Clinical Neurophysiology Department, Virgen de la Luz Hospital, Cuenca, Spain; Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, Cuenca, Spain.
Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, Cuenca, Spain.
Brain Res. 2023 Jan 1;1798:148131. doi: 10.1016/j.brainres.2022.148131. Epub 2022 Oct 31.
Epilepsy detection is essential for patients with epilepsy and their families, as well as for researchers and medical staff. The use of electroencephalogram (EEG) as a tool to support the diagnosis of patients with epilepsy is fundamental. Today, machine learning (ML) techniques are widely applied in neuroscience. The main objective of our study is to differentiate patients with idiopathic generalized epilepsy from healthy controls by applying machine learning techniques on interictal electroencephalographic recordings. Our research predicts which patients have idiopathic generalized epilepsy from a scalp EEG study. In addition, this study focuses on using the extreme gradient boosting (XGB) method applied to scalp EEG. XGB is one of the variants of gradient boosting and is a supervised learning algorithm. This type of system is developed to increase performance and processing speed. Through this proposed method, an attempt is made to recognize patterns from scalp EEG recordings that would allow the detection of IGE with high accuracy and differentiate IGE patients from healthy controls, creating an additional tool to support clinicians in their decision-making. Among the ML methods applied, the proposed XGB method achieves a better prediction of the distinct features in EEG signals from patients with IGE. XGB was 6.26% more accurate than the k-Nearest Neighbours method and was more accurate than the support vector machine (10.61%), decision tree (9.71%) and Gaussian Naïve Bayes (11.83%). Besides, the proposed XGB method showed the highest area under the curve (AUC 98%) and balanced accuracy (98.13%) of all methods tested. Application of ML technique in EEG of patients with epilepsy is very recent and is emerging with promising results. In this research work, we showed the usefulness of ML techniques to identify and predict generalized epilepsy from healthy controls in scalp EEG studies. These findings could help develop automated tools that integrate these ML techniques to assist clinicians in differentiating between patients with IGE from healthy controls in daily practice.
癫痫检测对癫痫患者及其家属以及研究人员和医务人员至关重要。脑电图（EEG）作为支持癫痫患者诊断的工具是基础。如今，机器学习（ML）技术在神经科学中得到了广泛应用。我们研究的主要目的是通过应用机器学习技术对间发性脑电图记录进行分析，将特发性全面性癫痫患者与健康对照者区分开来。我们的研究预测从头皮 EEG 研究中哪些患者患有特发性全面性癫痫。此外，本研究侧重于使用应用于头皮 EEG 的极端梯度提升（XGB）方法。XGB 是梯度提升的变体之一，是一种监督学习算法。这种类型的系统是为了提高性能和处理速度而开发的。通过这种提出的方法，尝试从头皮 EEG 记录中识别模式，以便能够以高精度检测 IGE 并将 IGE 患者与健康对照者区分开来，从而创建一个额外的工具来支持临床医生做出决策。在所应用的 ML 方法中，提出的 XGB 方法在预测 IGE 患者 EEG 信号的独特特征方面表现更好。XGB 比 k-最近邻方法准确 6.26%，比支持向量机（10.61%）、决策树（9.71%）和高斯朴素贝叶斯（11.83%）更准确。此外，与所有测试的方法相比，提出的 XGB 方法的曲线下面积（AUC 98%）和平衡准确性（98.13%）最高。ML 技术在癫痫患者 EEG 中的应用非常新，并且具有有希望的结果。在这项研究工作中，我们展示了 ML 技术在识别和预测头皮 EEG 研究中健康对照者的全面性癫痫中的有用性。这些发现可以帮助开发集成这些 ML 技术的自动化工具，以帮助临床医生在日常实践中区分 IGE 患者和健康对照者。
