Liu Su, Sha Zhiyi, Sencer Altay, Aydoseli Aydin, Bebek Nerse, Abosch Aviva, Henry Thomas, Gurses Candan, Ince Nuri Firat
Department of Biomedical Engineering, University of Houston, Houston, TX, USA.
J Neural Eng. 2016 Apr;13(2):026026. doi: 10.1088/1741-2560/13/2/026026. Epub 2016 Feb 29.
High frequency oscillations (HFOs) in intracranial electroencephalography (iEEG) recordings are considered as promising clinical biomarkers of epileptogenic regions in the brain. The aim of this study is to improve and automatize the detection of HFOs by exploring the time-frequency content of iEEG and to investigate the seizure onset zone (SOZ) detection accuracy during the sleep, awake and pre-ictal states in patients with epilepsy, for the purpose of assisting the localization of SOZ in clinical practice.
Ten-minute iEEG segments were defined during different states in eight patients with refractory epilepsy. A three-stage algorithm was implemented to detect HFOs in these segments. First, an amplitude based initial detection threshold was used to generate a large pool of HFO candidates. Then distinguishing features were extracted from the time and time-frequency domain of the raw iEEG and used with a Gaussian mixture model clustering to isolate HFO events from other activities. The spatial distribution of HFO clusters was correlated with the seizure onset channels identified by neurologists in seven patient with good surgical outcome.
The overlapping rates of localized channels and seizure onset locations were high in all states. The best result was obtained using the iEEG data during sleep, achieving a sensitivity of 81%, and a specificity of 96%. The channels with maximum number of HFOs identified epileptogenic areas where the seizures occurred more frequently.
The current study was conducted using iEEG data collected in realistic clinical conditions without channel pre-exclusion. HFOs were investigated with novel features extracted from the entire frequency band, and were correlated with SOZ in different states. The results indicate that automatic HFO detection with unsupervised clustering methods exploring the time-frequency content of raw iEEG can be efficiently used to identify the epileptogenic zone with an accurate and efficient manner.
颅内脑电图（iEEG）记录中的高频振荡（HFOs）被认为是大脑中癫痫源区很有前景的临床生物标志物。本研究的目的是通过探索iEEG的时频内容来改进和自动化HFOs的检测，并研究癫痫患者在睡眠、清醒和发作前期状态下的发作起始区（SOZ）检测准确性，以辅助临床实践中SOZ的定位。
在8例难治性癫痫患者的不同状态下定义了10分钟的iEEG片段。实施了一种三阶段算法来检测这些片段中的HFOs。首先，基于幅度的初始检测阈值用于生成大量的HFO候选者。然后从原始iEEG的时域和时频域中提取区分特征，并与高斯混合模型聚类一起使用，以将HFO事件与其他活动隔离开来。HFO簇的空间分布与7例手术效果良好的患者中神经科医生确定的发作起始通道相关。
在所有状态下，定位通道与发作起始位置的重叠率都很高。使用睡眠期间的iEEG数据获得了最佳结果，灵敏度为81%，特异性为96%。HFO数量最多的通道识别出癫痫发作更频繁发生的癫痫源区。
本研究使用在实际临床条件下收集的iEEG数据进行，没有进行通道预排除。利用从整个频带提取的新特征对HFOs进行了研究，并将其与不同状态下的SOZ相关联。结果表明，采用探索原始iEEG时频内容的无监督聚类方法进行自动HFO检测，可以高效准确地识别癫痫源区。
