De, A. Singh and A. K. Bhandari, "A Novel Vision Transformer Based Multimodal Fusion Approach for Clinical MDD Diagnosis Using EEG and Audio Signals," IEEE Transactions on Computational Biology and Bioinformatics, vol. 22, no. 6, pp. 3399-3409, Nov.-Dec. 2025
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GLEAM: A Multimodal Deep Learning Framework for Chronic Lower Back Pain Detection Using EEG and sEMG Signals
Published in Computers in Biology and Medicine, 2025
Recommended citation: S. De, P. Mukherjee, A. H. Roy, "GLEAM: A multimodal deep learning framework for chronic lower back pain detection using EEG and sEMG signals," Computers in Biology and Medicine, Volume 189, 2025, 109928, ISSN 0010-4825
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TasteNet: A Novel Deep Learning Approach for EEG-Based Basic Taste Perception Recognition Using CEEMDAN Domain Entropy Features
Published in Journal of Neuroscience Methods, 2025
Recommended citation: S. De, P. Mukherjee, A. H. Roy, "TasteNet: A novel deep learning approach for EEG-based basic taste perception recognition using CEEMDAN domain entropy features," Journal of Neuroscience Methods, Volume 419, 2025, 110463, ISSN 0165-0270
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Identification of patients with de novo Parkinson’s Disease from chemosensory EEG signals using ICEEMDAN domain Entropy Features
Published in IEEE Sensors Letters, 2025
Recommended citation: S. De, S. Pavuluri and A. K. Gupta, "Identification of patients with de novo Parkinson's Disease from chemosensory EEG signals using ICEEMDAN domain Entropy Features," IEEE Sensors Letters, vol. 9, no. 6, pp. 1-4, June 2025, Art no. 7002804
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Quantifying the Impact of Speaker and Content Features on ASR Systems Using Unsupervised Distance Metrics
Published in IEEE Sensors Reviews, 2025
Recommended citation: S. Pavuluri, S. De and A. K. Gupta, "Quantifying the Impact of Speaker and Content Features on ASR Systems Using Unsupervised Distance Metrics," IEEE Sensors Reviews, vol. 2, pp. 170-178, 2025
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Awards & Honours
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