基于迁移学习的卷积神经网络高效检测癫痫高频生物标志物信号

IF 0.8 Q4 ENGINEERING, BIOMEDICAL Advanced Biomedical Engineering Pub Date : 2021-01-01 DOI:10.14326/abe.10.158
Yukinobu Takayanagi, Y. Takayama, K. Iijima, M. Iwasaki, Y. Ono
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引用次数: 0

摘要

高频振荡(HFO)是一种重要的电生理生物标志物,用于估计癫痫患者的癫痫区,但由于传统的基于一维光谱能量特征的自动检测方法存在较高的假阳性检出率,其临床应用受到限制。本研究的目的是将基于卷积神经网络(CNN)的分类器应用于传统方法检测到的候选信号,更准确、自动地提取hfo。我们采用了基于图像的CNN,因为hfo在时间和频率上都表现出局域化的功率分布,可以用于hfo的目视检测。为了减少一名患者所需的训练数据集数量,我们采用了现有自然图像分类器的迁移学习或另一名患者的CNN hfo分类器。我们将所提出的方法应用于2例局灶性癫痫患者术前检查的皮质电图数据。将自然图像鉴别器AlexNet移植到HFO分类器中,使用3000个训练数据集,准确率达到93.0±0.997%。在常规方法完成时,HFO的错误发现率(FDR)为78.0%,应用基于cnn的HFO分类器后,FDR显著提高至19.0±4.42%。当用一个患者训练过的HFO分类器使用另一个患者的训练数据集进一步重新学习时,当纳入200个或更多的训练数据集时,后者的HFO识别准确率始终在91.0%以上(最高93.3±0.967%)。这些结果表明,所提出的方法可以提供准确、自动和个性化的HFO分类器,同时将神经科医生从耗时的人工检测HFO信号中解放出来。
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Efficient Detection of High-frequency Biomarker Signals of Epilepsy by a Transfer-learning-based Convolutional Neural Network
High-frequency oscillation (HFO) is an important electrophysiological biomarker for estimating the epileptogenic zone in patients with epilepsy, but its clinical use is limited due to the high false-positive detection rate associated with conventional auto-detection methods based on one-dimensional spectral energy features. The purpose of this study was to apply a convolutional neural network (CNN)-based classifier to the candidate signals detected using conventional methods, and to extract HFOs more accurately and automatically. We adopted an image-based CNN because HFOs exhibit a localized power distribution in both time and frequency, which is utilized for the visual inspection of HFOs. To reduce the number of training datasets required for one patient, we employed transfer learning of an existing natural image classifier or the CNN HFO-classifier of another patient. We applied the proposed methods to the electrocorticography data of two patients with focal epilepsy who underwent pre-surgical examination. When the natural image discriminator AlexNet was transfer-learned to the HFO classifier, an accuracy of 93.0 ± 0.997% was achieved using 3000 training datasets. The false discovery rate (FDR) of HFO was 78.0% at the completion of the conventional method, which was significantly improved to 19.0 ± 4.42% after applying the CNN-based HFO classifier. When the HFO classifier trained with one patient was further relearned using the training datasets of another patient, the accuracy of determining HFOs in the latter patient was consistently above 91.0% (maximum 93.3 ± 0.967%) with the incorporation of 200 or more training datasets. These results suggest that the proposed method may provide an accurate, automatic, and personalized HFO classifier while liberating neurologists from the time-consuming manual detection of HFO signals for diagnosis.
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来源期刊
Advanced Biomedical Engineering
Advanced Biomedical Engineering ENGINEERING, BIOMEDICAL-
CiteScore
1.40
自引率
10.00%
发文量
15
审稿时长
15 weeks
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