Spindle Density Analysis of Adult Epilepsy based on Automatic Detection Algorithms in EEG
Authors: 
Yajin Huang, Yanjun Liu, Junqiang Li, Yan Yue, Yaqing Liu, Tiancheng WangAuthors Info & Claims
Yajin Huang, Yanjun Liu, Junqiang Li, Yan Yue, Yaqing Liu, Tiancheng WangAuthors Info & ClaimsPages 463 - 467
Abstract
Electrophysiological investigations of sleep provide an important advantage for recording spontaneous neural activity and quantifying brain function. This study combined artificial intelligence technology to quantitatively analyze spindle density in adult patients with epilepsy. All patients received one-night sleep electroencephalogram monitoring. We employed a convolutional neural network-based sleep staging system to predict sleep macrostructure. Then we applied two species of advanced algorithms: Spindler and Latent state spindle detector, to automatically detect sleep spindle during non-rapid eye movement sleep stage 2. And we calculated three kinds of frequency range spindle density involving 11-17 Hz, 9-12 Hz, and 12-15 Hz. Our results suggested that 11-17 Hz and 12-15 Hz spindle density in adult epilepsy predominated in parietal and 9-12 Hz spindle density in prefrontal regions.
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Published In
October 2022
594 pages
ISBN:9781450398442
DOI:10.1145/3570773
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Published: 09 December 2022
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ISAIMS 2022
ISAIMS 2022: 2022 3rd International Symposium on Artificial Intelligence for Medicine Sciences
October 13 - 15, 2022
Amsterdam, Netherlands
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