Author

Raman Sankar

University of California, Los AngelesORCID 0000-0002-5609-0362Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Neuroscience· 3

Signature Assays, Organisms & Methods

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Assay:electronic health records· 3 Approach:deep learning· 2 Research Focus:differential expression· 2 Organism:human· 2 Research Focus:cell type annotation· 1 Approach:statistical model· 1

Frequent Co-Authors

Hiroki NariaiiD· 3 shared Richard J. StabaiD· 3 shared Shaun A. HussainiD· 3 shared Aria FallahiD· 2 shared Eishi AsanoiD· 2 shared Jerome EngeliD· 2 shared Tonmoy MonsooriD· 2 shared Vwani RoychowdhuryiD· 2 shared

Activity by Year

2020-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersNeuroscience

Hiroki NariaiORCID

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Author profile →

3 papers

Recent·Most cited

PyHFO: Lightweight Deep Learning-poweredEnd-to-End High-Frequency Oscillations AnalysisApplication

Yipeng Zhang, Lawrence Liu, Yuanyi Ding, Xin Chen, Tonmoy Monsoor, Atsuro Daida, Shingo Oana, Shaun A. Hussain, Raman Sankar, Aria Fallah, Cesar Santana‐Gomez, Jerome Engel, Richard J. Staba, William F Speier, Jianguo Zhang, , Vwani Roychowdhury

PyHFO is a lightweight deep learning platform for efficient HFO detection in EEG data, achieving 50x speed improvements over standard methods.

Journal of Neural Engineering · May 9, 2024Peer-reviewedNeuroscience

Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach

Yipeng Zhang, Qiujing Lu, Tonmoy Monsoor, Shaun A. Hussain, Joe X Qiao, Noriko Salamon, Aria Fallah, Myung‐Shin Sim, Eishi Asano, Raman Sankar, Richard J. Staba, Jerome Engel, William F Speier, Vwani Roychowdhury, Hiroki Nariai

This paper develops a deep learning algorithm to detect and classify epileptogenic high-frequency oscillations (eHFOs) from intracranial EEG data with high accuracy.

Brain Communications · Nov 1, 2021Peer-reviewedNeuroscience

Scalp EEG interictal high frequency oscillations as an objective EEG biomarker of infantile spasms

Hiroki Nariai, Shaun A. Hussain, Danilo Bernardo, Hirotaka Motoi, Masaki Sonoda, Naoto Kuroda, Eishi Asano, Jimmy C. Nguyen, David A. Elashoff, Raman Sankar, Anatol Bragin, Richard J. Staba, Joyce Y. Wu

This study shows that scalp EEG high frequency oscillations (HFOs) and their coupling with slow-wave activity correlate with active infantile spasms and can predict them better than hypsarrhythmia.

Clinical Neurophysiology · Nov 1, 2020Peer-reviewedNeuroscience