Nanyang Technological University, School of Electrical and Electronic Engineering, Singapore 639798, Singapore.
Nanyang Technological University, School of Electrical and Electronic Engineering, Singapore 639798, Singapore; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Cambridge, MA, USA.
J Neurosci Methods. 2019 Oct 1;326:108362. doi: 10.1016/j.jneumeth.2019.108362. Epub 2019 Jul 13.
Finding interictal epileptiform discharges (IEDs) in the EEG is a part of diagnosing epilepsy. Automated software for annotating EEGs of patients with suspected epilepsy can therefore help with reaching a diagnosis. A large amount of data is required for training and evaluating an effective IED detection system. IEDs occur infrequently in the most patients' EEG, therefore, interictal EEG recordings contain mostly background waveforms.
As the first step to detect IEDs, we propose a machine learning technique eliminating most EEG background data using an ensemble of simple fast classifiers based on several EEG features. This could save computation time for an IED detection method, allowing the remaining waveforms to be classified by more computationally intensive methods. We consider several efficient features and reject background by applying thresholds on them in consecutive steps.
We applied the proposed algorithm on a dataset of 156 EEGs (93 and 63 with and without IEDs, respectively). We were able to eliminate 78% of background waveforms while retaining 97% of IEDs on our cross-validated dataset.
We applied support vector machine, k-nearest neighbours, and random forest classifiers to detect IEDs with and without initial background rejection. Results show that rejecting background by our proposed method speeds up the overall classification by a factor ranging from 1.8 to 4.7 for the considered classifiers.
The proposed method successfully reduces computation time of an IED detection system. Therefore, it is beneficial in speeding up IED detection especially when utilizing large EEG datasets.
在 EEG 中发现癫痫发作间期放电（IEDs）是诊断癫痫的一部分。因此，用于标注疑似癫痫患者 EEG 的自动化软件可以帮助做出诊断。训练和评估有效的 IED 检测系统需要大量数据。大多数患者的 EEG 中 IEDs 发生频率较低，因此，发作间期 EEG 记录主要包含背景波形。
作为检测 IEDs 的第一步，我们提出了一种机器学习技术，使用基于几个 EEG 特征的简单快速分类器集合来消除大多数 EEG 背景数据。这可以为 IED 检测方法节省计算时间，允许使用更复杂的方法对剩余的波形进行分类。我们考虑了几种高效的特征，并通过在连续步骤中对它们应用阈值来拒绝背景。
我们将所提出的算法应用于 156 个 EEG 数据集（分别有 93 个和 63 个有和没有 IEDs）。在我们的交叉验证数据集中，我们能够消除 78%的背景波形，同时保留 97%的 IEDs。
我们应用支持向量机、k-最近邻和随机森林分类器来检测有和没有初始背景拒绝的 IEDs。结果表明，我们提出的方法通过拒绝背景可以使考虑到的分类器的整体分类速度提高 1.8 到 4.7 倍。
所提出的方法成功地减少了 IED 检测系统的计算时间。因此，它有利于加快 IED 检测速度，特别是在利用大型 EEG 数据集时。
