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首页> 外文会议>International Conference on Electrical Engineering, Computing Science and Automatic Control >Pediatric Seizure Forecasting using Nonlinear Features and Gaussian Mixture Hidden Markov Models on Scalp EEG Signals
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Pediatric Seizure Forecasting using Nonlinear Features and Gaussian Mixture Hidden Markov Models on Scalp EEG Signals

机译：基于头皮脑电信号非线性特征和高斯混合隐马尔可夫模型的小儿惊厥预测

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Seizure forecasting systems have been studied in recent years for improving the quality of life for patients with epilepsy and gain further understanding about seizures. A common approach for this is the study of electroencephalography (EEG) recordings, using signal processing techniques and, more recently, machine learning algorithms. A four-stage system is developed for patient-specific seizure prediction; consisting of pre-processing, dimensionality reduction, feature extraction and classification between interictal and preictal EEG signals. A hybrid method using principal component analysis (PCA) and independent component analysis (ICA) is applied for dimensionality reduction. Nonlinear features are selected for the analysis and characterization of the signals. A Hidden Markov Model (HMM) with Gaussian mixture emissions is trained for each type of signal and evaluated as a classifier. A sensitivity of 0.95 and a specificity of 0.86 were achieved.
机译：近年来，已经研究了癫痫发作预测系统，以改善癫痫患者的生活质量，并进一步了解癫痫发作。一种常见的方法是使用信号处理技术以及最近的机器学习算法来研究脑电图（EEG）记录。开发了一个四阶段系统，用于特定患者的癫痫发作预测;包括预处理，降维，特征提取以及脑室和脑室EEG信号之间的分类。使用主成分分析（PCA）和独立成分分析（ICA）的混合方法可降低尺寸。选择非线性特征用于信号的分析和表征。针对每种信号类型训练具有高斯混合辐射的隐马尔可夫模型（HMM），并作为分类器进行评估。灵敏度为0.95，特异性为0.86。

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