小波分解结合自适应神经模糊推理系统的呼吸预测研究

目的:研究一种方法精确预测胸腹部肿瘤放射治疗中的非规则呼吸运动。方法:提出基于小波分解和自适应神经模糊推理系统的呼吸运动预测方法（WANFIS）,利用小波分解将呼吸信号分成基线、低频和高频三部分,并分别采用线性拟合、自适应神经模糊推理系统（ANFIS）、简单移动平均进行预测,然后综合三部分预测值作为呼吸运动预测结果。基于30例临床数据回顾性分析,将WANFIS算法与神经网络（NN）、CyberKnife放射外科系统的Synchrony呼吸同步追踪系统、ANFIS这三种典型预测算法进行对照比较。结果:本文提出的WANFIS算法的归一化均方根误差（nRMSE）平均值为0.09,小于NN的0.17、Synchrony的0.11 以及ANFIS的0.11。结论:WANFIS能更好地预测非规则呼吸信号,更有效地补偿放疗系统时间延迟。     

基于特征提取的压力蒸汽灭菌设备运行故障诊断方法研究

目的 针对现有压力蒸汽灭菌设备运行故障诊断中存在诊断准确度较低、耗时较长的问题,提出设计一种基于特征提取的压力蒸汽灭菌设备运行故障诊断方法。方法 通过Lab VIEW编程软件结合数据采集卡,采集压力蒸汽灭菌设备运行故障数据。通过AR模型和敏感IMF分解方法提取压力蒸汽灭菌设备运行故障信号特征,并通过近邻图构建映射,利用拉普拉斯算子获取映射矩阵,将数据直接映射至低维子空间中,完成设备运行故障特征的约简。通过神经模糊推理自适应系统设计智能混合运行故障诊断模型,将约简后的特征数据输入模型中,实现压力蒸汽灭菌设备运行故障诊断。结果利用提出的设计方法对某医院压力蒸汽灭菌设备进行故障诊断,所设计方法对该设备故障的平均诊断准确率达到了89.2%,不同故障类型的平均诊断时间在640 ms内。结论 本研究设计方法可有效提升压力蒸汽灭菌设备运行故障诊断效果,具有一定可行性。     

心钠素与肺动脉高压

<正>心钠素(atrial natriuretic factor,ANF)是由人和哺乳类动物心房肌细胞分泌的、新发现的一类循环激素,具有强大的利钠、利尿、扩张血管和抑制肾素—血管紧张素—醛固酮系统RAAS的作用(Renin—Angiotensin—Aidosterone System).肺动脉高压PHT(Pulmonary Hypertension),对血浆 ANF浓度PANFC(Plasma Concentration of Atrial NatriureticFactor)明显升高及用外源性ANF治疗PHT取得了初步成功,由此推测ANF参与了PHT的病理生理过程.本文重点叙述ANF在PHT时的变化和意义及用外源性ANF治疗PHT.     

婴儿痉挛症的病因诊断及治疗进展

婴儿痉挛症 (IS)又名West综合征 ,是一种年龄依赖性难治性癫痫。诊断标准以痉挛发作、智能障碍、脑电图高峰节律紊乱为三大特点[1] 。近年来 ,随着影像学技术及分子生物学、神经免疫学等的发展 ,在IS的病因诊断及治疗上取得了一定的进展 ,现结合国内外文献综述如下。1　神经影像学检查是查找IS病因的重要手段IS病因复杂多样 ,目前大致分为三类 ,即症状性、隐源性和原发性。以往主要根据临床症状、体征及一些辅助检查进行综合分析。随着影像诊断技术的改进与发展 ,使原来病因不明的IS病人找到了病因 ,目前国内外相继应用颅脑CT对IS进行…     

肝炎,肝硬化患者血浆心钠素检测的意义

心钠素(ANF)是心房肌细胞产生和分泌的一种循环激素,具有强大的利尿、利钠、舒张血管和降低血压的作用。肝功能受损可导致血中ANF水平的改变。为探讨ANF检测的意义,笔者用放射免疫法对病毒性肝炎、肝炎后肝硬化患者共98例进行了血浆ANF浓度的检测。材料和方法一、检测对象: 1.正常对照组:42例。男26例,女16例。年龄     

