2.7k citations · 3.9k across the 27 of their papers we have counts for
27 papers
NeuroStrata: An Electroencephalographic Connectivity-Aware Deep Representation Learning Framework for Dynamic Brain Network Analysis of Mental Stress
Sayantan Acharya, Hamzeh Asgharnezhad, Abbas Khosravi +3
This study introduces NeuroStrata, a connectivity-aware deep representation learning framework for EEG-based mental stress analysis using Time-Varying Partial Directed Coherence (T…
UCTECG-Net: Uncertainty-aware Convolution Transformer ECG Network for Arrhythmia Detection
Hamzeh Asgharnezhad, Pegah Tabarisaadi, Abbas Khosravi +2
Deep learning has improved automated electrocardiogram (ECG) classification, but limited insight into prediction reliability hinders its use in safety-critical settings. This paper…
SSI-GAN: Semi-Supervised Swin-Inspired Generative Adversarial Networks for Neuronal Spike Classification
Danial Sharifrazi, Nouman Javed, Mojtaba Mohammadi +5
Mosquitos are the main transmissive agents of arboviral diseases. Manual classification of their neuronal spike patterns is very labor-intensive and expensive. Most available deep…
Rewiring Human Brain Networks via Lightweight Dynamic Connectivity Framework: An EEG-Based Stress Validation
Sayantan Acharya, Abbas Khosravi, Douglas Creighton +2
In recent years, Electroencephalographic analysis has gained prominence in stress research when combined with AI and Machine Learning models for validation. In this study, a lightw…
Exploring the Relationships Between Physiological Signals During Automated Fatigue Detection
Kourosh Kakhi, Abbas Khosravi, Roohallah Alizadehsani +1
Fatigue detection using physiological signals is critical in domains such as transportation, healthcare, and performance monitoring. While most studies focus on single modalities,…
A Quad-Step Approach to Uncertainty-Aware Deep Learning for Skin Cancer Classification
Hamzeh Asgharnezhad, Pegah Tabarisaadi, Abbas Khosravi +2
Accurate skin cancer diagnosis is vital for early treatment and improved patient outcomes. Deep learning (DL) models have shown promise in automating skin cancer classification, ye…