Author

Amir H. Ansari

KU LeuvenORCID 0000-0003-3261-0404Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:electronic health records· 2 Organism:human· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

A DereymaekeriD· 1 shared Alexander BertrandiD· 1 shared Aomesh BhattiD· 1 shared Caroline HartleyiD· 1 shared Emad ArastehiD· 1 shared Gabriela Schmidt MelladoiD· 1 shared Gunnar NaulaersiD· 1 shared Katrien JanseniD· 1 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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1 shared paperNeuroscience

A DereymaekerORCID

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2 papers

Recent·Most cited

NeurosciencebioRxivPreprintJan 24, 2023

Brain age as an estimator of neurodevelopmental outcome: A deep learning approach for neonatal cot-side monitoring

Amir H. Ansari, Kirubin Pillay, Luke Baxter…·Rebeccah Slater

This paper develops a deep learning model to estimate brain age from neonatal EEG signals, achieving high accuracy with short recordings and showing correlation with neurodevelopmental outcomes.

6 citations

humanelectronic health records

𝕏 PostBluesky

NeuroscienceeLifePeer-reviewedApr 30, 2021

EEG-based detection of the locus of auditory attention with convolutional neural networks

Servaas Vandecappelle, Lucas Deckers, Neetha Das…·Tom Francart

This study uses convolutional neural networks to detect auditory attention locus (left/right) from EEG signals in multi-speaker scenarios, achieving 81% median accuracy within 1-2 seconds.

152 citationstop 1%

humanelectronic health records

𝕏 PostBluesky