A Stacked Sparse Autoencoder-Based Detector for Automatic Identification of Neuromagnetic High Frequency Oscillations in Epilepsy.
A Stacked Sparse Autoencoder-Based Detector for Automatic Identification of Neuromagnetic High Frequency Oscillations in Epilepsy.
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DOI：
10.1109/tmi.2018.2836965
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发表时间：
2018-11
影响因子：
10.6
通讯作者：
Gao Y
中科院分区：
文献类型：
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作者：
Guo J;Yang K;Liu H;Yin C;Xiang J;Li H;Ji R;Gao Y
高频振荡(HFO)是一种自发的脑磁图(MEG)模式，已被公认为是识别癫痫灶的生物标志物。脑磁图信号中HFO的正确检测对于准确和及时的临床评估至关重要。由于HFOS的目视检查费时、容易出错，而且评价者之间的可靠性较差，因此在临床实践中非常需要一种自动的HFOS检测器。然而，现有的HFO检测方法可能不适用于具有噪声背景活动的脑磁图信号。因此，我们采用了堆叠式稀疏自动编码器(SSAE)，并提出了一种基于堆叠式稀疏自动编码器(SSAE)的MEG HFOS(SMO)检测器，以便于临床检测HFO。据我们所知，这是首次尝试使用深度学习方法在脑磁图中进行HFO检测。经过结构优化，我们提出的SMO检测器的准确率达到89.9%，敏感度88.2%，特异度91.6%，优于其他经典的同级模型。此外，我们还使用各种验证方案测试了模型的性能一致性。性能指标的分布表明，我们的模型可以获得稳定的性能。
High-frequency oscillations (HFOs) are spontaneous magnetoencephalography (MEG) patterns that have been acknowledged as a putative biomarker to identify epileptic foci. Correct detection of HFOs in the MEG signals is crucial for accurate and timely clinical evaluation. Since the visual examination of HFOs is time-consuming, error-prone and with poor inter-reviewer reliability, an automatic HFOs detector is highly desirable in clinical practice. However, existing approaches for HFOs detection may not be applicable for MEG signals with noisy background activity. Therefore, we employ the stacked sparse autoencoder (SSAE)and propose an SSAE-based MEG HFOs (SMO) detector to facilitate the clinical detection of HFOs. To the best of our knowledge, this is the first attempt to conduct HFOs detection in MEG using deep learning methods. After configuration optimization, our proposed SMO detector outperformed other classic peer models by achieving 89.9% in accuracy, 88.2% in sensitivity, and 91.6% in specificity. Furthermore, we have tested the performance consistency of our model using various validation schemes. The distribution of performance metrics demonstrate that our model can achieve steady performance.