Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients

Electrophysiological recordings are considered a reliable method of assessing a person's alertness. Sleep medicine is asked to offer objective methods to measure daytime alertness, tiredness and sleepiness. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in recognition of alertness level. This paper deals with a novel method of analysis of EEG signals using wavelet transform, and classification using ANN. EEG signals were decomposed into the frequency sub-bands using wavelet transform and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients. Then these statistical features were used as an input to an ANN with three discrete outputs: alert, drowsy and sleep. The error back-propagation neural network is selected as a classifier to discriminate the alertness level of a subject. EEG signals were obtained from 30 healthy subjects. The group consisted of 14 females and 16 males with ages ranging from 18 to 65 years and a mean age of 33.5 years, and a Body Mass Index (BMI) of 32.4±7.3 kg/m². Alertness level and classification properties of ANN were tested using the data recorded in 12 healthy subjects, whereby the EEG recordings were not used to train the ANN. The statistics were used as a measure of potential applicability of the ANN. The accuracy of the ANN was 95±3% alert, 93±4% drowsy and 92±5% sleep.     

Automatic recognition of vigilance state by using a wavelet-based artificial neural network

In this study, 5-s long sequences of full-spectrum electroencephalogram (EEG) recordings were used for classifying alert versus drowsy states in an arbitrary subject. EEG signals were obtained from 30 healthy subjects and the results were classified using a wavelet-based neural network. The wavelet-based neural network model, employing the multilayer perceptron (MLP), was used for the classification of EEG signals. A multilayer perceptron neural network (MLPNN) trained with the Levenberg–Marquardt algorithm was used to discriminate the alertness level of the subject. In order to determine the MLPNN inputs, spectral analysis of EEG signals was performed using the discrete wavelet transform (DWT) technique. The MLPNN was trained, cross-validated, and tested with training, cross-validation, and testing sets, respectively. The correct classification rate was 93.3% alert, 96.6% drowsy, and 90% sleep. The classification results showed that the MLPNN trained with the Levenberg–Marquardt algorithm was effective for discriminating the vigilance state of the subject.     

Computation of resonant frequency for equilateral triangular microstrip antennas using the adaptive neuro-fuzzy inference system

A new method based on the adaptive neuro-fuzzy inference system (ANFIS) for calculating the resonant frequency of the equilateral triangular microstrip patch antenna is presented. The ANFIS has the advantages of the expert knowledge of the fuzzy inference system and the learning capability of neural networks. A hybrid-learning algorithm, which combines the least-square method and the backpropagation algorithm, is used to identify the parameters of ANFIS. The results of the new method show better agreement with the experimental results, as compared to the results of previous methods available in the literature. © 2004 Wiley Periodicals, Inc. Int J RF and Microwave CAE 14, 134–143, 2004.     

Navigation of multiple mobile robots in a highly clutter terrains using adaptive neuro-fuzzy inference system

In recent years, the interest in research on robots has increased extensively; mainly due to avoid human to involve in hazardous task, automation of Industries, Defence, Medical and other household applications. Different kinds of robots and different techniques are used for different applications. In the current research proposes the Adaptive Neuro Fuzzy Inference System (ANFIS) Controller for navigation of single as well as multiple mobile robots in highly cluttered environment. In this research it has tried to design a control system which will be able decide its own path in all environmental conditions to reach the target efficiently. Some other requirement for the mobile robot is to perform behaviours like obstacle avoidance, target seeking, speed controlling, knowing the map of the unknown environments, sensing different objects and sensor-based navigation in robot’s environment.     

An adaptive neuro-fuzzy inference system for bridge risk assessment

Bridge risks are often evaluated periodically so that the bridges with high risks can be maintained timely. This paper develops an adaptive neuro-fuzzy system (ANFIS) using 506 bridge maintenance projects for bridge risk assessment, which can help Highways Agency to determine the maintenance priority ranking of bridge structures more systematically, more efficiently and more economically in comparison with the existing bridge risk assessment methodologies which require a large number of subjective judgments from bridge experts to build the complicated nonlinear relationships between bridge risk score and risk ratings. The ANFIS proves to be very effective in modelling bridge risks and performs better than artificial neural networks (ANN) and multiple regression analysis (MRA).     

