Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America.
Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America.
Biomed Phys Eng Express. 2024 Sep 20;10(6). doi: 10.1088/2057-1976/ad6c53.
Periodic discharges (PDs) are pathologic patterns of epileptiform discharges repeating at regular intervals, commonly detected in the human electroencephalogram (EEG) signals in patients who are critically ill. The frequency and spatial extent of PDs are associated with the tendency of PDs to cause brain injury, existing automated algorithms do not quantify the frequency and spatial extent of PDs. The present study presents an algorithm for quantifying frequency and spatial extent of PDs. The algorithm quantifies the evolution of these parameters within a short (10-14 second) window, with a focus on lateralized and generalized periodic discharges. We test our algorithm on 300 'easy', 300 'medium', and 240 'hard' examples (840 total epochs) of periodic discharges as quantified by interrater consensus from human experts when analyzing the given EEG epochs. We observe 95.0% agreement with a 95% confidence interval (CI) of [94.9%, 95.1%] between algorithm outputs with reviewer clincal judgement for easy examples, 92.0% agreement (95% CI [91.9%, 92.2%]) for medium examples, and 90.4% agreement (95% CI [90.3%, 90.6%]) for hard examples. The algorithm is also computationally efficient and is able to run in 0.385 ± 0.038 seconds for a single epoch using our provided implementation of the algorithm. The results demonstrate the algorithm's effectiveness in quantifying these discharges and provide a standardized and efficient approach for PD quantification as compared to existing manual approaches.
周期性放电（PDs）是指在患有重病的患者的人类脑电图（EEG）信号中以规则间隔重复出现的病理性癫痫样放电模式。PDs 的频率和空间范围与 PD 引起脑损伤的倾向有关，现有的自动化算法无法量化 PD 的频率和空间范围。本研究提出了一种量化 PD 频率和空间范围的算法。该算法在短（10-14 秒）窗口内量化这些参数的演变，重点关注侧化和广义周期性放电。我们在 300 个“简单”、300 个“中等”和 240 个“困难”的周期性放电示例（总计 840 个 EEG 时段）上测试了我们的算法，这些示例的周期性放电是由人类专家在分析给定 EEG 时段时的组内共识来量化的。我们观察到，对于简单示例，算法输出与审查者临床判断之间的一致性为 95.0%，置信区间为[94.9%，95.1%]；对于中等示例，一致性为 92.0%（95%置信区间[91.9%，92.2%]）；对于困难示例，一致性为 90.4%（95%置信区间[90.3%，90.6%]）。该算法还具有计算效率，使用我们提供的算法实现，单个时段的运行时间为 0.385 ± 0.038 秒。结果表明，该算法在量化这些放电方面是有效的，并提供了一种标准化且高效的 PD 量化方法，与现有的手动方法相比。
