Ryu, Seungjun Back, Seunghyeok Lee, Seongju Seo, Hyeon Park, Chanki Lee, Kyoobin Kim, Dong-Seok 2024-01-19T14:04:07Z 2024-01-19T14:04:07Z 2022-01-25 2021-07 0256-7040 https://pubs.kist.re.kr/handle/201004/116703 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. English SPRINGER Pilot study of a single-channel EEG seizure detection algorithm using machine learning Article 10.1007/s00381-020-05011-9 1 CHILDS NERVOUS SYSTEM, v.37, no.7, pp.2239 - 2244 CHILDS NERVOUS SYSTEM 37 7 2239 2244 N scie scopus 000646507100001 2-s2.0-85105405395 Clinical Neurology Pediatrics Surgery Neurosciences & Neurology Pediatrics Surgery Article MODEL Machine learning Seizures Electroencephalography Principal component analysis