Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Isfahan, Iran.
Comput Biol Med. 2012 Aug;42(8):848-56. doi: 10.1016/j.compbiomed.2012.06.008. Epub 2012 Jul 15.
This paper proposes a novel real-time patient-specific seizure diagnosis algorithm based on analysis of electroencephalogram (EEG) and electrocardiogram (ECG) signals to detect seizure onset. In this algorithm, spectral and spatial features are selected from seizure and non-seizure EEG signals by Gabor functions and principal component analysis (PCA). Furthermore, four features based on heart rate acceleration are extracted from ECG signals to form feature vector. Then a neural network classifier based on improved particle swarm optimization (IPSO) learning algorithm is developed to determine an optimal nonlinear decision boundary. This classifier allows to adjust the parameters of the neural network classifier, efficiently. This algorithm can automatically detect the presence of seizures with minimum delay which is an important factor from a clinical viewpoint. The performance of the proposed algorithm is evaluated on a dataset consisting of 154 h records and 633 seizures from 12 patients. The results indicate that the algorithm can recognize the seizures with the smallest latency and higher good detection rate (GDR) than other presented algorithms in the literature.
本文提出了一种基于分析脑电图（EEG）和心电图（ECG）信号的新型实时患者特异性癫痫发作诊断算法，以检测癫痫发作的开始。在该算法中，通过 Gabor 函数和主成分分析（PCA）从癫痫和非癫痫 EEG 信号中选择谱和空间特征。此外，从 ECG 信号中提取基于心率加速的四个特征以形成特征向量。然后，开发了一种基于改进粒子群优化（IPSO）学习算法的神经网络分类器，以确定最佳非线性决策边界。该分类器允许有效地调整神经网络分类器的参数。该算法可以自动检测到癫痫发作的存在，具有最小的延迟，这是从临床角度来看的一个重要因素。该算法在由 12 名患者的 154 小时记录和 633 次癫痫发作组成的数据集上进行了评估。结果表明，与文献中提出的其他算法相比，该算法能够以最小的潜伏期识别癫痫发作，并具有更高的良好检测率（GDR）。
