* ESPCI ParisTech, PSL Research University, 10 rue Vauquelin, 75005 Paris, France.
† SIGMA (SIGnal processing and MAchine learning) Laboratory, 10 rue Vauquelin, 75231 Paris Cedex 05, France.
Int J Neural Syst. 2015 Dec;25(8):1550032. doi: 10.1142/S012906571550032X. Epub 2015 Aug 21.
In this paper, we introduce a novel entropy measure, termed epoch-based entropy. This measure quantifies disorder of EEG signals both at the time level and spatial level, using local density estimation by a Hidden Markov Model on inter-channel stationary epochs. The investigation is led on a multi-centric EEG database recorded from patients at an early stage of Alzheimer's disease (AD) and age-matched healthy subjects. We investigate the classification performances of this method, its robustness to noise, and its sensitivity to sampling frequency and to variations of hyperparameters. The measure is compared to two alternative complexity measures, Shannon's entropy and correlation dimension. The classification accuracies for the discrimination of AD patients from healthy subjects were estimated using a linear classifier designed on a development dataset, and subsequently tested on an independent test set. Epoch-based entropy reached a classification accuracy of 83% on the test dataset (specificity = 83.3%, sensitivity = 82.3%), outperforming the two other complexity measures. Furthermore, it was shown to be more stable to hyperparameter variations, and less sensitive to noise and sampling frequency disturbances than the other two complexity measures.
在本文中，我们引入了一种新的熵测度，称为基于时段的熵。该测度通过在通道间的静止时段上使用隐马尔可夫模型进行局部密度估计，从时间和空间水平上量化 EEG 信号的无序性。该研究基于从早期阿尔茨海默病（AD）患者和年龄匹配的健康受试者记录的多中心 EEG 数据库进行。我们研究了该方法的分类性能、对噪声的鲁棒性以及对采样频率和超参数变化的敏感性。该测度与两种替代复杂度测度（Shannon 熵和关联维数）进行了比较。使用设计在开发数据集上的线性分类器估计 AD 患者与健康受试者之间的分类准确性，然后在独立测试集上进行测试。基于时段的熵在测试数据集上达到 83%的分类准确性（特异性=83.3%，敏感性=82.3%），优于其他两种复杂度测度。此外，与其他两种复杂度测度相比，它对超参数变化更稳定，对噪声和采样频率干扰的敏感性更低。
