Analysis of the impact of deep learning know-how and data in modelling neonatal EEG.

ABSTRACT: The performance gains achieved by deep learning models nowadays are mainly attributed to the usage of ever larger datasets. In this study, we present and contrast the performance gains that can be achieved via accessing larger high-quality datasets versus the gains that can be achieved from harnessing the latest deep learning architectural and training advances. Modelling neonatal EEG is particularly affected by the lack of publicly available large datasets. It is shown that greater performance gains can be achieved from harnessing the latest deep learning advances than using a larger training dataset when adopting AUC as a metric, whereas using AUC90 or AUC-PR as metrics greater performance gains are achieved from using a larger dataset than harnessing the latest deep learning advances. I

SUBMITTER: Daly A 

PROVIDER: S-EPMC11564755 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature