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.

中文翻译：

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检测癫痫前体的常规特征的遗传编程。

本文介绍了遗传编程 (GP) 的应用，以优化选择和融合传统特征 (C 特征)，以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，癫痫前体可能定位于 IEEG 和癫痫治疗中对癫痫产生很重要的区域。然而，目前检测癫痫前兆的方法缺乏一种可靠的方法来自动选择和组合最能将癫痫事件与背景区分开来的 C 特征，主要依赖于视觉检查。这项工作建议在评估二进制检测器的性能后，GP 作为创建单个特征的最佳替代方案：1）遗传编程特征；2) 通过GP选择的功能；3) 向前依次选择的特征；和 4) 视觉选择的特征。结果表明，具有基因编程特征的检测器优于其他三种方法，在 95% 的置信水平下实现了超过 78.5% 的阳性预测值、83.5% 的灵敏度和 93% 的特异性。