Estimation of Sleep Stages by an Artificial Neural Network Employing EEG, EMG and EOG

Analysis and classification of sleep stages is essential in sleep research. In this particular study, an alternative system which estimates sleep stages of human being through a multi-layer neural network (NN) that simultaneously employs EEG, EMG and EOG. The data were recorded through polisomnography device for 7 h for each subject. These collective variant data were first grouped by an expert physician and the software of polisomnography, and then used for training and testing the proposed Artificial Neural Network (ANN). A good scoring was attained through the trained ANN, so it may be put into use in clinics where lacks of specialist physicians.     

人工神经网络在红霉素发酵过程状态预估中的应用

探索了动态ＢＰ网络和ＲＢＦ网络在红霉素发酵过程状态预估中的应用,比较了它们的收敛速度和学习能力。结果表明,ＢＰ网络和ＲＢＦ网络都具有相当好的学习能力,但ＲＢＦ网络的收敛速度更快,训练好的神经网络,在红霉素发酵过程中可在线预估出红霉素效价、葡萄糖浓度、ＮＨ２－Ｎ浓度、丙醇浓度和菌体浓度等参数值,并可在进上步的过程优化和控制中应用。     

人工神经网络在中医诊断信息化中的应用

中医学是一个非常复杂的系统,临床证候之间、临床证候与诊断目标之间、临床证候与方药之间的关系具有非线性、复杂性、模糊性、非定量的特点.人工神经网络能从海量数据中提取隐含的有意义的知识,能模拟这种非线性映射关系,建立诊断、判别模型,做出前瞻性决策,正是这种优势使得人工神经网络技术有可能为解决中医脉象辨识信息化、中医舌象辨识信息化、中医证候辨识信息化中权值难以明确的问题提供更为科学的方法与途径.     

脉象人工神经网络分析系统模型

提出一种以人工神经网络为手段的脉象智能分析系统模型。由于脉象模糊性处理的要求,作者建立了其人工神经网络辨识系统。并且不以单一脉本身为处理对象,而考虑它是否是某些足以将其区分的特征的组合,设计了与此相适应的网络系统结构;通过对采样数据的实际处理,按照各特征网络训练的要求,形成了样本数据库;探讨了神经网络用于脉象分析的特点,系统分析结果证实了人工神经网络用于脉象分析的可行性和优势     

Using an Artificial Neural Network to Define the Planning Target Volume in Radiotherapy

A neural network for predicting the planning target volume in radiotherapy from the shape of the detected tumor is designed and tested in this research project. The proposed neural network is able to generalize expert medical knowledge and predict the planning target volume from a three-dimensional image of the detected tumor. Initial results for simple shaped brain tumors are presented in this paper.     

基于神经网络的非正常工况预报

分析了PTA生产中氧化反应器尾氧浓度的影响因素，提出一种用小波分析对数据进行降噪处理的方法。采用BP神经网络并对其进行了一定程度的改进。通过降噪前后的网络仿真结果对比，表明基于小波降噪的神经网络具有更好的精度和更强的泛化能力。用此网络预测尾氧浓度，实现了对非正常工况的预报。通过实际对比，表明该网络能够较理想地预报出非正常情况。     

基于人工神经网络的冠心病鉴别诊断方

人工神经网络在冠心病诊断领域已取得广泛应用并取得良好效果,但其在冠心病鉴别诊断领域的应用仍为空白。本文从冠心病的鉴别诊断入手,选用基于LM算法的人工神经网络,结合目前中国医疗场所对冠心病及其他疾病的诊断方法,就如何运用人工神经网络实现冠心病的鉴别诊断进行了理论上的探讨,并给出了具体的样本信息数字化方法,填补了国内相关领域研究的空白。     

微晶蜡非催化氧化反应的神经网络模型

对微晶蜡进行了非催化空气氧化，并利用人工神经网络将氧化微晶蜡的酸值、酯值及微晶蜡的氧化条件（反应温度、空气流量和反应时间）进行关联，建立了微晶蜡非催化氧化的酸值、酯值的神经网络模型，并用该模型预测了反应条件对微晶蜡氧化反应过程的影响。结果表明，该模型不但具有较高的计算精度，而且具有满意的预测能力。     

医院感染人工神经网络预测模型的构建

目的:构建住院患者医院感染人工神经网络预测模型。方法:以入住ICU超过48 h患者为研究对象,构建医院感染人工神经网络预测模型,对模型进行拟合优度检验、ROC曲线下面积分析。结果:构建的神经网络模型结构为{25-4-1},对感染结局影响最大的因素依次为ICU入住时间、抗菌药物使用情况、基础疾病诊断、年龄、使用插管等,模型ROC曲线下面积为0.861。结论:构建的入住ICU患者医院感染人工神经网络模型预测拟合度较好。     

