论文标题
明显的低维光谱时间特征用于癫痫发作
Significant Low-dimensional Spectral-temporal Features for Seizure Detection
论文作者
论文摘要
脑电图（EEG）信号中的癫痫发作检测是一项艰巨的任务，这是由于非疾病的癫痫发作活动以及它们自然界中的随机和非平稳性特征。据信，关节光谱时期特征包含足够有力的特征信息，以缺乏癫痫发作。但是，由此产生的高维特征涉及冗余信息，需要大量的计算负载。在这里，我们在小波变换系数（MS-WTC）的均值偏差（MS-WTC）方面发现了显着的低维光谱特征，该特征基于新的缺勤癫痫发作检测框架。 EEG信号转变为光谱时端结构域，其低维特征供应到卷积神经网络中。在广泛使用的基准数据集以及中国301医院的临床数据集上实现了出色的检测性能。对于前者，对七个分类任务的精度从99.8％到100.0％进行了评估，而对于后者，该方法的平均准确性为94.7％，这些方法具有低维时和光谱特征的其他方法。两个癫痫发作数据集的实验结果显示了我们提出的MS-WTC方法的可靠性，效率和稳定性，从而验证了提取的低维光频谱特征的重要性。
Seizure onset detection in electroencephalography (EEG) signals is a challenging task due to the non-stereotyped seizure activities as well as their stochastic and non-stationary characteristics in nature. Joint spectral-temporal features are believed to contain sufficient and powerful feature information for absence seizure detection. However, the resulting high-dimensional features involve redundant information and require heavy computational load. Here, we discover significant low-dimensional spectral-temporal features in terms of mean-standard deviation of wavelet transform coefficient (MS-WTC), based on which a novel absence seizure detection framework is developed. The EEG signals are transformed into the spectral-temporal domain, with their low-dimensional features fed into a convolutional neural network. Superior detection performance is achieved on the widely-used benchmark dataset as well as a clinical dataset from the Chinese 301 Hospital. For the former, seven classification tasks were evaluated with the accuracy from 99.8% to 100.0%, while for the latter, the method achieved a mean accuracy of 94.7%, overwhelming other methods with low-dimensional temporal and spectral features. Experimental results on two seizure datasets demonstrate reliability, efficiency and stability of our proposed MS-WTC method, validating the significance of the extracted low-dimensional spectral-temporal features.
