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Pilot study of a single-channel EEG seizure detection algorithm using machine learning

Authors
 Seungjun Ryu  ;  Seunghyeok Back  ;  Seongju Lee  ;  Hyeon Seo  ;  Chanki Park  ;  Kyoobin Lee  ;  Dong-Seok Kim 
Citation
 CHILDS NERVOUS SYSTEM, Vol.37(7) : 2239-2244, 2021-07 
Journal Title
CHILDS NERVOUS SYSTEM
ISSN
 0256-7040 
Issue Date
2021-07
MeSH
Algorithms ; Electroencephalography* ; Humans ; Infant, Newborn ; Machine Learning ; Pilot Projects ; Seizures* / diagnosis
Keywords
Electroencephalography ; Machine learning ; Principal component analysis ; Seizures
Abstract
Objective: Seizures are one of the most common emergencies in the neonatal intensive care unit (NICU). They are identified through visual inspection of electroencephalography (EEG) reports and treated by neurophysiologic experts. To support clinical seizure detection, several feature-based automatic neonatal seizure detection algorithms have been proposed. However, as they were unsuitable for clinical application due to their low accuracy, we developed a new seizure detection algorithm using machine learning for single-channel EEG to overcome these limitations.

Methods: The dataset applied in our algorithm contains EEG recordings from human neonates. A 19-channel EEG system recorded the brain waves of 79 term neonates admitted to the NICU at the Helsinki University Hospital. From these datasets, we selected six patients with conformational seizure annotations for the pilot study and allocated four and two patients for our training and testing datasets, respectively. The presence of seizures in the EEGs was annotated independently by three experts through visual interpretation. We divided the data into epochs of 5 s each and further defined a seizure block to label the annotations from each expert recorded every second. Subsequently, to create a balanced dataset, any data point with a non-seizure label was moved to the training and test dataset.

Result: The developed principal component feature-extracted machine learning algorithm used 62.5% of the relative time (only 5 s for decision) of the baseline, reaching an area under the ROC curve score of 0.91. The effect of diversified parameters was meticulously examined, and 100 principal components were extracted to optimize the model performance.

Conclusion: Our machine learning-based seizure detection algorithm exhibited the potential for clinical application in NICUs, general wards, and at home and proved its convenience by requiring only a single channel for implementation.
Full Text
https://link.springer.com/article/10.1007/s00381-020-05011-9
DOI
10.1007/s00381-020-05011-9
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Neurosurgery (신경외과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Dong Seok(김동석)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/190844
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