Ruiz Marín Manuel, Villegas Martínez Irene, Rodríguez Bermúdez Germán, Porfiri Maurizio
Department of Quantitative Methods, Law and Modern Languages, Technical University of Cartagena (UPCT), Cartagena, Murcia 30201, Spain.
Bio-Health Institute (IMIB-Arrixaca), Health Science Campus, Murcia, CP 30120, Spain.
iScience. 2020 Dec 28;24(1):101997. doi: 10.1016/j.isci.2020.101997. eCollection 2021 Jan 22.
Automated seizure detection in long-term video-EEG recordings is far from being integrated into common clinical practice. Here, we leverage classical and state-of-the-art complexity measures to robustly and automatically detect seizures from scalp recordings. Brain activity is scored through eight features, encompassing traditional time domain and novel measures of recurrence. A binary classification algorithm tailored to treat unbalanced dataset is used to determine whether a time window is ictal or non-ictal from its features. The application of the algorithm on a cohort of ten adult patients with focal refractory epilepsy indicates sensitivity, specificity, and accuracy of 90%, along with a true alarm rate of 95% and less than four false alarms per day. The proposed approach emphasizes ictal patterns against noisy background without the need of data preprocessing. Finally, we benchmark our approach against previous studies on two publicly available datasets, demonstrating the good performance of our algorithm.
长期视频脑电图记录中的自动癫痫发作检测远未融入常规临床实践。在此，我们利用经典和最新的复杂性度量方法，从头皮记录中稳健且自动地检测癫痫发作。通过八个特征对脑电活动进行评分，这些特征涵盖传统时域特征和新的递归度量方法。使用一种专门针对不平衡数据集处理的二元分类算法，根据其特征确定一个时间窗口是发作期还是非发作期。该算法在一组十名患有局灶性难治性癫痫的成年患者中的应用显示，灵敏度、特异性和准确率均为90%，真阳性率为95%，且每天误报少于四次。所提出的方法强调在有噪声背景下的发作期模式，而无需数据预处理。最后，我们将我们的方法与之前在两个公开可用数据集上的研究进行基准测试，证明了我们算法的良好性能。