Classification of EEG Recordings by Using Fast Independent Component Analysis and Artificial Neural Network

Since there is no definite decisive factor evaluated by the experts, visual analysis of EEG signals in time domain may be inadequate. Routine clinical diagnosis requests to analysis of EEG signals. Therefore, a number of automation and computer techniques have been used for this aim. In this study we aim at designing a MLPNN classifier based on the Fast ICA that accurately identifies whether the associated subject is normal or epileptic. By analyzing a data set consisting of 100 normal and 100 epileptic EEG time series, we have found that the MLPNN classifier based on the Fast ICA achieved and sensitivity rate of 98%, and specificity rate of 90.5%. The results demonstrate that the testing performance of the neural network diagnostic system is found to be satisfactory and we think that this system can be used in clinical studies. Since the time series analysis of EEG signals is unsatisfactory and requires specialist clinicians to evaluate, this application brings objectivity to the evaluation of EEG signals.     

密相气力输送参数的人工神经网络预测

在水平管道中，用压缩空气和氢气对煤粉和小米进行密相气力输送实验，利用改进的BP神经网络对不同气量下的固体质量流率进行预测。结果表明，BP网络能对不同实验条件下的固体质量流率进行较好的预测。并画出不同气量下，固体质量流量的等值图。根据此图，可对密相气力输送参数进行初步优化，控制固体的输送量，减少密相气输送中的盲目操作。     

椎管内麻醉在妇科腹腔镜手术的应用

目的　比较腰麻与硬膜外麻醉在妇科腹腔镜手术的应用。方法　 10 0例妇科腹腔镜手术病人 ,随机分为两组 ,每组 50例。A组 (腰麻 ) :选L2～ 3注 0 .5%重比重布比卡因 ;B组 (硬膜外 ) :选T12 L1注 2 %利多卡因。结果　两组起效时间、阻滞完善时间对比有显著性差异 (P <0 .0 1) ,麻醉效果达优良级无显著性差异 (P>0 .0 5) ,SPO2 均在 96%～ 99% ,PETCO2 3 .2～ 6.0kPa ,气腹前后有差异 ,气腹后PETCO2 出现下降 ,且呼吸频率明显增快 3～ 6次 /min ,术后腰麻引起头痛 3例。结论　椎管内麻醉应用于妇科腹腔镜手术能达满意麻醉效果且安全 ,腰麻适用于年轻、体质较好、手术时间短的病人 ,而硬膜外麻醉平面和作用时间易控制 ,应用更加广泛     

利用蛋白质组学与人工神经网络构建大肠癌肝转移诊断模型

目的应用双向电泳联合质谱技术筛选大肠癌肝转移血清特异性标志物,并利用这些标志物以数据挖掘技术构建人工神经网络大肠癌肝转移诊断模型。方法收集大肠癌无肝转移、伴肝转移患者各12例血清样本,同组血清等量混合进行双向电泳,用ImageMaster V5.0软件分析两组蛋白质的差异,差异蛋白质进行MALDI-TOF-MS鉴定。ELISA法测定差异蛋白及CEA含量。用受试者工作曲线评价其对大肠癌肝转移的诊断价值,以人工神经网络方法,筛选出准确度最高者作为大肠癌肝转移诊断模型。结果双向电泳显示,大肠癌伴肝转移组与无肝转移组相比较有4个蛋白有统计学意义,其中2个上调的蛋白质分别是Transferrin和Complement component C9;2个下调的蛋白质分别是Haptoglobin和Isoform 1 of Serum albumin。受试者工作曲线分析与大肠癌肝转移相关性依次为Transferrin>Haptoglobin>CEA。人工神经网络的诊断方法以Transferrin和Haptoglobin这两个蛋白联合建立的模型预测准确度最高,为88.57%,其敏感度为63.64%,特异度为100%。结论利用蛋白质组学与人工神经网络构建了Transferrin与Haptoglobin联合的大肠癌肝转移诊断模型,横断面验证有较高的准确度,开辟了诊断大肠癌肝转移的新方法。     

脉象特征人工神经网络分类器

以人工神经网络为手段，以提取脉象信息为目的，由临床采样数据形成了网络训练输入特征向量库，不以单一脉本身为分类对象，而考虑它是否是某些可识别特征的组合，建立了浮沉、弦滑、迟数等一组脉象特征网络。证实了人工神经网络用于具有模糊性的脉象特征的识别和分类是可行的，带智能处理的特色。其分辨准确率可达９０％。     