EEG signal classification using wavelet feature extraction and a mixture of expert model

Mixture of experts (ME) is modular neural network architecture for supervised learning. A double-loop Expectation-Maximization (EM) algorithm has been introduced to the ME network structure for detection of epileptic seizure. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. EEG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT). Then these sub-band frequencies were used as an input to a ME network with two discrete outputs: normal and epileptic. In order to improve accuracy, the outputs of expert networks were combined according to a set of local weights called the “gating function”. The invariant transformations of the ME probability density functions include the permutations of the expert labels and the translations of the parameters in the gating functions. The performance of the proposed model was evaluated in terms of classification accuracies and the results confirmed that the proposed ME network structure has some potential in detecting epileptic seizures. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network model.     

Predicting survival of individual patients with esophageal cancer by adaptive neuro-fuzzy inference system approach

Since esophageal cancer has no symptoms in the early stage, it is usually not detected until advanced stages in which treatment is challenging. Integrated treatment provided by a multidisciplinary team is crucial for maximizing the prognosis and survival of patients with esophageal cancer. Currently, clinicians must rely on the cancer staging system for diagnosis and treatment. An accurate and easily applied system for predicting the prognosis of esophageal cancer would be useful for comparing different treatment strategies and for calculating cancer survival probability. This study presents a hazard modeling and survival prediction system based on adaptive neuro-fuzzy inference system (ANFIS) to assist clinicians in prognostic assessment of patients with esophageal cancer and in predicting the survival of individual patients. Expert knowledge was used to construct the fuzzy rule based prognosis inference system for esophageal cancer. Fuzzy logic was used to process the values of input variables rather than categorizing values as normal or abnormal based on cutoffs. After transformation and expansion, censored survival data could be used by the ANFIS for training to establish the risk model for accurately predicting individual survival for different time intervals or for different treatment modalities. Actual values for serum C-reactive protein, albumin, and time intervals were input into the model for use in predicting the survival of individual patients for different time intervals. The curves obtained by the ANFIS approach were fitted to those obtained using the actual values. The comparison results show that the ANFIS is a practical, effective, and accurate method of predicting the survival of esophageal cancer patients.     

Evolutionary neuro-fuzzy system for surface roughness evaluation

The paper presents a system that, according to the requirements referring to the product quality given in surface roughness, with minimum machining time and maximum metal removal rate, recommends optimal cutting parameters with the possibility of surface roughness control during the machining process. The suggested evolutionary neuro-fuzzy system for evaluation of surface roughness is composed of three units: surface roughness prediction by cutting parameters, multi-objective optimization of cutting parameters aimed at minimum machining time and maximum metal removal rate and control of obtained or required surface roughness by means of the features quantified from digital image of the observed machined surface. The paper outlines the idea and architecture of the system as well as the possibilities of implementation. The obtained results, illustrated by experimental research, justify the application and further development of the suggested evolutionary neuro-fuzzy system for evaluation of surface roughness within the given constraints.     

An expert system of price forecasting for used cars using adaptive neuro-fuzzy inference

An expert system for used cars price forecasting using adaptive neuro-fuzzy inference system (ANFIS) is presented in this paper. The proposed system consists of three parts: data acquisition system, price forecasting algorithm and performance analysis. The effective factors in the present system for price forecasting are simply assumed as the mark of the car, manufacturing year and engine style. Further, the equipment of the car is considered to raise the performance of price forecasting. In price forecasting, to verify the effect of the proposed ANFIS, a conventional artificial neural network (ANN) with back-propagation (BP) network is compared with proposed ANFIS for price forecast because of its adaptive learning capability. The ANFIS includes both fuzzy logic qualitative approximation and the adaptive neural network capability. The experimental result pointed out that the proposed expert system using ANFIS has more possibilities in used car price forecasting.     

基于减法聚类和自适应神经模糊推理系统的递阶模糊系统的设计

提出了一种设计递阶模糊系统的简易而有效的方法.在得到一个单级模糊系统的基础上,用灵敏度分析法对每一个输入变量的重要性进行排序,从而确定每一级子系统的输入变量.利用减法聚类和自适应神经 模糊推理系统逐级对子系统进行训练.所得到的递阶模糊系统可进一步得到简化.仿真实例证实了设计方法的有效性.     

