The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index–approximate ...The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index–approximate entropy and a support vector machine that has strong generalization ability were applied to classify electroencephalogram signals at epileptic interictal and ictal periods. Our aim was to verify whether approximate entropy waves can be effectively applied to the automatic real-time detection of epilepsy in the electroencephalogram, and to explore its generalization ability as a classifier trained using a nonlinear dynamics index. Four patients presenting with partial epileptic seizures were included in this study. They were all diagnosed with neocortex localized epilepsy and epileptic foci were clearly observed by electroencephalogram. The electroencephalogram data form the four involved patients were segmented and the characteristic values of each segment, that is, the approximate entropy, were extracted. The support vector machine classifier was constructed with the approximate entropy extracted from one epileptic case, and then electroencephalogram waves of the other three cases were classified, reaching a 93.33% accuracy rate. Our findings suggest that the use of approximate entropy allows the automatic real-time detection of electroencephalogram data in epileptic cases. The combination of approximate entropy and support vector machines shows good generalization ability for the classification of electroencephalogram signals for epilepsy.展开更多
文摘目的探讨轻中型颅脑损伤患者血清神经元特异性烯醇化酶(NSE)的变化规律与颅脑损伤的严重程度和预后的关系。方法使用电化学发光法分别在轻中型颅脑损伤患者伤后24 h内、48 h、1周时检测患者血清NSE的值,用统计学方法分析血清NSE与格拉斯哥昏迷评分(GCS)、颅脑损伤分型和格拉斯哥预后评分(GOS)的关系。结果颅脑损伤患者24 h NSE检测结果与GCS评分、GOS评分呈负相关(P<0.05);48 h NSE与GCS评分呈负相关(P<0.05),与颅脑损伤分型呈正相关(P<0.05);1周后NSE与GCS评分呈负相关(P<0.05)。颅脑损伤24h后,中型患者血清NSE浓度高于轻型患者,差异有统计学意义(P<0.05)。40例完成3次检测的患者,血清NSE浓度呈现出损伤24h<48h后<1周后的趋势,相互比较差异有统计学意义(P<0.05)。CT平扫提示左枕叶脑挫裂伤伴脑内小血肿患者血清NSE浓度高于双额叶片状脑挫裂伤灶伴硬膜下小血肿,差异有统计学意义(P<0.05)。结论颅脑损伤患者伤后24 h内的NSE越高,GCS评分越低,病情越重,预后越差。伤后24 h内的NSE最有临床指导意义,24 h内达到峰值后呈逐渐下降趋势,检测NSE对评估颅脑损伤有一定的临床价值。
基金financially supported by the National Natural Science Foundation of China,No.61263011,81000554Program in Sun Yat-sen University supported by Fundamental Research Funds for the Central Universities,No.11ykpy07+1 种基金Natural Science Foundation of Guangdong Province,No.S2011010005309Innovation Fund of Xinjiang Medical University,No.XJC201209
文摘The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index–approximate entropy and a support vector machine that has strong generalization ability were applied to classify electroencephalogram signals at epileptic interictal and ictal periods. Our aim was to verify whether approximate entropy waves can be effectively applied to the automatic real-time detection of epilepsy in the electroencephalogram, and to explore its generalization ability as a classifier trained using a nonlinear dynamics index. Four patients presenting with partial epileptic seizures were included in this study. They were all diagnosed with neocortex localized epilepsy and epileptic foci were clearly observed by electroencephalogram. The electroencephalogram data form the four involved patients were segmented and the characteristic values of each segment, that is, the approximate entropy, were extracted. The support vector machine classifier was constructed with the approximate entropy extracted from one epileptic case, and then electroencephalogram waves of the other three cases were classified, reaching a 93.33% accuracy rate. Our findings suggest that the use of approximate entropy allows the automatic real-time detection of electroencephalogram data in epileptic cases. The combination of approximate entropy and support vector machines shows good generalization ability for the classification of electroencephalogram signals for epilepsy.
