Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:259-262. doi: 10.1109/EMBC48229.2022.9871595.
Seizures frequently occur in paediatric emergency and critical care, with up to 74% being sub-clinical seizures making detection difficult. Delays in seizure detection and treatment worsen the neurological outcome of critically-ill patients. Gold-standard seizure detections using multi-channels electroencephalograms (EEG) require trained clinical physiologists to apply scalp electrodes and highly specialised neurologists to interpret and identify seizures. In this study, we extracted phase synchrony and cross-channel coherence amplitude across 4 and 8 pre-selected scalp EEG signals. Binary classification is used to determine whether the signal segment is seizure or non-seizure, and the predictions were compared against the gold-standard seizure onset markings. The application of the algorithm on a cohort of forty routinely collected EEGs from paediatric patients showed an average accuracy of 77.2 % and 76.5% using 4 and 8 channels, respectively. Clinical Relevance- This work demonstrates the feasibility of seizure detection with pre-defined 4 and 8 EEG electrodes with an average accuracy of 77%. This means for the first time seizure detection is possible using an EEG montage that can be applied readily at the bedside independent of expert input.
癫痫发作在儿科急诊和重症监护中很常见，多达 74%的癫痫发作是亚临床发作，这使得癫痫发作的检测变得困难。癫痫发作检测和治疗的延迟会使危重症患者的神经预后恶化。使用多通道脑电图（EEG）进行的金标准癫痫发作检测需要经过培训的临床生理学家来应用头皮电极，以及高度专业的神经科医生来解释和识别癫痫发作。在这项研究中，我们提取了 4 个和 8 个预选头皮 EEG 信号之间的相位同步和跨通道相干幅度。使用二进制分类来确定信号段是否是癫痫发作或非癫痫发作，并将预测结果与金标准的癫痫发作起始标记进行比较。该算法应用于一组 40 例常规收集的儿科患者 EEG 中，平均准确率分别为 77.2%和 76.5%，使用 4 个和 8 个通道。临床相关性-这项工作证明了使用预定义的 4 个和 8 个 EEG 电极进行癫痫发作检测的可行性，平均准确率为 77%。这意味着，首次可以使用一种脑电图监测模式进行癫痫发作检测，该模式可以在床边独立于专家输入而易于应用。
