IEEE Trans Neural Syst Rehabil Eng. 2021;29:1604-1613. doi: 10.1109/TNSRE.2021.3103210. Epub 2021 Aug 20.
As one of the most challenging data analysis tasks in chronic brain diseases, epileptic seizure prediction has attracted extensive attention from many researchers. Seizure prediction, can greatly improve patients' quality of life in many ways, such as preventing accidents and reducing harm that may occur during epileptic seizures. This work aims to develop a general method for predicting seizures in specific patients through exploring the time-frequency correlation of features obtained from multi-channel EEG signals. We convert the original EEG signals into spectrograms that represent time-frequency characteristics by applying short-time Fourier transform (STFT) to the EEG signals. For the first time, we propose a dual self-attention residual network (RDANet) that combines a spectrum attention module integrating local features with global features, with a channel attention module mining the interdependence between channel mappings to achieve better forecasting performance. Our proposed approach achieved a sensitivity of 89.33%, a specificity of 93.02%, an AUC of 91.26% and an accuracy of 92.07% on 13 patients from the public CHB-MIT scalp EEG dataset. Our experiments show that different EEG signal prediction segment lengths are an important factor affecting prediction performance. Our proposed method is competitive and achieves good robustness without patient-specific engineering.
作为慢性脑疾病中最具挑战性的数据分析任务之一，癫痫发作预测引起了许多研究人员的广泛关注。通过探索从多通道 EEG 信号中获得的特征的时频相关性，癫痫发作预测可以在许多方面极大地提高患者的生活质量，例如预防事故和减少癫痫发作期间可能发生的伤害。这项工作旨在通过探索从多通道 EEG 信号中获得的特征的时频相关性，开发一种针对特定患者的癫痫发作预测的通用方法。我们通过对 EEG 信号应用短时傅里叶变换（STFT）将原始 EEG 信号转换为表示时频特征的频谱图。我们首次提出了一种双重自注意力残差网络（RDANet），它结合了一个频谱注意力模块，该模块集成了局部特征和全局特征，以及一个通道注意力模块，挖掘通道映射之间的相关性，以实现更好的预测性能。我们的方法在来自公共 CHB-MIT 头皮 EEG 数据集的 13 名患者上实现了 89.33%的敏感性、93.02%的特异性、91.26%的 AUC 和 92.07%的准确性。我们的实验表明，不同的 EEG 信号预测段长度是影响预测性能的一个重要因素。我们提出的方法具有竞争力，无需针对特定患者的工程设计即可实现良好的稳健性。
