Synaptic plasticity based model for epileptic seizures

In this paper, a new dynamic model describing the epileptic seizure initiation through transition from interictal to ictal state in a brain predisposed to epilepsy is suggested. The model follows Freeman’s approach where the brain is viewed as a network of interconnected oscillators. The proposed nonlinear model is experimentally motivated and relies on changes in synaptic strength in response to excitatory spikes. This model exhibits a threshold beyond which a bifurcation toward a short-term plasticity state occurs leading to seizure onset. A resulting explanatory assumption is that when considering epilepsy, brain regions are characterized by abnormally low thresholds toward short-term synaptic plasticity. It is shown by simulation that the proposed model enables some experimentally observed qualitative features to be reproduced. Moreover, a preliminary discussion on the impact of the underlying assumptions on the fundamental issue of seizure control is proposed through an EEG based feedback control scheme.     

A feedback control systems view of epileptic seizures

To understand basic functional mechanisms that cause epileptic seizures, the paper discusses some key features of theoretical brain functioning models. The hypothesis is put forward that a plausible reason for seizures is pathological feedback in brain circuitry. The analysis of such circuitry has an interesting physical interpretation and may be used to cure epilepsy. The paper is dedicated to the 70th birthday of Academician I. V. Sergienko. Published in Kibernetika i Sistemnyi Analiz, No. 4, pp. 26–40, July–August 2006.     

石灰石的氧化钙含量超标的原因分析

某燃煤电厂烟气脱硫采用石灰石—石膏湿法脱硫工艺。针对在石灰石氧化钙含量测定时发现一批次的石灰石氧化钙含量超标,氧化钙含量高达60%,且折算成碳酸钙含量则大幅超过100%,明显不符合常理的问题。通过一系列的化学分析试验,验证了试验方法、检测试剂、试验过程以及铁铝杂质含量并不是造成氧化钙含量超标的原因。最后通过对检测原理的进一步分析和试验,得出主要原因是石灰石中掺入了生石灰或熟石灰,人为添加钙成分类似于牛奶中添加三聚氰胺,干扰了石灰石中氧化钙的测定。通过定性检测样品的水溶液是否含有钙离子,可快速有效的检测石灰石中是否掺入了生石灰或熟石灰,保证了石灰石的质量验收和脱硫系统的安全运行,很大程度上弥补了国标测定方法《化工用石灰石中氧化钙和氧化镁含量的测定》的不足     

Adaptive multi-parent crossover GA for feature optimization in epileptic seizure identification

EEG signal analysis involves multi-frequency non-stationary brain waves from multiple channels. Segmenting these signals, extracting features to obtain the important properties of the signal and classification are key aspects of detecting epileptic seizures. Despite the introduction of several techniques, it is very challenging when multiple EEG channels are involved. When many channels exist, a spatial filter is required to eliminate noise and extract relevant information. This adds a new dimension of complexity to the frequency feature space. In order to stabilize the classifier of the channels, feature selection is very important. Furthermore, and to improve the performance of a classifier, more data is required from EEG channels for complex problems. The increase of such data poses some challenges as it becomes difficult to identify the subject dependent bands when the channels increase. Hence, an automated process is required for such identification.The proposed approach in this work tends to tackle the multiple EEG channels problem by segmenting the EEG signals in the frequency domain based on changing spikes rather than the traditional time based windowing approach. While to reduce the overall dimensionality and preserve the class-dependent features an optimization approach is used. This process of selecting an optimal feature subset is an optimization problem. Thus, we propose an adaptive multi-parent crossover Genetic Algorithm (GA) for optimizing the features used in classifying epileptic seizures. The GA-based approach is used to optimize the various features obtained. It encodes the temporal and spatial filter estimates and optimize the feature selection with respect to the classification error. The classification was done using a Support Vector Machine (SVM).The proposed technique was evaluated using the publicly available epileptic seizure data from the machine learning repository of the UCI center for machine learning and intelligent systems. The proposed approach outperforms other ones and achieved a high level of accuracy. These results, indicate the ability of a multi-parent crossover GA in optimizing the feature selection process in EEG classification.     