Application of an adaptive neuro-fuzzy inference system for classification of Behcet disease using the fast Fourier transform method

Abstract: In this study, ophthalmic arterial Doppler signals were obtained from 200 subjects, 100 of whom suffered from ocular Behcet disease while the rest were healthy subjects. An adaptive neuro-fuzzy inference system (ANFIS) was used to detect the presence of ocular Behcet disease. Spectral analysis of the ophthalmic arterial Doppler signals was performed by the fast Fourier transform method for determining the ANFIS inputs. The ANFIS was trained with a training set and tested with a testing set. All these data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ocular Behcet disease. Performance indicators and statistical measures were used for evaluating the ANFIS. The correct classification rate was 94% for healthy subjects and 90% for unhealthy subjects suffering from ocular Behcet disease. The classification results showed that the ANFIS was effective at detecting ophthalmic arterial Doppler signals from subjects with Behcet disease.     

An adaptive network-based fuzzy inference system (ANFIS) for the forecasting: The case of close price indices

The close price prediction model of the Zagreb Stock Exchange Crobex® index is presented in this paper. For the input/output data plan modeling the Crobex® index close price historical data are retrieved from the Zagreb Stock Exchange official internet pages. The prediction model is created in the way that for each of 5 days in advance it predicts the Crobex® close price. The prediction model is generated based on the input/output data plan by means of the adaptive neuro-fuzzy inference system method, representing the fuzzy inference system. It is of the essence to point out that for each day a separate fuzzy inference system is created by means of the adaptive neuro-fuzzy inference system method based on the same set of input/output data, the only difference being that for every separate fuzzy inference system different subsets for training and checking are used so that input variables are differently created. The input/output data set represents the historical data of the Crobex® index close price from 4 November 2010 to 24 January 2012 and the Crobex® index close price is predicted for the subsequent 5 days, the first day of prediction being 25 January 2012. After that the above mentioned input/output data set is shifted 5 days in advance and the Crobex® index close price is predicted in advance for the next 5 days starting with the last day of the input/output data set. In that way the Crobex® index close prices are predicted until 19 October 2012 based on the Crobex® index close price historical data. At the end of the paper qualitative and quantitative estimates are presented for the given approach of predicting the Crobex® index close price showing that the approach is useful for predicting within its limits.     

Application of an adaptive neural fuzzy inference system to thermal comfort and group technology problems

The Adaptive Neural Fuzzy Inference System (ANFIS) is used to design two vague systems, namely thermal comfort and group technologies in production and operations management. Results show that both systems can be modeled successfully by the combined use of a fuzzy approach and neural network learning.     

Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area

This paper presents landslide-susceptibility mapping using an adaptive neuro-fuzzy inference system (ANFIS) using a geographic information system (GIS) environment. In the first stage, landslide locations from the study area were identified by interpreting aerial photographs and supported by an extensive field survey. In the second stage, landslide-related conditioning factors such as altitude, slope angle, plan curvature, distance to drainage, distance to road, soil texture and stream power index (SPI) were extracted from the topographic and soil maps. Then, landslide-susceptible areas were analyzed by the ANFIS approach and mapped using landslide-conditioning factors. In particular, various membership functions (MFs) were applied for the landslide-susceptibility mapping and their results were compared with the field-verified landslide locations. Additionally, the receiver operating characteristics (ROC) curve for all landslide susceptibility maps were drawn and the areas under curve values were calculated. The ROC curve technique is based on the plotting of model sensitivity — true positive fraction values calculated for different threshold values, versus model specificity — true negative fraction values, on a graph. Landslide test locations that were not used during the ANFIS modeling purpose were used to validate the landslide susceptibility maps. The validation results revealed that the susceptibility maps constructed by the ANFIS predictive models using triangular, trapezoidal, generalized bell and polynomial MFs produced reasonable results (84.39%), which can be used for preliminary land-use planning. Finally, the authors concluded that ANFIS is a very useful and an effective tool in regional landslide susceptibility assessment.     

