机构：[1]Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China首都医科大学附属天坛医院[2]China National Clinical Research Center for Neurological Diseases, Beijing, China,[3]Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China[4]Department of Neurology, Xingtai People’s Hospital, Hebei, China[5]Department of Functional Neurosurgery, Beijing Haidian Hospital, Beijing, China[6]Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China神经科系统神经内科首都医科大学宣武医院[7]Department of Neurosurgery, Capital Institute of Pediatrics, Children’s Hospital, Beijing, China首都儿科研究所首都医科大学附属北京儿童医院[8]Department of Neurology, National Center for Children’s Health, Beijing Children’s Hospital, Capital Medical University, Beijing, China首都医科大学附属北京儿童医院[9]Guangzhou Laboratory, Guangzhou, China[10]College of Electrical and Control Engineering, North China University of Technology, Beijing, China
Accurately identifying epileptogenic zone (EZ) using high-frequency oscillations (HFOs) is a challenge that must be mastered to transfer HFOs into clinical use. We analyzed the ability of a convolutional neural network (CNN) model to distinguish EZ and non-EZ HFOs. Nineteen medically intractable epilepsy patients with good surgical outcomes 2 years after surgery were studied. Five-minute interictal intracranial electroencephalogram epochs of slow-wave sleep were selected randomly. Then 5 s segments of ripples (80-200 Hz) and fast ripples (FRs, 200-500 Hz) were detected automatically. The EZs and non-EZs were identified using the surgery resection range. We innovatively converted all epochs into four types of images using two scales: original waveforms, filtered waveforms, wavelet spectrum images, and smoothed pseudo Wigner-Ville distribution (SPWVD) spectrum images. Two scales were fixed and fitted scales. We then used a CNN model to classify the HFOs into EZ and non-EZ categories. As a result, 7,000 epochs of ripples and 2,000 epochs of FRs were randomly selected from the EZ and non-EZ data for analysis. Our CNN model can distinguish EZ and non-EZ HFOs successfully. Except for original ripple waveforms, the results from CNN models that are trained using fixed-scale images are significantly better than those from models trained using fitted-scale images (p < 0.05). Of the four fixed-scale transformations, the CNN based on the adjusted SPWVD (ASPWVD) produced the best accuracies (80.89 +/- 1.43% and 77.85 +/- 1.61% for ripples and FRs, respectively, p < 0.05). The CNN using ASPWVD transformation images is an effective deep learning method that can be used to classify EZ and non-EZ HFOs.</p>
基金：
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [81801280, 61803003, 81601126, 81971202]; Youth Research Fund of Beijing Tiantan Hospital affiliated to Capital Medical University [2017-YQN-01]; National Key R&D Program of China [2017YFC1307500]; Capital Health Research and Development of Special grants [2016-1-2011, 2020-1-2013]; Beijing-Tianjin-Hebei Cooperative Basic Research Program [H2018206435]; Beijing Natural Science FoundationBeijing Natural Science Foundation [Z200024]
第一作者机构：[1]Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China[2]China National Clinical Research Center for Neurological Diseases, Beijing, China,
通讯作者：
通讯机构：[1]Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China[2]China National Clinical Research Center for Neurological Diseases, Beijing, China,[3]Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China
推荐引用方式(GB/T 7714)：
Ren Guoping,Sun Yueqian,Wang Dan,et al.Identification of Epileptogenic and Non-epileptogenic High-Frequency Oscillations Using a Multi-Feature Convolutional Neural Network Model[J].FRONTIERS IN NEUROLOGY.2021,12:doi:10.3389/fneur.2021.640526.
APA：
Ren, Guoping,Sun, Yueqian,Wang, Dan,Ren, Jiechuan,Dai, Jindong...&Wang, Qun.(2021).Identification of Epileptogenic and Non-epileptogenic High-Frequency Oscillations Using a Multi-Feature Convolutional Neural Network Model.FRONTIERS IN NEUROLOGY,12,
MLA：
Ren, Guoping,et al."Identification of Epileptogenic and Non-epileptogenic High-Frequency Oscillations Using a Multi-Feature Convolutional Neural Network Model".FRONTIERS IN NEUROLOGY 12.(2021)