神经元钙振荡的非线性动力学研究

在神经元的生理实验中经常观察到丰富的钙振荡模式,本文详细综述了产生这些现象的钙流交换机理和各类通道调节机理,以及描述这些生理机理的数学表达式.介绍三类典型的研究钙振荡的非线性动力学模型,即电压动力学与钙动力学相耦合的模型,多个钙存储单元之间钙流平衡的模型和考虑信使物质IP3的振荡与钙振荡相互作用的模型;并针对第一个模型简要地讨论其复杂的动力学行为;最后对神经元钙振荡的非线性动力学研究提出了一些展望.     

Recurrent neural network based prediction of epileptic seizures in intra- and extracranial EEG

Predicting the onset of epileptic seizure is an important and difficult biomedical problem, which has attracted substantial attention of the intelligent computing community over the past two decades. We apply recurrent neural networks (RNN) combined with signal wavelet decomposition to the problem. We input raw EEG and its wavelet-decomposed subbands into RNN training/testing, as opposed to specific signal features extracted from EEG. To the best of our knowledge this approach has never been attempted before. The data used included both scalp and intracranial EEG recordings obtained from two epileptic patients. We demonstrate that the existence of a “preictal” stage (immediately preceding seizure) of some minutes duration is quite feasible.     

An evaluation of quantum neural networks in the detection of epileptic seizures in the neonatal electroencephalogram

This paper presents the results of an experimental study that evaluated the ability of quantum neural networks (QNNs) to capture and quantify uncertainty in data and compared their performance with that of conventional feedforward neural networks (FFNNs). In this work, QNNs and FFNNs were trained to classify short segments of epileptic seizures in neonatal EEG. The experiments revealed significant differences between the internal representations created by trained QNNs and FFNNs from sample information provided by the training data. The results of this experimental study also confirmed that the responses of trained QNNs are more reliable indicators of uncertainty in the input data compared with the responses of trained FFNNs.     

一种用于在体监测的新型钙离子传感器的研制

将钙离子敏感试剂(ETH129),DBP,NaTP,PVC按一定比例制成敏感膜,复合在高纯度铂丝基体电极上,制成了一种涂丝钙离子传感器。测试结果表明:该传感器的线性范围为10-6~10-2mol/L,响应斜率为20.35mV/Decup le,响应时间约为10 s。该传感器有好的选择性、稳定性及重现性,它的针状结构特点适于对生物体内的钙离子浓度进行在体动态监测。     

钙离子传感针的研制

为了对针刺治疗过程中腧穴组织内钙离子活度进行在体实时动态监测 ,研制了钙离子传感针 ,它是以针灸针为基体 ,将钙离子敏感材料 (即活性剂 )、增塑剂、添加剂、PVC粉按一定比例配制的PVC膜复合其上而制成的一类全固态离子选择微电极 ,其线性响应范围为 10 -5～ 10 -1mol/L ,响应斜率为 2 3mV(30℃ ) ,检测下限为 4 .4× 10 -6mol/L ,响应时间小于 30s,有较好的选择性、稳定性和适宜的pH范围     

Process development and accurate low‐cost characterization for OLED sealants by using a calcium test

Abstract— A calcium measurement setup was built for testing encapsulation especially for OLED applications. This setup is able to measure both reflective and transmissive cells. For the characterization of sealants, a method to compare them with other sealing products will be described. This includes the use of spacers, a homogeneous surface energy, and the geometry of the sealant line. The effects of different geometries will be discussed. The setup was designed to achieve good accuracy at a very reasonable component cost, which will allow other facilities to replicate this setup. Therefore, the construction plan as well as the list of components can be downloaded from our website (Ref. 3).