规范化(社会学)
时频分析
频域
低频
模式识别(心理学)
脑电图
频带
振荡(细胞信号)
人工智能
数学
计算机科学
医学
化学
计算机视觉
精神科
带宽(计算)
社会学
滤波器(信号处理)
电信
生物化学
计算机网络
人类学
作者
Nicolas Roehri,Jean‐Marc Lina,John C. Mosher,Fabrice Bartoloméi,Christian Bénar
标识
DOI:10.1109/tbme.2016.2556425
摘要
BACKGROUND: High-frequency oscillations (HFOs) are considered to be highly representative of brain tissues capable of producing epileptic seizures. The visual review of HFOs on intracerebral electroencephalography is time consuming and tedious, and it can be improved by time-frequency (TF) analysis. The main issue is that the signal is dominated by lower frequencies that mask the HFOs. Our aim was to flatten (i.e., whiten) the frequency spectrum to enhance the fast oscillations while preserving an optimal signal to noise ratio (SNR). METHOD: We investigated eight methods of data whitening based on either prewhitening or TF normalization in order to improve the detectability of HFOs. We detected all local maxima of the TF image above a range of thresholds in the HFO band. RESULTS: We obtained the precision and recall curves at different SNR and for different HFO types and illustrate the added value of whitening both in the TF plane and in time domain. CONCLUSION: z-score") are more precise than the others. SIGNIFICANCE: z-score provides an optimal framework for representing and detecting HFOs, independent of a baseline and a priori frequency bands.
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