目的:功能性癫痫发作(FS)看起来像癫痫发作,但其特征是大脑缺乏癫痫活动。大约五分之一转诊到癫痫诊所的患者被诊断出患有这种疾病。FS通过使用视频脑电图(EEG)记录癫痫发作来诊断,专家从中检查符号学和脑电图。然而,这种方法可能是昂贵且难以获得的,并且可能给患者带来巨大负担.没有发现单一的生物标志物来诊断FS。然而,当前FS诊断中的局限性可以通过机器学习来改进,以对从EEG中提取的信号特征进行分类,从而为临床医生提供潜在的非常有用的帮助。
方法:当前的研究已经调查了使用无癫痫发作的EEG信号与机器学习来从癫痫患者中识别FS的受试者。数据集包括48名FS受试者的发作间和发作前脑电图记录(平均年龄=34.76±10.55岁,14名男性)和29名癫痫患者(平均年龄=38.95±13.93岁,18名男性),其中有各种统计数据,temporal,并从五个脑电频带中提取频谱特征,然后进行阈值精度分析,五个机器学习分类器,和两种特征重要性方法。
结果:阈值法报告的最高分类准确率为60.67%。然而,时间特征表现最好,具有机器学习模型报告的最高平衡精度:95.71%,所有频带组合和支持向量机分类器。
结论:机器学习比使用个体特征更有效,并且可能是FS诊断的有力辅助。此外,在大多数情况下,组合频带提高了分类器的准确性,表现最低的EEG波段始终是δ和γ。
OBJECTIVE: Functional seizures (FS) look like epileptic seizures but are characterized by a lack of epileptic activity in the brain. Approximately one in five referrals to epilepsy clinics are diagnosed with this condition. FS are diagnosed by recording a seizure using video-electroencephalography (EEG), from which an expert inspects the semiology and the EEG. However, this method can be expensive and inaccessible and can present significant patient burden. No single biomarker has been found to diagnose FS. However, the current limitations in FS diagnosis could be improved with machine learning to classify signal features extracted from EEG, thus providing a potentially very useful aid to clinicians.
METHODS: The current study has investigated the use of seizure-free EEG signals with machine learning to identify subjects with FS from those with epilepsy. The dataset included interictal and preictal EEG recordings from 48 subjects with FS (mean age = 34.76 ± 10.55 years, 14 males) and 29 subjects with epilepsy (mean age = 38.95 ± 13.93 years, 18 males) from which various statistical, temporal, and spectral features from the five EEG frequency bands were extracted then analyzed with threshold accuracy, five machine learning classifiers, and two feature importance approaches.
RESULTS: The highest classification accuracy reported from thresholding was 60.67%. However, the temporal features were the best performing, with the highest balanced accuracy reported by the machine learning models: 95.71% with all frequency bands combined and a support vector machine classifier.
CONCLUSIONS: Machine learning was much more effective than using individual features and could be a powerful aid in FS diagnosis. Furthermore, combining the frequency bands improved the accuracy of the classifiers in most cases, and the lowest performing EEG bands were consistently delta and gamma.