Jeleazcov C, Egner S, Bremer F, Schwilden H
Klinik für Anästhesiologie der Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen.
Biomed Tech (Berl). 2004 May;49(5):125-31. doi: 10.1515/BMT.2004.025.
The computer-aided detection of artefacts became an essential task with increasing automation of quantitative electroencephalogram (EEG) analysis during anaesthesiological applications. The different algorithms published so far required individual manual adjustment or have been based on limited decision criteria. In this study, we developed an artificial neural networks-(ANN-)aided method for automated detection of artefacts and EEG suppression periods. 72 hr EEG recorded before, during and after anaesthesia with propofol have been evaluated. Selected parameterized patterns of 0.25 s length were used to train the ANN (22 input, 8 hidden and 4 output neurons) with error back propagation. The detection performance of the ANN-aided method was tested with processing epochs between 1 to10 s. Related to examiner EEG evaluation, the average detection performance of the method was 72% sensitivity and 80% specificity for artefacts and 90% sensitivity and 92% specificity for EEG suppression. The improvement in signal-to-noise ratio with automated artefact processing was 1.39 times for the spectral edge frequency 95 (SEF95) and 1.89 times for the approximate entropy (ApEn). We conclude that ANN-aided preprocessing provide an useful tool for automated EEG evaluation in anaesthesiological applications.
随着麻醉应用中定量脑电图（EEG）分析自动化程度的提高，计算机辅助伪迹检测成为一项重要任务。迄今为止发布的不同算法需要单独手动调整，或者基于有限的决策标准。在本研究中，我们开发了一种基于人工神经网络（ANN）的方法，用于自动检测伪迹和脑电图抑制期。我们对丙泊酚麻醉前、麻醉期间和麻醉后记录的72小时脑电图进行了评估。使用选定的0.25秒长的参数化模式，通过误差反向传播训练人工神经网络（22个输入神经元、8个隐藏神经元和4个输出神经元）。使用1至10秒的处理时段测试了人工神经网络辅助方法的检测性能。与检查者的脑电图评估相关，该方法对伪迹的平均检测性能为灵敏度72%、特异性80%，对脑电图抑制的灵敏度为90%、特异性为92%。自动伪迹处理使频谱边缘频率95（SEF95）的信噪比提高了1.39倍，近似熵（ApEn）提高了1.89倍。我们得出结论，人工神经网络辅助预处理为麻醉应用中的自动脑电图评估提供了一个有用的工具。
