Embedded Systems Laboratory, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland.
Department of Electrical and Information Technology, Lund University, Lund, Sweden.
Epilepsia. 2023 Dec;64 Suppl 4:S23-S33. doi: 10.1111/epi.17176. Epub 2022 Feb 3.
Long-term automatic detection of focal seizures remains one of the major challenges in epilepsy due to the unacceptably high number of false alarms from state-of-the-art methods. Our aim was to investigate to what extent a new patient-specific approach based on similarly occurring morphological electroencephalographic (EEG) signal patterns could be used to distinguish seizures from nonseizure events, as well as to estimate its maximum performance.
We evaluated our approach on >5500 h of long-term EEG recordings using two public datasets: the PhysioNet.org Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) Scalp EEG database and the EPILEPSIAE European epilepsy database. We visually identified a set of similarly occurring morphological patterns (seizure signature) seen simultaneously over two different EEG channels, and within two randomly selected seizures from each individual. The same seizure signature was then searched for in the entire recording from the same patient using dynamic time warping (DTW) as a similarity metric, with a threshold set to reflect the maximum sensitivity our algorithm could achieve without false alarm.
At a DTW threshold providing no false alarm during the entire recordings, the mean seizure detection sensitivity across patients was 84%, including 96% for the CHB-MIT database and 74% for the European epilepsy database. A 100% sensitivity was reached in 50% of patients, including 79% from the CHB-MIT database and 27% from the European epilepsy database. The median latency from seizure onset to its detection was 17 ± 10 s, with 84% of seizures being detected within 40 s.
Personalized EEG signature combined with DTW appears to be a promising method to detect ictal events from a limited number of EEG channels with high sensitivity despite low rate of false alarms, high degree of interpretability, and low computational complexity, compatible with its future use in wearable devices.
由于现有方法产生的假警报数量过高，长期自动检测局灶性癫痫发作仍然是癫痫领域的主要挑战之一。我们旨在研究基于相似形态脑电图（EEG）信号模式的新的个体化方法在多大程度上可用于区分癫痫发作与非癫痫发作事件，并估计其最大性能。
我们使用两个公共数据集（PhysioNet.org 波士顿儿童医院-麻省理工学院头皮 EEG 数据库和 EPILEPSIAE 欧洲癫痫数据库）评估了我们的方法在超过 5500 小时的长期 EEG 记录中的应用。我们通过视觉识别同时出现在两个不同 EEG 通道上的一组相似形态模式（癫痫发作特征），并在每个个体的两个随机选择的癫痫发作中识别。然后使用动态时间规整（DTW）作为相似性度量在来自同一患者的整个记录中搜索相同的癫痫发作特征，阈值设置为反映我们的算法在不产生假警报的情况下所能达到的最大灵敏度。
在整个记录过程中，在不产生假警报的 DTW 阈值下，患者的平均癫痫发作检测灵敏度为 84%，其中 CHB-MIT 数据库为 96%，欧洲癫痫数据库为 74%。在 50%的患者中达到了 100%的灵敏度，其中 CHB-MIT 数据库为 79%，欧洲癫痫数据库为 27%。从癫痫发作开始到检测到的中位潜伏期为 17±10s，84%的癫痫发作在 40s 内被检测到。
个性化 EEG 特征与 DTW 相结合，似乎是一种很有前途的方法，可以从数量有限的 EEG 通道中以高灵敏度检测癫痫发作，尽管假警报率低、可解释性高、计算复杂度低，非常适合在可穿戴设备中使用。
