The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, 30 Royal Parade, Parkville, VIC 3052, Australia; University of Queensland Centre for Clinical Research, The University of Queensland, Building 71/918, Royal Brisbane & Women's Hospital Campus, Herston, QLD 4029, Australia.
Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University, PO Box 33 PC 123, Al-Khoud, Muscat, Oman; University of Queensland Centre for Clinical Research, The University of Queensland, Building 71/918, Royal Brisbane & Women's Hospital Campus, Herston, QLD 4029, Australia.
Comput Methods Programs Biomed. 2022 Sep;224:107014. doi: 10.1016/j.cmpb.2022.107014. Epub 2022 Jul 9.
In newborns, it is often difficult to accurately differentiate between seizure and non-seizure based solely on clinical manifestations. This highlights the importance of electroencephalogram (EEG) in the recognition and management of neonatal seizures. This paper proposes an effective algorithm for the detection of neonatal seizure using multichannel EEG.
Neonatal EEG changes morphology as it alternates between seizure and non-seizure states. A new signal complexity measure based on matching pursuit (MP) decomposition is proposed and used to detect transitions between these two states. The new measure, referred to as weighted structural complexity (WSC), was used for the detection of seizures in 30 newborn EEG records. Multiple IIR filters and an MP-based filter were designed and used to remove artifacts from the EEG data. Geometrical correlation between the EEG data channels was applied to reduce the number of false detections caused by remnant artifacts. The seizure detector's performance was assessed using several epoch-based (e.g., accuracy) and event-based (GDR = good detection rate and FD/h = false detections per hour) metrics.
Compared to the neurologist marking, the proposed detector was able to detect EEG seizures with 94% accuracy, 90.9% GDR, and 0.14 FD/h (95% CI: [0.06, 0.34]).
The high performance of the MP-based detector may have significant implications for the accurate diagnosis of neonatal seizures and the appropriate use of anticonvulsants and ongoing clinical assessment and care of the newborn.
在新生儿中，仅凭临床表现往往难以准确区分癫痫发作和非癫痫发作。这凸显了脑电图（EEG）在识别和管理新生儿癫痫中的重要性。本文提出了一种使用多通道 EEG 检测新生儿癫痫的有效算法。
新生儿 EEG 在癫痫发作和非癫痫发作状态之间交替时会改变形态。提出了一种基于匹配追踪（MP）分解的新信号复杂度度量方法，用于检测这两种状态之间的转换。新的度量方法称为加权结构复杂度（WSC），用于检测 30 例新生儿 EEG 记录中的癫痫发作。设计了多个 IIR 滤波器和基于 MP 的滤波器，用于去除 EEG 数据中的伪影。应用 EEG 数据通道之间的几何相关性来减少由残余伪影引起的假阳性检测次数。使用基于epoch 的（例如，准确性）和基于事件的（GDR=良好检测率和 FD/h=每小时假阳性检测数）指标评估了癫痫发作检测器的性能。
与神经科医生标记相比，所提出的检测器能够以 94%的准确率、90.9%的 GDR 和 0.14 的 FD/h（95%CI：[0.06, 0.34]）检测 EEG 癫痫发作。
MP 基检测器的高性能可能对新生儿癫痫的准确诊断以及抗癫痫药物的合理使用以及对新生儿的持续临床评估和护理具有重要意义。
