摘要
一位新闻记者兼机器人与机器学习公司的新闻编辑每日新闻-关于中枢神经系统疾病和状况的新研究-癫痫是一篇报道的主题。根据NewsRx记者在中国江苏的NEWS报道，Research指出：“微状态代表头皮记录的脑电活动的全球和地形分布，本研究旨在探索局灶性癫痫患者用药前的脑电微状态，并利用提取的微状态指标预测奥卡西平E单药治疗的疗效。”新闻记者从东南大学获得了这项研究的一句话：“这项研究涉及25名新诊断的局灶性癫痫患者(13名女性)，年龄12至68岁，有不同的病因。根据他们的第一次随访结果，患者被分为非癫痫无(NSF)组和癫痫无(SF)组。”采用聚类分析方法对4种有代表性的微状态进行识别,提取微状态的时间参数和转移概率,并进行分组差异分析,采用GE神经样本法、支持向量机(SVM)、Logistic回归(LR)和朴素贝叶斯(NB)分类器对治疗结果进行预测,NSF组微状态1(MS1)的持续时间显著延长(均值±标准差=0.092±0.008 vs 0.085±0.008,P=0.047)。与SF组相比，MS2的发生率(均值±标准差=2.587±0.334 vs2.260±0.278，P=0.014)和覆盖率(均值±标准差=0.240±0.046 vs0.194±0.040，P=0.014)增加，从微状态2(MS2)和微状态3(MS3)到MS1的转换概率增加，在MS2中，NSF组表现出更强的相关性(均值±标准与SF组相比，SF组的总体解释ED方差(平均值±标准差=0.083±0.035 vs 0.055±0.023，P=0.027)更高。相反，SF组的微状态4(MS4)显示出明显的GR食客覆盖率(平均值±标准差=0.388±0.074 vs 0.334±0.052，P=0.046)，以及更多的从MS2到MS4的快速转换，表明CAR曲线(AUCs)下面积分别为0.95、0.70和0.86，由LR、NB和SVM实现。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Central Nervous System Diseases and Conditions - Epilepsy is the subject of a report. According to new s reporting from Jiangsu, People’s Republic of China, by NewsRx journalists, res earch stated, “Microstates represent the global and topographical distribution o f electrical brain activity from scalp-recorded EEG. This study aims to explore EEG microstates of patients with focal epilepsy prior to medication, and employ extracted microstate metrics for predicting treatment outcomes with Oxcarbazepin e monotherapy.” The news correspondents obtained a quote from the research from Southeast Univer sity, “This study involved 25 newly-diagnosed focal epilepsy patients (13 female s), aged 12 to 68, with various etiologies. Patients were categorized into Non-S eizure-Free (NSF) and Seizure-Free (SF) groups according to their first follow-u p outcomes. From pre-medication EEGs, four representative microstates were ident ified by using clustering. The temporal parameters and transition probabilities of microstates were extracted and analyzed to discern group differences. With ge nerating sample method, Support Vector Machine (SVM), Logistic Regression (LR), and Naive Bayes (NB) classifiers were employed for predicting treatment outcomes . In the NSF group, Microstate 1 (MS1) exhibited a significantly higher duration (mean±std. = 0.092±0.008 vs. 0.085±0.008, p = 0.047), occurrence (mean±std. = 2 .587±0.334 vs. 2.260±0.278, p = 0.014), and coverage (mean±std. = 0.240±0.046 vs . 0.194±0.040, p = 0.014) compared to the SF group. Additionally, the transition probabilities from Microstate 2 (MS2) and Microstate 3 (MS3) to MS1 were increa sed. In MS2, the NSF group displayed a stronger correlation (mean±std. = 0.618±0 .025 vs. 0.571±0.034, p<0.001) and a higher global explain ed variance (mean±std. = 0.083±0.035 vs. 0.055±0.023, p = 0.027) than the SF gro up. Conversely, Microstate 4 (MS4) in the SF group demonstrated significantly gr eater coverage (mean±std. = 0.388±0.074 vs. 0.334±0.052, p = 0.046) and more fre quent transitions from MS2 to MS4, indicating a distinct pattern. Temporal param eters contribute major predictive role in predicting treatment outcomes of Oxcar bazepine, with area under curves (AUCs) of 0.95, 0.70, and 0.86, achieved by LR, NB and SVM, respectively.”
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