大鼠脑内存有心房利钠因子神经元的证据

哺乳动物心房内含有对肾脏机能和血管阻力产生强大作用的肽类,这些具有较强利钠和利尿作用的心房肽称为心房利钠因子(ANF)。曾有人报道了ANF的纯化和氨基酸顺序分析的结果。几种ANF肽的氨基酸顺序相似性提示,它们都是从共同的前体物     

心钠素概述

心钠素(Cardionation)亦称心房利钠因子(Atrial natriuretic factor,以下简称ANF),由21～35个氨基酸组成的一类生物活性多肽,它是心脏产生和分泌的一种激素。因此,目前认为心脏不仅是一个循环器官、并且具有内分泌功能,ANF具有强大     

精氨酸加压素和心钠素与高血压病左室肥厚的关系

目的　探讨精氨酸加压素 (AVP)和心钠素 (ANF)在高血压病左室肥厚 (LVH)发生中的作用。方法　观察 36例高血压病合并LVH、36例不合并LVH患者及 2 4例正常人血压、血浆AVP、ANF和ANF/AVP比值变化。结果　LVH组和非LVH组血浆AVP和ANF浓度均高于正常对照组 ;LVH组收缩压、AVP和ANF高于非LVH组 ,ANF/AVP比值低于非LVH组。非LVH组血浆AVP与收缩压、舒张压和血浆ANF呈正相关。结论　循环AVP和ANF水平改变及其平衡失调可能参与高血压病LVH的形成过程     

神经内分泌免疫网络及其在神经精神病学研究上的应用

对环境刺激作出适应性反应是生物普遍具有和生存的必要条件。神经系统(NS)调节重要的生命活动,免疫系统(IS)通过特定的方式感受抗原性异物的刺激并产生相适应的应答,以维持生理平衡和稳定。本文比较NS与IS的组成、主要反应特点、简述神经内分泌免疫网络(NEIN)的研究进展,希望有助于扩大对机体调节行为的认识和对神经精神病学的研究。     

不同起搏方式对血浆心钠素的影响

目的 :本研究采用高位右心房 ( HRA)、房室交界区 ( AVJ)、右室流出道( RVOT)和右室心尖部 ( RVA) 4种不同的右心起搏方式 ,观察房室激动收缩顺序和心室激动顺序对血浆 ANF的影响。方法 :对 1 0例病态窦房结综合征 ( SSS)病人 ,于永久性人工心脏起搏器植入术中以同一频率分别按 HRA、AVJ、RVOT和 RVA的顺序各起搏 5min,每次起搏间隔至少 5min。分别取基础状态及 4种不同方式起搏末右房血 2 ml,应用放射免疫方法测定血浆 ANF水平。结果 :HRA起搏并不引起血浆 ANF的显著变化 ;而 AVJ、RVOT和 RVA起搏后血浆 ANF水平比基础状态均显著升高 ,以 RVA最为明显 ;RVOT和 RVA起搏后血浆 ANF水平显著高于 AVJ起搏 ;RVOT及 RVA起搏伴 V-A传导者血浆 ANF水平显著高于无 V-A传导者。结论 :病态窦房结综合征中 ,房室不同步、心室不同步及 V-A传导是引起血浆 ANF升高的因素 ,AVJ和 RVOT由于保持了近似正常的心室激动顺序 ,作为除 RVA以外的右室起搏部位     

Adaptive Neuro-Fuzzy Inference Systems for Automatic Detection of Breast Cancer

This paper intends to an integrated view of implementing adaptive neuro-fuzzy inference system (ANFIS) for breast cancer detection. The Wisconsin breast cancer database contained records of patients with known diagnosis. The ANFIS classifiers learned how to differentiate a new case in the domain by given a training set of such records. The ANFIS classifier was used to detect the breast cancer when nine features defining breast cancer indications were used as inputs. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of breast cancer were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performances and classification accuracies and the results confirmed that the proposed ANFIS model has potential in detecting the breast cancer.     

