102 citations · 148 across the 10 of their papers we have counts for
10 papers
Beat-Synchronous Tokenization for ECG Transformers
Ahmed Sameh, Nolan Wilson, Max Enderlein +1
Transformer-based electrocardiogram (ECG) models commonly tokenize waveforms into fixed temporal patches. Though convenient, fixed patching can split heartbeat structures across to…
The Impact of Temporal Context Length and Encoding Strategies on Self-Supervised ECG Representation Learning
Ahmed Sameh, Ramzi Al-Sharawi, Yogatheesan Varatharajah
Self-supervised electrocardiogram (ECG) models are often trained on a few seconds of ECG signal and, increasingly, on discretized token sequences. It remains unclear whether these…
Improving Out-of-distribution Human Activity Recognition via IMU-Video Cross-modal Representation Learning
Seyyed Saeid Cheshmi, Buyao Lyu, Thomas Lisko +3
Human Activity Recognition (HAR) based on wearable inertial sensors plays a critical role in remote health monitoring. In patients with movement disorders, the ability to detect ab…
EEG Foundation Models: A Critical Review of Current Progress and Future Directions
Gayal Kuruppu, Neeraj Wagh, Vaclav Kremen +3
Premise. Patterns of electrical brain activity recorded via electroencephalography (EEG) offer immense value for scientific and clinical investigations. The inability of supervised…
Tensor Decomposition of Large-scale Clinical EEGs Reveals Interpretable Patterns of Brain Physiology
Teja Gupta, Neeraj Wagh, Samarth Rawal +3
Identifying abnormal patterns in electroencephalography (EEG) remains the cornerstone of diagnosing several neurological diseases. The current clinical EEG review process relies he…
Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts
Neeraj Wagh, Jionghao Wei, Samarth Rawal +2
The recent availability of large datasets in bio-medicine has inspired the development of representation learning methods for multiple healthcare applications. Despite advances in…
SCORE-IT: A Machine Learning-based Tool for Automatic Standardization of EEG Reports
Samarth Rawal, Yogatheesan Varatharajah
Machine learning (ML)-based analysis of electroencephalograms (EEGs) is playing an important role in advancing neurological care. However, the difficulties in automatically extract…
EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network
Neeraj Wagh, Yogatheesan Varatharajah
This paper presents a novel graph convolutional neural network (GCNN)-based approach for improving the diagnosis of neurological diseases using scalp-electroencephalograms (EEGs).…
Integrating Artificial Intelligence with Real-time Intracranial EEG Monitoring to Automate Interictal Identification of Seizure Onset Zones in Focal Epilepsy
Yogatheesan Varatharajah, Brent Berry, Jan Cimbalnik +6
An ability to map seizure-generating brain tissue, i.e., the seizure onset zone (SOZ), without recording actual seizures could reduce the duration of invasive EEG monitoring for pa…
A Contextual-bandit-based Approach for Informed Decision-making in Clinical Trials
Yogatheesan Varatharajah, Brent Berry, Sanmi Koyejo +1
Clinical trials involving multiple treatments utilize randomization of the treatment assignments to enable the evaluation of treatment efficacies in an unbiased manner. Such evalua…