9 citations · 29 across the 7 of their papers we have counts for
5 papers · 1 filter
Type-Driven Automated Learning with Lale
Martin Hirzel, Kiran Kate, Avraham Shinnar +2
Machine-learning automation tools, ranging from humble grid-search to hyperopt, auto-sklearn, and TPOT, help explore large search spaces of possible pipelines. Unfortunately, each…
Machine Learning for removing EEG artifacts: Setting the benchmark
Subhrajit Roy
Electroencephalograms (EEG) are often contaminated by artifacts which make interpreting them more challenging for clinicians. Hence, automated artifact recognition systems have the…
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…