Diagnosis of Renal Failure Disease Using Adaptive Neuro-Fuzzy Inference System

Adaptive Neuro-Fuzzy Inference System (ANFIS) is one of the useful and powerful neural network approaches for the solution of function approximation and pattern recognition problems in the last decades. In this paper, the diagnosis of renal failure disease is investigated using ANFIS approach. Totally the raw data of 112 patients is obtained from Istanbul and Cerrahpasa Medical Faculties of Istanbul University, Turkey. Sixty-four of them are related to renal failures and the rest data belong to healthy persons. In ANFIS model, three rules and Gaussian membership functions are chosen, where rules are determined by the subtractive clustering method. Seven parameters of the patients are considered for the input of the system. These are: Blood Urea Nitrogen (BUN), Creatinine, Uric Acid, Potassium (K), Calcium (Ca), Phosphorus (P) and age. We try to decide whether the patient is ill or not. We have reached 100% success in ANFIS and have better results compared to Support Vector Machine (SVM) and Artificial Neural Networks (ANN).     

Comparison of Different Classifier Algorithms on the Automated Detection of Obstructive Sleep Apnea Syndrome

In this paper, we have compared the classifier algorithms including C4.5 decision tree, le artificial neural network (ANN), artificial immune recognition system (AIRS), and adaptive neuro-fuzzy inference system (ANFIS) in the diagnosis of obstructive sleep apnea syndrome (OSAS), which is an important disease that affects both the right and the left cardiac ventricle. The goal of this study was to find the best classifier model on the diagnosis of OSAS. The clinical features were obtained from Polysomnography device as a diagnostic tool for obstructive sleep apnea in patients clinically suspected of suffering this disease in this study. The clinical features are arousals index, apnea–hypopnea index (AHI), SaO₂ minimum value in stage of rapid eye movement, and percent sleep time in stage of SaO₂ intervals bigger than 89%. In our experiments, a total of 83 patients (58 with a positive OSAS (AHI > 5) and 25 with a negative OSAS such that normal subjects) were examined. The decision support systems can help to physicians in the diagnosing of any disorder or disease using clues obtained from signal or images taken from subject having any disorder. In order to compare the used classifier algorithms, the mean square error, classification accuracy, area under the receiver operating characteristics curve (AUC), and sensitivity and specificity analysis have been used. The obtained AUC values of C4.5 decision tree, ANN, AIRS, and ANFIS classifiers are 0.971, 0.96, 0.96, and 0.922, respectively. These results have shown that the best classifier system is C4.5 decision tree classifier on the diagnosis of obstructive sleep apnea syndrome.     

Artificial Intelligence Models for Predicting Iron Deficiency Anemia and Iron Serum Level Based on Accessible Laboratory Data

Iron deficiency anemia (IDA) is the most common nutritional deficiency worldwide. Measuring serum iron is time consuming, expensive and not available in most hospitals. In this study, based on four accessible laboratory data (MCV, MCH, MCHC, Hb/RBC), we developed an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) to diagnose the IDA and to predict serum iron level. Our results represent that the neural network analysis is superior to ANFIS and logistic regression models in diagnosing IDA. Moreover, the results show that the ANN is likely to provide an accurate test for predicting serum iron levels with high accuracy and acceptable precision.     

Neural Network Approaches to Grade Adult Depression

Depression is a common but worrying psychological disorder that adversely affects one's quality of life. It is more ominous to note that its incidence is increasing. On the other hand, screening and grading of depression is still a manual and time consuming process that might be biased. In addition, grades of depression are often determined in continuous ranges, e.g., 'mild to moderate' and 'moderate to severe' instead of making them more discrete as 'mild', 'moderate', and 'severe'. Grading as a continuous range is confusing to the doctors and thus affecting the management plan at large. Given this practical issue, the present paper attempts to differentiate depression grades more accurately using two neural net learning approaches-'supervised', i.e., classification with Back propagation neural network (BPNN) and Adaptive Network-based Fuzzy Inference System (ANFIS) classifiers, and 'unsupervised', i.e., 'clustering' technique with Self-organizing map (SOM), built in MATLAB 7. The reason for using the supervised and unsupervised learning approaches is that, supervised learning depends exclusively on domain knowledge. Supervision may induce biasness and subjectivities related to the decision-making. Finally, the performance of BPNN and ANFIS are compared and discussed. It was observed that ANFIS, being a hybrid system, performed much better compared to the BPNN classifier. On the other hand, SOM-based clustering technique is also found useful in constructing three distinct clusters. It also assists visualizing the multidimensional data clusters into 2-D.     

