A quadratic linear-parabolic model-based EEG classification to detect epileptic seizures

The two-point central difference is a common algorithm in biological signal processing and is particularly useful in analyzing physiological signals.In this paper,we develop a model-based classification method to detect epileptic seizures that relies on this algorithm to filter electroencephalogram(EEG) signals.The underlying idea was to design an EEG filter that enhances the waveform of epileptic signals.The filtered signal was fitted to a quadratic linear-parabolic model using the curve fitting technique.The model fitting was assessed using four statistical parameters,which were used as classification features with a random forest algorithm to discriminate seizure and non-seizure events.The proposed method was applied to 66 epochs from the Children Hospital Boston database.Results showed that the method achieved fast and accurate detection of epileptic seizures,with a92% sensitivity,96% specificity,and 94.1% accuracy.

Earthquake Electromagnetic Precursor Anomalies Detected by a New Ground-based Observation Network

It is a debated topic if there are any observable precursor anomalies prior to the earthquake(EQ hereafter)and if the stronger EQ can be successfully predicted.During last few decades quite a lot of observable electromagnetic(EM)precursors were published by using techniques equipped in either satellites or on ground-based stations.But there are only a few cases that the shortterm precursor anomalies of EM field before earthquakes were observed by using alternate EM fields on ground.This paper will present a new EM observation network built in recent years and show a new finding of EM field with the variation of a one-year cycle observed using the network.As an example,the short-term precursor anomalies of apparent resistivity before the Yangbi EQ(Ms 5.1)occurred on March 27,2017 in Yunnan Province will be studied.The observed anomalous phenomena indicate that the anomaly before the EQ can be captured only if reasonable effective methods including sophisticated analytical techniques are used,and it is believed that continuously observed data on the fixed observation network for a long time is an effective means for studying anomalies that appeared before earthquakes.This network can also play an important role in studying the EM environment from space.

中国地震交变电磁场观测数据处理技术新进展

交变电磁场是对地震事件反应最灵敏的物理场之一,已观测到与地震有关大量的电磁场异常现象.随着人工源极低频电磁技术和卫星电磁观测技术在地震观测中被逐步应用研究,交变电磁场法用于地震观测的数据量呈数量级的增加,急需发展适于海量数据或大数据的信息处理和挖掘技术.本文介绍了自适应滤波技术及其在地面电磁场观测数据处理中的应用研究,以及小波分析技术在地面和卫星观测数据分析中的应用研究,表明在对实际测量数据处理中,自适应滤波技术可以有效识别天然源信号和人工源信号,并且提高了电磁场频谱和视电阻率计算的信噪比,小波分析等技术可以发现电磁异常现象与地震事件可能存在的相关性.这些技术可能成为对海量观测数据处理分析的有效方法.

Advances in alternating electromagnetic field data processing for earthquake monitoring in China

The alternating electromagnetic(EM) field is one of the most sensitive physical fields related to earthquakes. There have been a number of publications reporting EM anomalies associated with earthquakes. With increasing applications and research of artificial-source extremely low frequency EM and satellite EM technologies in earthquake studies, the amount of observed data from the alternating EM method increases rapidly and exponentially, so it is imperative to develop suitable and effective methods for processing and analyzing the influx of big data. This paper presents research on the self-adaptive filter and wavelet techniques and their applications to analyzing EM data obtained from ground measurements and satellite observations, respectively. Analysis results show that the self-adaptive filter method can identify both natural- and artificial-source EM signals, and enhance the ratio between signal and noise of EM field spectra, apparent resistivity, and others. The wavelet analysis is capable of detecting possible correlation between EM anomalies and seismic events. These techniques are effective in processing and analyzing massive data obtained from EM observations.

Adaptivity and A Posteriori Error Control for Bifurcation Problems Ⅰ:The Bratu Problem

This article is concerned with the numerical detection of bifurcation points of nonlinear partial differential equations as some parameter of interest is varied.In particular,we study in detail the numerical approximation of the Bratu problem,based on exploiting the symmetric version of the interior penalty discontinuous Galerkin finite element method.A framework for a posteriori control of the discretization error in the computed critical parameter value is developed based upon the application of the dual weighted residual(DWR)approach.Numerical experiments are presented to highlight the practical performance of the proposed a posteriori error estimator.