Laboratory of Image, Signal and Telecommunication Devices-LIST, CP165/51, Université Libre de Bruxelles-ULB, Avenue F. Roosevelt 50, 1050 Brussels, Belgium.
J Neurosci Methods. 2012 Sep 30;210(2):259-65. doi: 10.1016/j.jneumeth.2012.07.015. Epub 2012 Jul 28.
Visual quantification of interictal epileptiform activity is time consuming and requires a high level of expert's vigilance. This is especially true for overnight recordings of patient suffering from epileptic encephalopathy with continuous spike and waves during slow-wave sleep (CSWS) as they can show tens of thousands of spikes. Automatic spike detection would be attractive for this condition, but available algorithms have methodological limitations related to variation in spike morphology both between patients and within a single recording. We propose a fully automated method of interictal spike detection that adapts to interpatient and intrapatient variation in spike morphology. The algorithm works in five steps. (1) Spikes are detected using parameters suitable for highly sensitive detection. (2) Detected spikes are separated into clusters. (3) The number of clusters is automatically adjusted. (4) Centroids are used as templates for more specific spike detections, therefore adapting to the types of spike morphology. (5) Detected spikes are summed. The algorithm was evaluated on EEG samples from 20 children suffering from epilepsy with CSWS. When compared to the manual scoring of 3 EEG experts (3 records), the algorithm demonstrated similar performance since sensitivity and selectivity were 0.3% higher and 0.4% lower, respectively. The algorithm showed little difference compared to the manual scoring of another expert for the spike-and-wave index evaluation in 17 additional records (the mean absolute difference was 3.8%). This algorithm is therefore efficient for the count of interictal spikes and determination of a spike-and-wave index.
棘波的视觉量化既耗时又需要专家高度警惕，这在患有癫痫性脑病且睡眠慢波中有连续棘波和尖波（CSWS）的患者的夜间记录中尤其如此，因为它们可能显示数万次棘波。对于这种情况，自动棘波检测很有吸引力，但现有的算法在棘波形态的个体间和个体内变异性方面存在方法学限制。我们提出了一种完全自动化的棘波检测方法，该方法适应棘波形态的个体间和个体内变化。该算法分五个步骤工作。（1）使用适合高度敏感检测的参数检测棘波。（2）将检测到的棘波分离成簇。（3）自动调整簇的数量。（4）将质心用作更具体的棘波检测的模板，从而适应棘波形态的类型。（5）检测到的棘波被求和。该算法在 20 名患有 CSWS 的癫痫儿童的 EEG 样本上进行了评估。与 3 位 EEG 专家的手动评分（3 个记录）相比，该算法的性能相似，因为敏感性和选择性分别高 0.3%和低 0.4%。与另一位专家对 17 个额外记录的棘波和尖波指数评估的手动评分相比，该算法差异很小（平均绝对差异为 3.8%）。因此，该算法对于棘波计数和棘波和尖波指数的确定是有效的。
