Detection of mesial temporal lobe epileptiform discharges on intracranial electrodes using deep learning.

Journal: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology

PMID: 31760212

OBJECTIVE: Develop a high-performing algorithm to detect mesial temporal lobe (mTL) epileptiform discharges on intracranial electrode recordings.

Maurice Abou Jaoude

Epilepsy Division, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Jin Jing

Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
Haoqi Sun

Neurology Department, Massachusetts General Hospital, Wang 720, Boston, MA, USA.
Claire S Jacobs

Epilepsy Division, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Kyle R Pellerin

Epilepsy Division, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
M Brandon Westover

Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts.
Sydney S Cash

Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
Alice D Lam

Epilepsy Division, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. Electronic address: Lam.Alice@mgh.harvard.edu.

Adult Algorithms Area Under Curve Artifacts Datasets as Topic Deep Learning Electrocorticography Electrodes, Implanted Epilepsy, Temporal Lobe Female Foramen Ovale Humans Male Reference Standards ROC Curve Sensitivity and Specificity Temporal Lobe

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