Bagheri Elham, Jin Jing, Dauwels Justin, Cash Sydney, Westover M Brandon
Nanyang Technological University, School of Electrical and Electronic Engineering, Singapore 639798.
Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; and Harvard Medical School, Cambridge, MA, USA.
Proc IEEE Int Conf Acoust Speech Signal Process. 2018 Apr;2018:970-974. doi: 10.1109/ICASSP.2018.8461992. Epub 2018 Sep 13.
The presence of Epileptiform Transients (ET) in the electroencephalogram (EEG) is a key finding in the medical workup of a patient with suspected epilepsy. Automated ET detection can increase the uniformity and speed of ET detection. Current ET detection methods suffer from insufficient precision and high false positive rates. Since ETs occur infrequently in the EEG of most patients, the majority of recordings comprise background EEG waveforms. In this work we establish a method to exclude as much background data as possible from EEG recordings by applying a classifier cascade. The remaining data can then be classified using other ET detection methods. We compare a single Support Vector Machine (SVM) to a cascade of SVMs for detecting ETs. Our results show that the precision and false positive rate improve significantly by incorporating a classifier cascade before ET detection. Our method can help improve the precision and false positive rate of an ET detection system. At a fixed sensitivity, we were able to improve precision by 6.78%; and at a fixed false positive rate, the sensitivity improved by 2.83%.
脑电图（EEG）中癫痫样瞬变（ET）的出现是疑似癫痫患者医学检查的关键发现。自动ET检测可以提高ET检测的一致性和速度。当前的ET检测方法存在精度不足和假阳性率高的问题。由于大多数患者的脑电图中ET出现频率较低，大多数记录包含背景脑电波形。在这项工作中，我们建立了一种方法，通过应用分类器级联从脑电图记录中排除尽可能多的背景数据。然后可以使用其他ET检测方法对剩余数据进行分类。我们将单个支持向量机（SVM）与SVM级联用于检测ET进行了比较。我们的结果表明，在ET检测之前加入分类器级联可以显著提高精度和降低假阳性率。我们的方法有助于提高ET检测系统的精度和降低假阳性率。在固定灵敏度下，我们能够将精度提高6.78%；在固定假阳性率下，灵敏度提高了2.83%。
