广东工业大学学报 ›› 2015, Vol. 32 ›› Issue (04): 60-66.doi: 10.3969/j.issn.1007-7162.2015.04.011

• 综合研究 • 上一篇    下一篇

基于脑电高频振荡节律的癫痫始发区快速定位算法研究

杜玉晓,陈崇毅   

  1. 广东工业大学 自动化学院, 广东 广州 510006
  • 收稿日期:2015-04-02 出版日期:2015-12-04 发布日期:2015-12-04
  • 作者简介:杜玉晓(1973-), 男, 副教授, 主要研究方向为嵌入式系统设计及应用、自动化装备与集成、医疗器械设备及脑机接口(BCI)技术. 陈崇毅(1989-),男,硕士研究生,主要研究方向为数字信号处理、数字逻辑与嵌入式系统设计,E-mail: sungngai.chen@gmail.com
  • 基金资助:

    科技支撑计划项目(504140001);广东省科技计划项目(502140112)

Fast Automated Detection Method of Seizure Onset Zone by High Frequency Oscillations

Du Yu-xiao, Chen Chong-yi   

  1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2015-04-02 Online:2015-12-04 Published:2015-12-04

摘要: 最新研究发现癫痫脑电信号中的高频振荡节律(High-frequency Oscillations, HFOs)是定位癫痫始发区(Seizure Onset Zone, SOZ)的重要生物指标.在癫痫致痫灶术前定位的过程中,以癫痫样放电判断癫痫始发区的方法十分耗时,而且癫痫样放电与癫痫发作的关系还不明确,不但增加病人感染疾病的风险,还会造成误诊.本文设计了一种基于高频振荡节律的快速定位算法,利用功率谱密度的差异提取疑似癫痫始发区,再根据曲线模板检出高频振荡节律同步出现的导联;同时基于复Morlet小波将信号变换到时频域进行分析,共同实现癫痫始发区快速定位.经过对4例临床病例处理的结果表明,该算法灵敏度和特异性良好,有助于临床癫痫手术术前精确定位.

关键词: 高频振荡节律; 癫痫脑电信号处理; AR模型; 复Morlet小波变换

Abstract: High frequency oscillations have been recently proposed as a new biomarker of seizure onset zone. Marking epileptic discharge is highly time-consuming, easy to get false detection, and the relation between discharge and seizure still leaves much to define. This paper presents a fast automated detection method based on the difference of power spectral density in each channel, and similarity of filtered signal enveloped on epilepsy propagation path, and also the combination with complex Morlet wavelet transform to classify high frequency oscillations.The results obtained in 4 clinical cases show high performance in terms of sensitivity and specificity which can be considered as an effective method in preoperative localization.

Key words: high frequency oscillations; epileptic recordings; AR model; complex Morlet wavelet transform

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