Department of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, PA, USA.
Department of Radiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Clin Neurophysiol. 2018 Jan;129(1):308-318. doi: 10.1016/j.clinph.2017.10.004. Epub 2017 Oct 21.
To develop a reliable software method using a topographic analysis of time-frequency plots to distinguish ripple (80-200 Hz) oscillations that are often associated with EEG sharp waves or spikes (RonS) from sinusoid-like waveforms that appear as ripples but correspond with digital filtering of sharp transients contained in the wide bandwidth EEG.
A custom algorithm distinguished true from false ripples in one second intracranial EEG (iEEG) recordings using wavelet convolution, identifying contours of isopower, and categorizing these contours into sets of open or closed loop groups. The spectral and temporal features of candidate groups were used to classify the ripple, and determine its duration, frequency, and power. Verification of detector accuracy was performed on the basis of simulations, and visual inspection of the original and band-pass filtered signals.
The detector could distinguish simulated true from false ripple on spikes (RonS). Among 2934 visually verified trials of iEEG recordings and spectrograms exhibiting RonS the accuracy of the detector was 88.5% with a sensitivity of 81.8% and a specificity of 95.2%. The precision was 94.5% and the negative predictive value was 84.0% (N = 12). Among, 1,370 trials of iEEG recording exhibiting RonS that were reviewed blindly without spectrograms the accuracy of the detector was 68.0%, with kappa equal to 0.01 ± 0.03. The detector successfully distinguished ripple from high spectral frequency 'fast ripple' oscillations (200-600 Hz), and characterize ripple duration and spectral frequency and power. The detector was confounded by brief bursts of gamma (30-80 Hz) activity in 7.31 ± 6.09% of trials, and in 30.2 ± 14.4% of the true RonS detections ripple duration was underestimated.
Characterizing the topographic features of a time-frequency plot generated by wavelet convolution is useful for distinguishing true oscillations from false oscillations generated by filter ringing.
Categorizing ripple oscillations and characterizing their properties can improve the clinical utility of the biomarker.
开发一种可靠的软件方法，使用时频图的地形分析来区分与 EEG 尖波或棘波（RonS）相关的高频（80-200Hz）震荡，以及与包含在宽带 EEG 中的锐变的数字滤波相对应的、表现为高频的正弦样波。
使用小波卷积，在一秒颅内 EEG（iEEG）记录中，通过识别等功率轮廓，并将这些轮廓分类为开环或闭环组，对真实与虚假高频进行区分。候选组的频谱和时频特征用于对高频进行分类，并确定其持续时间、频率和功率。基于模拟和原始及带通滤波信号的视觉检查，对探测器的准确性进行验证。
该探测器能够区分尖波上的模拟真实高频与虚假高频（RonS）。在 2934 次经过视觉验证的 iEEG 记录和显示 RonS 的频谱图试验中，探测器的准确性为 88.5%，灵敏度为 81.8%，特异性为 95.2%。精密度为 94.5%，阴性预测值为 84.0%（N=12）。在 1370 次没有频谱图的 iEEG 记录 RonS 试验中，探测器的准确性为 68.0%，kappa 值为 0.01±0.03。该探测器成功地将高频与高光谱频率“快高频”震荡（200-600Hz）区分开来，并对高频的持续时间和光谱频率和功率进行特征描述。在 7.31%±6.09%的试验中，该探测器会受到短暂的γ波（30-80Hz）活动的干扰，在 30.2%±14.4%的真正 RonS 检测中，高频的持续时间被低估。
通过对小波卷积生成的时频图的地形特征进行分析，有助于区分由滤波器振铃产生的真实震荡和虚假震荡。
对高频震荡进行分类并对其特征进行描述，可以提高该生物标志物的临床实用性。