诊断推理中人工神经网络与基于案例推理的结合

对基于人工神经网的诊断方法与基于案例推理的方法(Case-Based Reasoning, CBR)的结合进行了研究,提出了两种结合方案.针对CBR系统建立案例库索引这一难点,方案一利用人工神经网诊断分类器的诊断结果对案例库进行索引;方案二用人工神经网为待诊断对象建立模型,对正常的状态作出预测,通过预测值与实际测量值的差异对案例库进行索引.在作出最后诊断之前两种方案都利用CBR的推理结果对神经网的诊断结果进行检验和修正,从而给出更为精确、便于解释的诊断结果.经过实验对比验证,人工神经网与CBR方法的结合有效的弥补了它们在诊断推理应用中通常存在的局限.从诊断准确率、诊断速度以及诊断系统的自学习性等方面,都取得了优于传统人工神经网方法和CBR方法的性能,较好的完成了诊断推理工作.     

Control of Sevoflurane Anesthetic Agent Via Neural Network Using Electroencephalogram Signals During Anesthesia

In this study, power spectrum of the EEG data and the heartbeat data obtained from 250 patients has been applied to the designed Neural network system. A backpropagation artificial neural network has been developed which contains 53 nodes in the input layer, 27 nodes in the hidden and 1 node in the output layer. In the artificial neural network inputs, the power spectral density values corresponding 1-50 Hz frequency interval of the EEG slices which has 10 seconds of time interval, the ratio of the total of the PSD values of current EEG slice to the total PSD values of EEG slice of pre-anesthesia, the ratio of the total PSD values of the EEG data to the total PSD values of the previous EEG data, and the previous anaesthetic gas ratio values have been applied and the network has been educated. The designed neural network system has been tested by using 10 data set obtained from 4 different patients. In the anesthetic gas prediction according to the anesthesia level, successful results have been obtained with the designed system. The system has been able to correctly purposeful responses in average accuracy of 94% of the cases. This method is also computationally fast and acceptable real-time clinical performance has been obtained.     

Tuberculosis Disease Diagnosis Using Artificial Neural Network Trained with Genetic Algorithm

Tuberculosis is a common and often deadly infectious disease caused by mycobacterium; in humans it is mainly Mycobacterium tuberculosis (Wikipedia 2009). It is a great problem for most developing countries because of the low diagnosis and treatment opportunities. Tuberculosis has the highest mortality level among the diseases caused by a single type of microorganism. Thus, tuberculosis is a great health concern all over the world, and in Turkey as well. This article presents a study on tuberculosis diagnosis, carried out with the help of multilayer neural networks (MLNNs). For this purpose, an MLNN with two hidden layers and a genetic algorithm for training algorithm has been used. The tuberculosis dataset was taken from a state hospital’s database, based on patient’s epicrisis reports.     

基于人工神经网络的自适应疾病诊断模型研究

本文利用人工神经网络和遗传算法的融合技术构建了一个智能疾病诊断模型,该模型能针对不同疾病或诊断因子的变化,自适应地调整模型网络结构,表现出良好的泛化性和诊断精度.     

Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features

Electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The long-term EEG recordings of an epileptic patient obtained from the ambulatory recording systems contain a large volume of EEG data. Detection of the epileptic activity requires a time consuming analysis of the entire length of the EEG data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy have emerged in recent years. This paper discusses an automated diagnostic method for epileptic detection using a special type of recurrent neural network known as Elman network. The experiments are carried out by using time-domain as well as frequency-domain features of the EEG signal. Experimental results show that Elman network yields epileptic detection accuracy rates as high as 99.6% with a single input feature which is better than the results obtained by using other types of neural networks with two and more input features.     

E-Nose System for Anesthetic Dose Level Detection using Artificial Neural Network

In this study, an E-Nose system was realized for the anesthetic dose level prediction. For this purpose, sevoflurane anesthetic agent was measured using the E-Nose system implemented with sensor array of quartz crystal microbalances (QCM). In surgeries, anesthetic agents are given to the patients with carrier gases of oxygen (O₂) and nitrous oxide (N₂O). Frequency changes on QCM sensors to the eight sevoflurane anesthetic dose levels were recorded via RS-232 serial port. A multilayer feed forward artificial neural network (MLNN) structure was used to provide the relationship between the frequency change and the anesthetic dose level. The MLNNs were trained with the measured data using Levenberg–Marquardt algorithm. Then, the trained MLNNs were tested with random data. The results have showed that, acceptable anesthetic dose level predictions have been obtained successfully.