Permutation Entropy-Based Interpretability of Convolutional Neural Network Models for Interictal EEG Discrimination of Subjects with Epileptic Seizures vs. Psychogenic Non-Epileptic Seizures

The differential diagnosis of epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES) may be difficult, due to the lack of distinctive clinical features. The interictal electroencephalographic (EEG) signal may also be normal in patients with ES. Innovative diagnostic tools that exploit non-linear EEG analysis and deep learning (DL) could provide important support to physicians for clinical diagnosis. In this work, 18 patients with new-onset ES (12 males, 6 females) and 18 patients with video-recorded PNES (2 males, 16 females) with normal interictal EEG at visual inspection were enrolled. None of them was taking psychotropic drugs. A convolutional neural network (CNN) scheme using DL classification was designed to classify the two categories of subjects (ES vs. PNES). The proposed architecture performs an EEG time-frequency transformation and a classification step with a CNN. The CNN was able to classify the EEG recordings of subjects with ES vs. subjects with PNES with 94.4% accuracy. CNN provided high performance in the assigned binary classification when compared to standard learning algorithms (multi-layer perceptron, support vector machine, linear discriminant analysis and quadratic discriminant analysis). In order to interpret how the CNN achieved this performance, information theoretical analysis was carried out. Specifically, the permutation entropy (PE) of the feature maps was evaluated and compared in the two classes. The achieved results, although preliminary, encourage the use of these innovative techniques to support neurologists in early diagnoses.     

Use of the internal standardization for difficult sampling by graphite furnace atomic absorption spectrometry

This work shows the potentiality of As as internal standard to compensate errors from sampling of sparkling drinking water samples in the determination of selenium by graphite furnace atomic absorption spectrometry. The mixture Pd(NO₃)₂/Mg(NO₃)₂ was used as chemical modifier. All samples and reference solutions were automatically spiked with 500 μg l⁻¹ As and 0.2% (v/v) HNO₃ by the autosampler, eliminating the need for manual dilutions. For 10 μl dispensed sample into the graphite tube, a good correlation (r=0.9996) was obtained between the ratio of analyte absorbance by the internal standard absorbance and the analyte concentrations. The relative standard deviations (R.S.D.) of measurements varied from 0.05 to 2% and from 1.9 to 5% (n=12) with and without internal standardization, respectively. The limit of detection (LD) based on integrated absorbance was 3.0 μg l⁻¹ Se. Recoveries in the 94-109% range for Se spiked samples were obtained. Internal standardization (IS) improved the repeatability of measurements and increased the lifetime of the graphite tube in ca. 15%.     

The effect of the hydration degree on the hydrothermal and thermo-oxidative stability of some collageneous matrices

The methods of the thermal analysis (TG, DTG and DTA) were used in order to investigate the effect of the hydration degree on the thermal behaviour of some collageneous matrices. It was pointed out that the degradation of hydrated collagen in the temperature range 20-400°C occurs through two successive processes accompanied by mass losses. The first process, consisting in the collagen dehydration, is endothermic and takes place in the temperature range ≈25 - ≈125°C. The second process is exothermic and consists in the decomposition and/or thermo-oxidation of dry collagen. The thermal parameters of both processes depend on the hydration degree of collagen. The observed dependencies show that the hydrothermal and thermo-oxidative stability of collagen are strongly correlated with its water content. This revised version was published online in July 2006 with corrections to the Cover Date.     

块α-对角占优矩阵的讨论

应用矩阵块对角占优理论,讨论了块α-对角占优矩阵之间的蕴含关系,并得到了条件最弱的块严格α1-双对角占优的两个等价表征,并作为应用给出了块H矩阵新的判定准则,最后用数值例子说明结果的有效性.     

一类特殊分块矩阵为循环矩阵的循环分块矩阵的几个性质

本文给出一类特殊分块矩阵为循环矩阵的循环分块矩阵的几个性质。     

一类半对称图的构造

称一个有限简单无向图X是半对称图,如果图X是正则的且边传递但非点传递.主要利用仿射几何构造了一类2p~n阶连通p~3。度的半对称图的无限族,其中p≥n≥8.     

Bool阵的逆阵

We give a characterization of a regular Boolean matrix and prove that AB = I Implies that BA = I, where A and B are Boolean matrices whose elements belong to a Boolean algebra of a set with more than two elements.     

正定Hermite矩阵的若干行列式不等式

本文对满足条件Ａ^Ｈ＝Ａ＞０，１／２（Ｂ＋Ｂ^Ｈ）≥０的矩阵Ａ，Ｂ，建立了四个行列式不等式．某些著名的行列式不等式和一些已知结用，均可作为其推论．     

自校正信息融合Kalman预报器

对含未知噪声统计的多传感器系统,用现代时间序列分析方法,基于滑动平均（MA）新息模型的在线辨识和求解相关函数矩阵方程组,可在线估计噪声统计,进而在按矩阵加权线性最小方差最优信息融合准则下,提出了自校正信息融合Kalman预报器。证明了它的收敛性,即它具有渐近最优性,且自校正融合Kalman预报器比每个局部自校正Kalman预报器精度高。一个目标跟踪系统的仿真例子说明了其有效性。     

Multiscale Weighted Permutation Entropy Analysis of Schizophrenia Magnetoencephalograms

Schizophrenia is a neuropsychiatric disease that affects the nonlinear dynamics of brain activity. The primary objective of this study was to explore the complexity of magnetoencephalograms (MEG) in patients with schizophrenia. We combined a multiscale method and weighted permutation entropy to characterize MEG signals from 19 schizophrenia patients and 16 healthy controls. When the scale was larger than 42, the MEG signals of schizophrenia patients were significantly more complex than those of healthy controls (p<0.004). The difference in complexity between patients with schizophrenia and the controls was strongest in the frontal and occipital areas (p<0.001), and there was almost no difference in the central area. In addition, the results showed that the dynamic range of MEG complexity is wider in healthy individuals than in people with schizophrenia. Overall, the multiscale weighted permutation entropy method reliably quantified the complexity of MEG from schizophrenia patients, contributing to the development of potential magnetoencephalographic biomarkers for schizophrenia.