School of Mathematics and Statistics, Henan University, Kaifeng 475004, People's Republic of China. Author to whom any correspondence should be addressed.
Physiol Meas. 2019 Sep 30;40(9):095004. doi: 10.1088/1361-6579/ab3e2e.
Epilepsy is a common neurological disorder which can occur in people of all ages globally. For the clinical treatment of epileptic patients, the detection of epileptic seizures is of great significance.
Electroencephalography (EEG) is an essential component in the diagnosis of epileptic seizures, from which brain surgeons can detect important pathological information about patient epileptiform discharges. This paper focuses on adaptive seizure detection from EEG recordings. We propose a new feature extraction model based on an adaptive decomposition method, named intrinsic time-scale decomposition (ITD), which is suitable for analyzing non-linear and non-stationary data.
Firstly, using the ITD technique, every EEG recording is decomposed into several proper rotation components (PRCs). Secondly, the instantaneous amplitudes and frequencies of these PRCs can be calculated and then we extract their statistical indices. Furthermore, we combine all these statistical indices of the corresponding five PRCs as the feature vector of each EEG signal. Finally, these feature vectors are fed into a feedforward neural network (FNN) classifier for EEG classification. The whole process of feature extraction proposed in this paper only involves one parameter and the role of the ITD method is based on a piecewise linear function, which makes the computation of the model simple and fast. More useful information for classification can be obtained since we take advantage of both instantaneous amplitude and instantaneous frequency for feature extraction.
We consider the 17 classification problems which contain normal versus epileptic, non-seizure versus seizure and normal versus interictal versus ictal using a FNN classifier which only contains one hidden layer. Experimental results show that the proposed method can catch the discriminative features of EEG signals and obtain comparable results when compared with state-of-the-art detection methods.
Therefore, the proposed system has a great potential in real-time seizure detection and provides physicians with a real-time diagnostic aid in their practice.
癫痫是一种常见的神经系统疾病，可发生在全球各个年龄段的人群中。对于癫痫患者的临床治疗，癫痫发作的检测具有重要意义。
脑电图（EEG）是癫痫发作诊断的重要组成部分，脑外科医生可以从中检测到患者癫痫样放电的重要病理信息。本文专注于从 EEG 记录中进行自适应癫痫检测。我们提出了一种新的特征提取模型，基于自适应分解方法，称为固有时间尺度分解（ITD），它适用于分析非线性和非平稳数据。
首先，使用 ITD 技术，将每个 EEG 记录分解为几个适当的旋转分量（PRC）。其次，可以计算这些 PRC 的瞬时幅度和频率，然后提取它们的统计指标。此外，我们将对应五个 PRC 的所有这些统计指标组合为每个 EEG 信号的特征向量。最后，将这些特征向量输入前馈神经网络（FNN）分类器进行 EEG 分类。本文提出的特征提取全过程仅涉及一个参数，ITD 方法的作用基于分段线性函数，这使得模型的计算简单快捷。由于我们同时利用瞬时幅度和瞬时频率进行特征提取，可以获得更多对分类有用的信息。
我们考虑了 17 种分类问题，包含正常与癫痫、非发作与发作以及正常与发作间期与发作，使用仅包含一个隐藏层的 FNN 分类器。实验结果表明，所提出的方法可以捕捉 EEG 信号的区分特征，与最先进的检测方法相比，可获得可比的结果。
因此，所提出的系统在实时癫痫检测中具有很大的潜力，并为医生在实践中提供实时诊断辅助。
