Center for Health & Bioresources, AIT Austrian Institute of Technology GmbH, Vienna, Austria.
Department of Clinical Neurophysiology, Aarhus University Hospital and Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
Clin Neurophysiol. 2020 Jun;131(6):1174-1179. doi: 10.1016/j.clinph.2020.02.032. Epub 2020 Apr 2.
To validate an artificial intelligence-based computer algorithm for detection of epileptiform EEG discharges (EDs) and subsequent identification of patients with epilepsy.
We developed an algorithm for automatic detection of EDs, based on a novel deep learning method that requires a low amount of labeled EEG data for training. Detected EDs are automatically grouped into clusters, consisting of the same type of EDs, for rapid visual inspection. We validated the algorithm on an independent dataset of 100 patients with sharp transients in their EEG recordings (54 with epilepsy and 46 with non-epileptic paroxysmal events). The diagnostic gold standard was derived from the video-EEG recordings of the patients' habitual events.
The algorithm had a sensitivity of 89% for identifying EEGs with EDs recorded from patients with epilepsy, a specificity of 70%, and an overall accuracy of 80%.
Automated detection of EDs using an artificial intelligence-based computer algorithm had a high sensitivity. Human (expert) supervision is still necessary for confirming the clusters of detected EDs and for describing clinical correlations. Further studies on different patient populations will be needed to confirm our results.
The automated algorithm we describe here is a useful tool, assisting neurophysiologist in rapid assessment of EEG recordings.
验证一种基于人工智能的计算机算法，用于检测癫痫样脑电图放电（ED），并随后识别癫痫患者。
我们开发了一种基于新型深度学习方法的 ED 自动检测算法，该方法仅需少量标记的 EEG 数据进行训练。检测到的 ED 自动分组为簇，包含相同类型的 ED，以便快速进行视觉检查。我们在 100 名 EEG 记录中有锐波瞬变的患者的独立数据集上验证了该算法（54 名癫痫患者和 46 名非癫痫阵发性事件患者）。诊断的金标准来自患者习惯性事件的视频-EEG 记录。
该算法对记录有癫痫患者 ED 的 EEG 的识别具有 89%的敏感性、70%的特异性和 80%的总体准确性。
使用基于人工智能的计算机算法自动检测 ED 具有很高的敏感性。仍需要人工（专家）监督来确认检测到的 ED 簇，并描述临床相关性。需要对不同的患者群体进行进一步的研究来证实我们的结果。
我们在此描述的自动算法是一种有用的工具，可协助神经生理学家快速评估 EEG 记录。
