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A progressive deep wavelet cascade classification model for epilepsy detection
Artificial Intelligence in Medicine ( IF 6.1 ) Pub Date : 2021-05-28 , DOI: 10.1016/j.artmed.2021.102117
Hong He 1 , Xinyue Liu 2 , Yong Hao 3
Affiliation  

Automatic epileptic seizure detection according to EEG recordings is helpful for neurologists to identify an epilepsy occurrence in the initial anti-epileptic treatment. To quickly and accurately detect epilepsy, we proposed a progressive deep wavelet cascade classification model (PDWC) based on the discrete wavelet transform (DWT) and Random Forest (RF). Different from current deep networks, the PDWC mimics the progressive object identification process of human beings with recognition cycles. In every cycle, enhanced wavelet energy features at a specific scale were extracted by DWT and input into a set of cascade RF classifiers to realize one recognition. The recognition accuracy of PDWC is gradually improved by the fusion of classification results produced by multiple recognition cycles. Moreover, the cascade structure of PDWC can be automatically determined by the classification accuracy increment between layers. To verify the performance of the PDWC, we respectively applied five traditional schemes and four deep learning schemes to four public datasets. The results show that the PDWC is not only superior than five traditional schemes, including KNN, Bayes, DT, SVM, and RF, but also better than deep learning methods, i.e. convolutional neural network (CNN), Long Short-Term Memory (LSTM), multi-Grained Cascade Forest (gcForest) and wavelet cascade model (WCM). The mean accuracy of PDWC for all subjects of all datasets reaches to 0.9914. With a flexible structure and less parameters, the PDWC is more suitable for the epilepsy detection of diverse EEG signals.



中文翻译:

用于癫痫检测的渐进式深度小波级联分类模型

根据 EEG 记录自动检测癫痫发作有助于神经科医生在初始抗癫痫治疗中识别癫痫发生。为了快速准确地检测癫痫,我们提出了一种基于离散小波变换 (DWT) 和随机森林 (RF) 的渐进式深度小波级联分类模型 (PDWC)。与当前的深度网络不同,PDWC 模仿人类具有识别周期的渐进式物体识别过程。在每个循环中,DWT 提取特定尺度的增强小波能量特征,并输入到一组级联射频分类器中以实现一次识别。PDWC的识别精度是通过多次识别循环产生的分类结果的融合而逐步提高的。而且,PDWC 的级联结构可以通过层间分类精度增量自动确定。为了验证 PDWC 的性能,我们分别将五种传统方案和四种深度学习方案应用于四个公共数据集。结果表明,PDWC 不仅优于 KNN、贝叶斯、DT、SVM 和 RF 五种传统方案,而且优于深度学习方法,即卷积神经网络 (CNN)、长短期记忆 (LSTM) )、多粒度级联森林 (gcForest) 和小波级联模型 (WCM)。PDWC 对所有数据集的所有受试者的平均准确度达到 0.9914。PDWC结构灵活,参数少,更适用于多种脑电信号的癫痫检测。为了验证 PDWC 的性能,我们分别将五种传统方案和四种深度学习方案应用于四个公共数据集。结果表明,PDWC 不仅优于 KNN、贝叶斯、DT、SVM 和 RF 五种传统方案,而且优于深度学习方法,即卷积神经网络 (CNN)、长短期记忆 (LSTM) )、多粒度级联森林 (gcForest) 和小波级联模型 (WCM)。PDWC 对所有数据集的所有受试者的平均准确度达到 0.9914。PDWC结构灵活,参数少,更适用于多种脑电信号的癫痫检测。为了验证 PDWC 的性能,我们分别将五种传统方案和四种深度学习方案应用于四个公共数据集。结果表明,PDWC 不仅优于 KNN、贝叶斯、DT、SVM 和 RF 五种传统方案,而且优于深度学习方法,即卷积神经网络 (CNN)、长短期记忆 (LSTM) )、多粒度级联森林 (gcForest) 和小波级联模型 (WCM)。PDWC 对所有数据集的所有受试者的平均准确度达到 0.9914。PDWC结构灵活,参数少,更适用于多种脑电信号的癫痫检测。结果表明,PDWC 不仅优于 KNN、贝叶斯、DT、SVM 和 RF 五种传统方案,而且优于深度学习方法,即卷积神经网络 (CNN)、长短期记忆 (LSTM) )、多粒度级联森林 (gcForest) 和小波级联模型 (WCM)。PDWC 对所有数据集的所有受试者的平均准确度达到 0.9914。PDWC结构灵活,参数少,更适用于多种脑电信号的癫痫检测。结果表明,PDWC 不仅优于 KNN、贝叶斯、DT、SVM 和 RF 五种传统方案,而且优于深度学习方法,即卷积神经网络 (CNN)、长短期记忆 (LSTM) )、多粒度级联森林 (gcForest) 和小波级联模型 (WCM)。PDWC 对所有数据集的所有受试者的平均准确度达到 0.9914。PDWC结构灵活,参数少,更适用于多种脑电信号的癫痫检测。

更新日期:2021-06-10
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