Department of Neurological surgery, University of California, San Francisco, California.
Department of Neurology, University of California, San Francisco, California.
Neurosurgery. 2018 Oct 1;83(4):683-691. doi: 10.1093/neuros/nyx480.
Interictal epileptiform discharges are an important biomarker for localization of focal epilepsy, especially in patients who undergo chronic intracranial monitoring. Manual detection of these pathophysiological events is cumbersome, but is still superior to current rule-based approaches in most automated algorithms.
To develop an unsupervised machine-learning algorithm for the improved, automated detection and localization of interictal epileptiform discharges based on spatiotemporal pattern recognition.
We decomposed 24 h of intracranial electroencephalography signals into basis functions and activation vectors using non-negative matrix factorization (NNMF). Thresholding the activation vector and the basis function of interest detected interictal epileptiform discharges in time and space (specific electrodes), respectively. We used convolutive NNMF, a refined algorithm, to add a temporal dimension to basis functions.
The receiver operating characteristics for NNMF-based detection are close to the gold standard of human visual-based detection and superior to currently available alternative automated approaches (93% sensitivity and 97% specificity). The algorithm successfully identified thousands of interictal epileptiform discharges across a full day of neurophysiological recording and accurately summarized their localization into a single map. Adding a temporal window allowed for visualization of the archetypal propagation network of these epileptiform discharges.
Unsupervised learning offers a powerful approach towards automated identification of recurrent pathological neurophysiological signals, which may have important implications for precise, quantitative, and individualized evaluation of focal epilepsy.
发作间期癫痫样放电是局灶性癫痫定位的一个重要生物标志物，尤其在接受慢性颅内监测的患者中。手动检测这些病理生理事件很繁琐，但在大多数自动化算法中，仍然优于当前基于规则的方法。
开发一种无监督机器学习算法，用于基于时空模式识别来改进、自动检测和定位发作间期癫痫样放电。
我们使用非负矩阵分解（NNMF）将 24 小时颅内脑电图信号分解为基函数和激活向量。对激活向量和感兴趣的基函数进行阈值处理，分别在时间和空间（特定电极）上检测发作间期癫痫样放电。我们使用卷积 NNMF（一种改进的算法）为基函数添加时间维度。
基于 NNMF 的检测的接收者操作特征接近基于人类视觉的检测的金标准，优于目前可用的替代自动化方法（敏感性为 93%，特异性为 97%）。该算法成功识别了全天神经生理学记录中的数千次发作间期癫痫样放电，并准确地将其定位总结为单个图谱。添加时间窗口可可视化这些癫痫样放电的典型传播网络。
无监督学习为自动识别反复发作的病理生理信号提供了一种强大的方法，这可能对精确、定量和个体化评估局灶性癫痫具有重要意义。
