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Preictal state detection using prodromal symptoms: A machine learning approach
Epilepsia ( IF 6.6 ) Pub Date : 2021-01-19 , DOI: 10.1111/epi.16804
Louis Cousyn 1, 2, 3, 4 , Vincent Navarro 1, 2, 3, 4 , Mario Chavez 2
Affiliation  

A reliable identification of a high-risk state for upcoming seizures may allow for preemptive treatment and improve the quality of patients' lives. We evaluated and not evaluate the ability of prodromal symptoms to predict preictal states using a machine learning (ML) approach. Twenty-four patients with drug-resistant epilepsy were admitted for continuous video-electroencephalographic monitoring and filled out a daily four-point questionnaire on prodromal symptoms. Data were then classified into (1) a preictal group for questionnaires completed in a 24-h period prior to at least one seizure (n1 = 58) and (2) an interictal group for questionnaires completed in a 24-h period without seizures (n2 = 190). Our prediction model was based on a support vector machine classifier and compared to a Fisher's linear classifier. The combination of all the prodromal symptoms yielded a good prediction performance (area under the curve [AUC] = .72, 95% confidence interval [CI] = .61-.81). This performance was significantly enhanced by selecting a subset of the most relevant symptoms (AUC = .80, 95% CI = .69-.88). In comparison, the linear classifier systematically failed (AUCs < .6). Our findings indicate that the ML analysis of prodromal symptoms is a promising approach to identifying preictal states prior to seizures. This could pave the way for development of clinical strategies in seizure prevention and even a noninvasive alarm system.

中文翻译:

使用前驱症状检测发作前期状态:一种机器学习方法

可靠地识别即将发生的癫痫发作的高风险状态可能允许抢先治疗并提高患者的生活质量。我们使用机器学习 (ML) 方法评估而非评估前驱症状预测发作前状态的能力。24 名耐药性癫痫患者接受连续视频脑电图监测,并每天填写一份关于前驱症状的四点问卷。然后将数据分为(1)在至少一次癫痫发作之前的 24 小时内完成问卷调查的发作前组(n1 = 58)和(2)在没有癫痫发作的 24 小时内完成问卷调查的发作间期组( n2 = 190)。我们的预测模型基于支持向量机分类器,并与 Fisher 线性分类器进行比较。所有前驱症状的组合产生了良好的预测性能(曲线下面积 [AUC] = .72,95% 置信区间 [CI] = .61-.81)。通过选择最相关症状的子集(AUC = .80,95% CI = .69-.88),这种性能得到了显着提高。相比之下,线性分类器系统性地失败了(AUC < .6)。我们的研究结果表明,前驱症状的 ML 分析是一种很有前景的方法,可以在癫痫发作前识别发作前期状态。这可以为癫痫预防甚至无创警报系统的临床策略的开发铺平道路。通过选择最相关症状的子集(AUC = .80,95% CI = .69-.88),这种性能得到了显着提高。相比之下,线性分类器系统性地失败了(AUC < .6)。我们的研究结果表明,前驱症状的 ML 分析是一种很有前景的方法,可以在癫痫发作前识别发作前期状态。这可以为癫痫预防甚至无创警报系统的临床策略的开发铺平道路。通过选择最相关症状的子集(AUC = .80,95% CI = .69-.88),这种性能得到了显着提高。相比之下,线性分类器系统性地失败了(AUC < .6)。我们的研究结果表明,前驱症状的 ML 分析是一种很有前景的方法,可以在癫痫发作前识别发作前期状态。这可以为癫痫预防甚至无创警报系统的临床策略的开发铺平道路。
更新日期:2021-01-19
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