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首页> 外文会议>Annual International Conference of the IEEE Engineering in Medicine and Biology Society >Wavelet Spectral Time-Frequency Training of Deep Convolutional Neural Networks for Accurate Identification of Micro-Scale Sharp Wave Biomarkers in the Post-Hypoxic-Ischemic EEG of Preterm Sheep
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Wavelet Spectral Time-Frequency Training of Deep Convolutional Neural Networks for Accurate Identification of Micro-Scale Sharp Wave Biomarkers in the Post-Hypoxic-Ischemic EEG of Preterm Sheep

机译：深度卷积神经网络的小波谱时频训练，用于准确鉴定早产儿缺氧缺血性脑电图中的微尺度尖波生物标志物

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Neonatal hypoxic-ischemic encephalopathy (HIE) evolves over different phases of time during recovery. Some neuroprotection treatments are only effective for specific, short windows of time during this evolution of injury. Clinically, we often do not know when an insult may have started, and thus which phase of injury the brain may be experiencing. To improve diagnosis, prognosis and treatment efficacy, we need to establish biomarkers which denote phases of injury. Our pre-clinical research, using preterm fetal sheep, show that micro-scale EEG patterns (e.g. spikes and sharp waves), superimposed on suppressed EEG background, primarily occur during the early recovery from an HI insult (0-6 h), and that numbers of events within the first 2 h are strongly predictive of neural survival. Thus, real-time automated algorithms that could reliably identify EEG patterns in this phase will help clinicians to determine the phases of injury, to help guide treatment options. We have previously developed successful automated machine learning approaches for accurate identification and quantification of HI micro-scale EEG patterns in preterm fetal sheep post-HI. This paper introduces, for the first time, a novel online fusion strategy that employs a high-level wavelet-Fourier (WF) spectral feature extraction method in conjunction with a deep convolutional neural network (CNN) classifier for accurate identification of micro-scale preterm fetal sheep post-HI sharp waves in 1024Hz EEG recordings, along with 256Hz down-sampled data. The classifier was trained and tested over 4120 EEG segments within the first 2 hours latent phase recordings. The WF-CNN classifier can robustly identify sharp waves with considerable high-performance of 99.86% in 1024Hz and 99.5% in 256Hz data. The method is an alternative deep-structure approach with competitive high-accuracy compared to our computationally-intensive WS-CNN sharp wave classifier.
机译：新生儿缺氧缺血性脑病（HIE）在恢复过程中会在不同的时间阶段演变。一些神经保护疗法仅在这种损伤演变过程中的特定短时间内有效。在临床上，我们通常不知道什么时候开始侮辱，因此不知道大脑可能正处于受伤的哪个阶段。为了提高诊断，预后和治疗效果，我们需要建立表示损伤阶段的生物标志物。我们的临床前研究使用早产儿胎羊，发现微小的EEG模式（例如尖峰和尖波）叠加在抑制的EEG背景上，主要发生在从HI损伤早期恢复（0-6小时）的过程中，并且前2小时内的事件数量强烈预测了神经存活。因此，可以在此阶段可靠地识别脑电图模式的实时自动化算法将帮助临床医生确定损伤的阶段，从而帮助指导治疗方案。我们先前已经开发了成功的自动化机器学习方法，用于在HI后早产胎羊中准确识别和量化HI微型脑电图模式。本文首次介绍了一种新颖的在线融合策略，该策略采用高级小波-傅里叶（WF）频谱特征提取方法与深度卷积神经网络（CNN）分类器相结合，可精确识别微尺度早产1024Hz脑电图记录中的高保真后胎羊，以及256Hz下采样数据。在最初的2小时潜伏期记录中，对分类器进行了4120个EEG段的训练和测试。 WF-CNN分类器可以稳健地识别尖锐波，在1024Hz和256Hz数据中具有99.86％和99.5％的高性能。与我们的计算密集型WS-CNN尖波分类器相比，该方法是一种具有竞争力的高精度的替代性深层结构方法。

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