Department of Electrical and Systems Engineering, Washington University in St. Louis, 1 Brookings Drive, St. Louis 63130, MO, USA.
St. Louis Children's Hospital, One Children's Place, St. Louis 63110, MO, USA.
J Neurosci Methods. 2022 Aug 1;378:109660. doi: 10.1016/j.jneumeth.2022.109660. Epub 2022 Jun 30.
We observed an unusual modulatory phenomenon in the electroencephalogram (EEG) of pediatric patients with acquired brain injury. The modulation is orders of magnitude slower than the fast EEG background activity, necessitating new analysis procedures to systematically detect and quantify the phenomenon.
We propose a method for analyzing spatial and temporal relationships associated with slow, narrowband modulation of EEG. We extract envelope signals from physiological frequency bands of EEG. Then, we construct a sparse representation of the spectral content of the envelope signal across sliding windows. For the latter, we use an augmented LASSO regression to incorporate spatial and temporal filtering into the solution. The method can be applied to windows of variable length, depending on the desired frequency resolution.
The sparse estimates of the envelope power spectra enable the detection of narrowband modulation in the millihertz frequency range. Subsequently, we are able to assess non-stationarity in the frequency and spatial relationships across channels. The method can be paired with unsupervised anomaly detection to identify windows with significant modulation. We validated such findings by applying our method to a control set of EEGs.
To our knowledge, no methods have been previously proposed to quantify second order modulation at such disparate time-scales.
We provide a general EEG analysis framework capable of detecting signal content below 0.1 Hz, which is especially germane to clinical recordings that may contain multiple hours worth of continuous data.
我们观察到患有后天性脑损伤的儿科患者脑电图（EEG）中出现一种不寻常的调制现象。这种调制比快速 EEG 背景活动慢几个数量级，因此需要新的分析程序来系统地检测和量化这种现象。
我们提出了一种分析与 EEG 缓慢、窄带调制相关的空间和时间关系的方法。我们从 EEG 的生理频带中提取包络信号。然后，我们构建了一个在滑动窗口中对包络信号的频谱内容进行稀疏表示的方法。对于后者，我们使用增广 LASSO 回归将空间和时间滤波纳入解决方案中。该方法可应用于具有可变长度的窗口，具体取决于所需的频率分辨率。
包络功率谱的稀疏估计能够检测到毫赫兹频率范围内的窄带调制。随后，我们能够评估通道之间的频率和空间关系的非平稳性。该方法可以与无监督异常检测配对，以识别具有显著调制的窗口。我们通过将我们的方法应用于 EEG 的对照组来验证这些发现。
据我们所知，以前没有提出过在如此不同的时间尺度上量化二阶调制的方法。
我们提供了一种通用的 EEG 分析框架，能够检测低于 0.1 Hz 的信号内容，这对于可能包含多个小时连续数据的临床记录尤其相关。
