TY - GEN
T1 - Automated Detection of High Frequency Oscillations in Human Scalp Electroencephalogram
AU - Charupanit, Krit
AU - Nunez, Michael D.
AU - Bernardo, Danilo
AU - Bebin, Martina
AU - Krueger, Darcy A.
AU - Northrup, Hope
AU - Sahin, Mustafa
AU - Wu, Joyce Y.
AU - Lopour, Beth A.
N1 - Funding Information:
ACKNOWLEDGMENT Research reported in this publication was supported by the National Institute of Neurological Disorders And Stroke of the National Institutes of Health under Award Number U01NS082320. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We are sincerely indebted to the generosity of the families and patients in TSC clinics across the United States who contributed their time and effort to this study. We would like to thank the Tuberous Sclerosis Alliance for their continued support in TSC research. We also thank Tian Lan for starting this study.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/26
Y1 - 2018/10/26
N2 - High frequency oscillations (HFOs) > 80 Hz are a promising biomarker of epileptic tissue. Recent evidence has shown that spontaneous HFOs can be recorded from the scalp, but detection of these electrographic events remains a challenge. Here, we modified a simple automatic detector, used originally for intracranial EEG (iEEG) recordings, to detect ripples and fast ripples in scalp EEG. We analyzed scalp EEG recordings of seven subjects and validated our detector and artifact rejection algorithm via visual review. Of the candidate events marked by the detector, 40% and 60% were confirmed to be ripples and fast ripples, respectively, by human visual review, making this algorithm suitable for supervised detection. Detected HFOs occurred at a rate of <1/min in most channels, and the average duration was 47 and 24 ms for ripples and fast ripples, respectively. The simplicity of the algorithm, with only a single parameter, enables the consistent application of automatic detection across recording modalities, and it could therefore be a tool for the assessment and localization of epileptic activity.
AB - High frequency oscillations (HFOs) > 80 Hz are a promising biomarker of epileptic tissue. Recent evidence has shown that spontaneous HFOs can be recorded from the scalp, but detection of these electrographic events remains a challenge. Here, we modified a simple automatic detector, used originally for intracranial EEG (iEEG) recordings, to detect ripples and fast ripples in scalp EEG. We analyzed scalp EEG recordings of seven subjects and validated our detector and artifact rejection algorithm via visual review. Of the candidate events marked by the detector, 40% and 60% were confirmed to be ripples and fast ripples, respectively, by human visual review, making this algorithm suitable for supervised detection. Detected HFOs occurred at a rate of <1/min in most channels, and the average duration was 47 and 24 ms for ripples and fast ripples, respectively. The simplicity of the algorithm, with only a single parameter, enables the consistent application of automatic detection across recording modalities, and it could therefore be a tool for the assessment and localization of epileptic activity.
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U2 - 10.1109/EMBC.2018.8513033
DO - 10.1109/EMBC.2018.8513033
M3 - Conference contribution
C2 - 30441054
AN - SCOPUS:85056566216
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3116
EP - 3119
BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Y2 - 18 July 2018 through 21 July 2018
ER -