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首页> 外文会议>Annual International Conference of the IEEE Engineering in Medicine and Biology Society >Feature Generation and Dimensionality Reduction using the Discrete Spectrum of the Schrödinger Operator for Epileptic Spikes Detection
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Feature Generation and Dimensionality Reduction using the Discrete Spectrum of the Schrödinger Operator for Epileptic Spikes Detection

机译：使用Schrödinger算子的离散频谱进行癫痫峰值检测的特征生成和降维

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摘要

Epilepsy is a neurological disorder classified as the second most serious neurological disease known to humanity, after stroke. Magnetoencephalography (MEG) is performed to localize the epileptogenic zone in the brain. However, the detection of epileptic spikes requires the visual assessment of long MEG recordings. This task is time-consuming and might lead to wrong decisions. Therefore, the introduction of effective machine learning algorithms for the quick and accurate epileptic spikes detection from MEG recordings would improve the clinical diagnosis of the disease. The efficiency of machine learning based algorithms requires a good characterization of the signal by extracting pertinent features. In this paper, we propose new sets of features for MEG signals. These features are based on a Semi-Classical Signal Analysis (SCSA) method, which allows a good characterization of peak shaped signals. Moreover, this method improves the spike detection accuracy and reduces the feature vector size. We could achieve up to 93.68% and 95.08% in average sensitivity and specificity, respectively. We used the 5-folds cross-validation applied to a balanced dataset of 3104 frames, extracted from eight healthy and eight epileptic subjects with a frame size of 100 samples with a step size of 2 samples, using Random Forest (RF) classifier.
机译：癫痫病是继中风后被分类为人类已知的第二大最严重的神经系统疾病。进行脑磁图（MEG）定位大脑中的癫痫发生区。但是，检测癫痫发作峰值需要对长时间的MEG记录进行视觉评估。此任务很耗时，可能导致错误的决定。因此，引入有效的机器学习算法以从MEG记录中快速准确地检测癫痫发作，将改善该疾病的临床诊断。基于机器学习的算法的效率要求通过提取相关特征来对信号进行良好的表征。在本文中，我们提出了MEG信号的新功能集。这些功能基于半经典信号分析（SCSA）方法，可以很好地表征峰形信号。而且，该方法提高了尖峰检测精度并减小了特征向量的大小。我们的平均敏感性和特异性分别可以达到93.68％和95.08％。我们使用随机森林（RF）分类器，将5倍交叉验证应用于3104帧的平衡数据集，该数据是从8个健康的和8个癫痫的受试者中抽取的，这些受试者的帧大小为100个样本，步长为2个样本。

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