Genetic Programming of Conventional Features to Detect Seizure Precursors.
Genetic Programming of Conventional Features to Detect Seizure Precursors.
复制标题
检测癫痫前兆的常规特征的基因编程。
DOI：
10.1016/j.engappai.2007.02.002
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发表时间：
2007
影响因子：
8
通讯作者：
Vachtsevanos,George
中科院分区：
文献类型：
--
作者：
Smart,Otis;Firpi,Hiram;Vachtsevanos,George
本文介绍了遗传编程（GP）的应用，以最佳选择和融合常规特征（c特征），用于检测癫痫发作前的颅内脑电图（IEEG）记录中的癫痫波形，称为癫痫前兆。有证据表明，癫痫发作前体可能定位脑电图和癫痫治疗中对癫痫发作产生重要的区域。然而，目前检测癫痫前体的方法缺乏一种可靠的方法来自动选择和组合c特征，这些特征最能区分癫痫事件和背景，主要依赖于视觉回顾。这项工作表明，在评估了使用：(1)基因编程特征的二元探测器的性能后，GP是创建单个特征的最佳选择；(2)通过GP选择的特征；(3)向前依次选择特征；(4)视觉选择特征。结果表明，具有遗传编程特征的检测器优于其他三种方法，在95%的置信度水平上实现了超过78.5%的阳性预测值，83.5%的灵敏度和93%的特异性。
This paper presents an application of genetic programming (GP) to optimally select and fuse conventional features (C-features) for the detection of epileptic waveforms within intracranial electroencephalogram (IEEG) recordings that precede seizures, known as seizure precursors. Evidence suggests that seizure precursors may localize regions important to seizure generation on the IEEG and epilepsy treatment. However, current methods to detect epileptic precursors lack a sound approach to automatically select and combine C-features that best distinguish epileptic events from background, relying on visual review predominantly. This work suggests GP as an optimal alternative to create a single feature after evaluating the performance of a binary detector that uses: (1) genetically programmed features; (2) features selected via GP; (3) forward sequentially selected features; and (4) visually selected features. Results demonstrate that a detector with a genetically programmed feature outperforms the other three approaches, achieving over 78.5% positive predictive value, 83.5% sensitivity, and 93% specificity at the 95% level of confidence.