The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China.
College of Information Engineering, Henan University of Science and Technology, Luoyang, China.
BMC Med Inform Decis Mak. 2023 May 22;23(1):96. doi: 10.1186/s12911-023-02180-w.
Epilepsy is a neurological disorder that is usually detected by electroencephalogram (EEG) signals. Since manual examination of epilepsy seizures is a laborious and time-consuming process, lots of automatic epilepsy detection algorithms have been proposed. However, most of the available classification algorithms for epilepsy EEG signals adopted a single feature extraction, in turn to result in low classification accuracy. Although a small account of studies have carried out feature fusion, the computational efficiency is reduced due to too many features, because there are also some poor features that interfere with the classification results.
In order to solve the above problems, an automatic recognition method of epilepsy EEG signals based on feature fusion and selection is proposed in this paper. Firstly, the Approximate Entropy (ApEn), Fuzzy Entropy (FuzzyEn), Sample Entropy (SampEn), and Standard Deviation (STD) mixed features of the subband obtained by the Discrete Wavelet Transform (DWT) decomposition of EEG signals are extracted. Secondly, the random forest algorithm is used for feature selection. Finally, the Convolutional Neural Network (CNN) is used to classify epilepsy EEG signals.
The empirical evaluation of the presented algorithm is performed on the benchmark Bonn EEG datasets and New Delhi datasets. In the interictal and ictal classification tasks of Bonn datasets, the proposed model achieves an accuracy of 99.9%, a sensitivity of 100%, a precision of 99.81%, and a specificity of 99.8%. For the interictal-ictal case of New Delhi datasets, the proposed model achieves a classification accuracy of 100%, a sensitivity of 100%, a specificity of 100%, and a precision of 100%.
The proposed model can effectively realize the high-precision automatic detection and classification of epilepsy EEG signals. This model can provide high-precision automatic detection capability for clinical epilepsy EEG detection. We hope to provide positive implications for the prediction of seizure EEG.
癫痫是一种神经系统疾病，通常通过脑电图（EEG）信号检测。由于手动检查癫痫发作是一项费力且耗时的过程，因此已经提出了许多自动癫痫检测算法。然而，大多数现有的癫痫 EEG 信号分类算法都采用了单一的特征提取方法，从而导致分类精度较低。尽管有一些研究进行了特征融合，但由于特征过多，计算效率降低，因为其中一些较差的特征会干扰分类结果。
为了解决上述问题，本文提出了一种基于特征融合和选择的癫痫 EEG 信号自动识别方法。首先，提取 EEG 信号经离散小波变换（DWT）分解后子带的近似熵（ApEn）、模糊熵（FuzzyEn）、样本熵（SampEn）和标准差（STD）混合特征。其次，采用随机森林算法进行特征选择。最后，采用卷积神经网络（CNN）对癫痫 EEG 信号进行分类。
在基准波恩 EEG 数据集和新德里数据集上对所提出的算法进行了实证评估。在波恩数据集的间息和发作分类任务中，所提出的模型达到了 99.9%的准确率、100%的灵敏度、99.81%的精度和 99.8%的特异性。对于新德里数据集的间息-发作病例，所提出的模型达到了 100%的分类准确率、100%的灵敏度、100%的特异性和 100%的精度。
所提出的模型可以有效地实现癫痫 EEG 信号的高精度自动检测和分类。该模型可以为临床癫痫 EEG 检测提供高精度的自动检测能力。我们希望为癫痫 EEG 的预测提供积极的启示。
