School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
Comput Methods Programs Biomed. 2023 Oct;240:107678. doi: 10.1016/j.cmpb.2023.107678. Epub 2023 Jun 18.
Epilepsy is a serious brain disorder affecting more than 50 million people worldwide. If epileptic seizures can be predicted in advance, patients can take measures to avoid unfortunate consequences. Important approaches for epileptic seizure predictions are often signal transformation and classification using electroencephalography (EEG) signals. A time-frequency (TF) transformation, such as the short-term Fourier transform (STFT), has been widely used over many years but curtailed by the Heisenberg uncertainty principle. This research focuses on decomposing epileptic EEG signals with a higher resolution so that an epileptic seizure can be predicted accurately before its episodes.
This study applies a synchroextracting transformation (SET) and singular value decomposition (SET-SVD) to improve the time-frequency resolution. The SET is a more energy-concentrated TF representation than classical TF analysis methods.
The pre-seizure classification method employing a 1-dimensional convolutional neural network (1D-CNN) reached an accuracy of 99.71% (the CHB-MIT database) and 100% (the Bonn University database). The experiments on the CHB-MIT show that the accuracy, sensitivity and specificity from the SET-SVD method, compared with the results of the STFT, are increased by 8.12%, 6.24% and 13.91%, respectively. In addition, a multi-layer perceptron (MLP) was also used as a classifier. Its experimental results also show that the SET-SVD generates a higher accuracy, sensitivity and specificity by 5.0%, 2.41% and 11.42% than the STFT, respectively.
The results of two classification methods (the MLP and 1D-CNN) show that the SET-SVD has the capacity to extract more accurate information than the STFT. The 1D-CNN model is suitable for a fast and accurate patient-specific EEG classification.
癫痫是一种严重的脑部疾病，影响着全球超过 5000 万人。如果能提前预测癫痫发作，患者就能采取措施避免不幸的后果。预测癫痫发作的重要方法通常是使用脑电图（EEG）信号进行信号变换和分类。多年来，一种时频（TF）变换，如短时傅里叶变换（STFT），得到了广泛的应用，但受到海森堡测不准原理的限制。本研究专注于分解具有更高分辨率的癫痫 EEG 信号，以便在癫痫发作前准确预测癫痫发作。
本研究应用同步提取变换（SET）和奇异值分解（SET-SVD）来提高时频分辨率。SET 是一种比经典 TF 分析方法更集中能量的 TF 表示。
采用一维卷积神经网络（1D-CNN）的预发作分类方法在 CHB-MIT 数据库中达到了 99.71%的准确率，在 Bonn 大学数据库中达到了 100%的准确率。CHB-MIT 的实验表明，与 STFT 的结果相比，SET-SVD 方法的准确率、灵敏度和特异性分别提高了 8.12%、6.24%和 13.91%。此外，还使用了多层感知器（MLP）作为分类器。其实验结果也表明，SET-SVD 产生的准确率、灵敏度和特异性分别比 STFT 高 5.0%、2.41%和 11.42%。
两种分类方法（MLP 和 1D-CNN）的结果表明，SET-SVD 具有比 STFT 更准确地提取信息的能力。1D-CNN 模型适合快速准确地对患者特定的 EEG 进行分类。
