3 papers
q-bio.NC2026
Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts
Christelle Schneuwly Diaz, Narmina Baghirova, Duy-Thanh Vu +4
Accurate diagnostic classification and disease-severity prediction for Alzheimer's disease are hampered by the incompleteness and heterogeneity of real-world clinical data. Left un…
eess.SP2024
SzCORE: A Seizure Community Open-source Research Evaluation framework for the validation of EEG-based automated seizure detection algorithms
Jonathan Dan, Una Pale, Alireza Amirshahi +9
The need for high-quality automated seizure detection algorithms based on electroencephalography (EEG) becomes ever more pressing with the increasing use of ambulatory and long-ter…
eess.SP2023
EpiDeNet: An Energy-Efficient Approach to Seizure Detection for Embedded Systems
Thorir Mar Ingolfsson, Upasana Chakraborty, Xiaying Wang +6
Epilepsy is a prevalent neurological disorder that affects millions of individuals globally, and continuous monitoring coupled with automated seizure detection appears as a necessi…