Pairwise ANFIS Approach to Determining the Disorder Degree of Obstructive Sleep Apnea Syndrome

Obstructive sleep apnea syndrome (OSAS) is an important disease that affects both the right and the left cardiac ventricle. This paper presents a novel classification method called pairwise ANFIS based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and one against all method for detecting the obstructive sleep apnea syndrome. In order to extract the features related with OSAS, we have used the clinical features obtained from Polysomnography device as a diagnostic tool for obstructive sleep apnea (OSA) in patients clinically suspected of suffering from this disease. The clinical features obtained from Polysomnography Reports are Arousals Index (ARI), Apnea and Hypoapnea Index (AHI), SaO₂ minimum value in stage of REM, and Percent Sleep Time (PST) in stage of SaO₂ intervals bigger than 89%. Since ANFIS has output with one class, we have extended the output of ANFIS to multi class by means of one against all method to diagnose the OSAS that has four classes consisting of normal (25 subjects), mild OSAS (AHI = 5–15 and 14 subjects), middle OSAS (AHI < 15–30 and 18 subjects), and heavy OSAS (AHI > 30 and 26 subjects). The classification accuracy, sensitivity and specifity analysis, mean square error, and confusion matrix have been used to test the performance of proposed method. The obtained classification accuracies are 82.92%, 82.92%, 85.36%, and 87.80% for each class including normal, mild OSAS, middle OSAS, and heavy OSAS using ANFIS with one against all method with 50–50% train-test split, respectively. Combining ANFIS and one against all method that is firstly proposed by us was firstly applied for diagnosing the OSAS. The proposed method has produced very promising results in the detecting the obstructive sleep apnea syndrome.     

Usage of Case-Based Reasoning, Neural Network and Adaptive Neuro-Fuzzy Inference System Classification Techniques in Breast Cancer Dataset Classification Diagnosis

Breast cancer is a common to females worldwide. Today, technological advancements in cancer treatment innovations have increased the survival rates. Many theoretical and experimental studies have shown that a multiple classifier system is an effective technique for reducing prediction errors. This study compared the particle swarm optimizer (PSO) based artificial neural network (ANN), the adaptive neuro-fuzzy inference system (ANFIS), and a case-based reasoning (CBR) classifier with a logistic regression model and decision tree model. It also applied three classification techniques to the Mammographic Mass Data Set, and measured its improvements in accuracy and classification errors. The experimental results showed that, the best CBR-based classification accuracy is 83.60%, and the classification accuracies of the PSO-based ANN classifier and ANFIS are 91.10% and 92.80%, respectively.     

基于PID神经网络的非线性系统辨识与控制

针对工业控制领域中非线性系统采用传统的控制方法不能达到满意的控制效果,提出一种基于P ID神经网络的控制方案,以对其进行辨识和控制。将P ID神经网络引入控制系统中,既具有常规P ID控制结构简单、参数物理意义明确等优点,同时又具有神经网络的并行结构和学习记忆功能及非线性映射能力。仿真结果表明:该控制系统响应速度快、超调量小、稳态精度高,能够快速跟踪系统输出并进行有效控制,且具有一定的自适应性和鲁棒性,满足实时控制的要求。     

基于改进云粒子群优化的模糊神经网络在甲醇合成转化率软测量中的应用

针对基本PSO算法早熟、搜索精度不高与易陷入局部最优的缺点,结合云滴的随机性、稳定倾向性,提出了一种改进粒子群优化算法(ICPSO)。将改进算法用于模糊神经网络的参数优化,并应用于甲醇单程转化率建模中。仿真实验结果表明：该模型具有较高的精度和较好的泛化能力,能够实现甲醇转化率的实时监测。