Gu Zeyu, Yang Shihao, Yu Zhongyuan, Liu Feng
bioRxiv. 2024 May 4:2024.05.01.592107. doi: 10.1101/2024.05.01.592107.
High Frequency Oscillations (HFOs) is an important biomarker that can potentially pinpoint the epileptogenic zones (EZs). However, the duration of HFO is short with around 4 cycles, which might be hard to recognize when embedded within signals of lower frequency oscillatory background. In addition, annotating HFOs manually can be time-consuming given long-time recordings and up to hundreds of intracranial electrodes. We propose to leverage a Switching State Space Model (SSSM) to identify the HFOs events automatically and instantaneously without relying on extracting features from sliding windows. The effectiveness of the SSSM for HFOs detection is fully validated in the intracranial EEG recording from human subjects undergoing the presurgical evaluations and showed improved accuracy when capturing the HFOs occurrence and their duration.
高频振荡（HFOs）是一种重要的生物标志物，有可能精确确定癫痫发作起源区（EZs）。然而，HFO的持续时间很短，约为4个周期，当它嵌入低频振荡背景信号中时可能很难识别。此外，鉴于长时间记录以及多达数百个颅内电极，手动标注HFOs可能很耗时。我们建议利用切换状态空间模型（SSSM）自动且即时地识别HFOs事件，而无需依赖从滑动窗口中提取特征。SSSM用于HFOs检测的有效性在接受术前评估的人类受试者的颅内脑电图记录中得到了充分验证，并且在捕捉HFOs的发生及其持续时间时显示出更高的准确性。
