9 citations · 29 across the 7 of their papers we have counts for
5 papers · 1 filter
Disability prediction in multiple sclerosis using performance outcome measures and demographic data
Subhrajit Roy, Diana Mincu, Lev Proleev +8
Literature on machine learning for multiple sclerosis has primarily focused on the use of neuroimaging data such as magnetic resonance imaging and clinical laboratory tests for dis…
ML4H Abstract Track 2020
Emily Alsentzer, Matthew B. A. McDermott, Fabian Falck +3
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) workshop at NeurIPS 2020. This index is not complete, as some accepted abstracts chose to opt-out…
A semi-supervised deep learning algorithm for abnormal EEG identification
Subhrajit Roy, Kiran Kate, Martin Hirzel
Systems that can automatically analyze EEG signals can aid neurologists by reducing heavy workload and delays. However, such systems need to be first trained using a labeled datase…
SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification
Umar Asif, Subhrajit Roy, Jianbin Tang +1
Automatic classification of epileptic seizure types in electroencephalograms (EEGs) data can enable more precise diagnosis and efficient management of the disease. This task is cha…
Seizure Type Classification using EEG signals and Machine Learning: Setting a benchmark
Subhrajit Roy, Umar Asif, Jianbin Tang +1
Accurate classification of seizure types plays a crucial role in the treatment and disease management of epileptic patients. Epileptic seizure types not only impact the choice of d…