An adaptive neuro-fuzzy inference system for predicting the risks of low back disorders due to manual material lifting jobs

Low back disorders (LBDs) due to manual material lifting tasks have become a significant issue which affects the quality of life of industrial population of workers in the U.S. and has enormous economic impact. For the last three decades researchers have been trying to understand the phenomena of LBDs and develop practical guidelines which could prevent these injuries from happening or limit the severity of these injuries after they have already occurred. One of the research streams concentrated on creating and testing various classification models based on a landmark Marras data set. The goal of these models was to categorize manual lifting jobs as low risk or high risk with respect to LBDs. This paper summarizes and critiques the previous approaches as some of them yielded unrealistically high classification accuracy rates. The paper also proposes an adaptive neuro-fuzzy inference system (ANFIS) to classify tasks into high risk or low risk. To our best knowledge ANFIS has not been used in this context yet and has not been used for classification of a binary target variable. The paper also compares the classification performances of the different parameters or configurations of ANFIS. The ANFIS model appears to be a viable option for risk classification as it exhibits the classification accuracy rates consistent with several previous studies. More importantly ANFIS generates easy to interpret control surfaces, membership functions, and fuzzy rules, thus allowing one to get a deeper insight into the relationships between risk factors which interact with each other in a complex and nonlinear way. Such insights could prove to be very useful for the much needed efforts to better understand LBDs.     

Survey of four models of probability density functions of wind speed and directions by adaptive neuro-fuzzy methodology

The probabilistic distribution of wind speed is one of the discriminating wind qualities for the assessment of wind energy potential and for the execution of wind energy conversion frameworks. The wind energy spread might be obtained when wind speed probability function is known. Thusly, the probability movement of wind speed is an uncommonly huge touch of information needed in the assessment of wind energy potential. The two-parameter Weibull circulation has been normally used, recognized and endorsed in expositive interpretation to express the wind speed repeat transport for most wind regions. The Gumbel and Frechet dissemination is frequently used to model large wind speeds. The joint probability density functions (JPDF) model is advanced by minimal disseminations of wind speed and wind direction that is expected as an Extreme-Value mathematical statement. In the present study an exertion has been made to figure out the best fitting circulation of wind speed information by a soft computing methodology. We used adaptive neuro-fuzzy inference framework (ANFIS) in this paper, which is a specific kind of the neural frameworks family, to foresee the wind speed probability density dispersion. For this reason, two parameters Weibull and JPDF and three parameter Frechet and Gumbel conveyances are fitted to data and parameters for each distribution and utilized as preparing and checking information for ANFIS model. At long last, ANFIS effects are contrasted and the four introduced appropriations recommending that ANFIS conveyances are discovered to be most suitable as contrasted with the Weibull, JPDF, Frechet and Gumbel circulations.     

Self-spawning neuro-fuzzy system for rule extraction

In this paper we propose self-spawning neuro-fuzzy system (SSNFS), a new neuro-fuzzy system to derive fuzzy rules from data. The SSNFS model is based on a generic definition of incremental perceptron and a new learning algorithm that is capable of both structural (rule) learning and parametric learning. It constructs the fuzzy system by detecting a suitable number of rule patches and their positions and shapes in the input space. Initially the rule base consists of one single fuzzy rule; during the iterative learning process the rule base expands according to a supervised spawning validity measure. The rule induction process terminates when a given stop criterion is satisfied. SSNFS is very general since it does not require the prior knowledge about the input space and can be used in any application based on the scatter-partitioning fuzzy system. To demonstrate the effectiveness and applicability of our algorithm, we present a synthetic example and real-world modelling problems.

Assessing safety at work using an adaptive neuro-fuzzy inference system (ANFIS) approach aided by partial least squares structural equation modeling (PLS-SEM)

The main objective of this research was to apply an adaptive neuro-fuzzy inference system (ANFIS) approach aided by Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess safety at work, defined as employee propensity to follow safety regulations, including safe work practices at the workplace. A survey with seven main components: 1) use of mobile technology, 2) tacit safety knowledge, 3) explicit safety knowledge, 4) attitudes toward safety: psychological aspects, 5) attitudes toward safety: emotional aspects, 6) safety culture: behavioral aspects, and 7) safety culture: psychological aspects, was used for this purpose. Workers from three manufacturing companies located in southeastern Poland completed a paper-based survey. PLS-SEM, combined with an adaptive neuro-fuzzy inference system (ANFIS) method, was used to develop the study model and determine its main components. The results showed that tacit safety knowledge, attitudes toward safety: psychological aspects, attitudes toward safety: emotional aspects, safety culture: behavioral aspects, safety culture: psychological aspects, and the use of mobile technology were significant factors influencing the perceived safety at work. Moreover, the results of the ANFIS modeling approach showed that behavioral aspects of safety culture were the most critical predictor of the perceived safety at work.     

Automatic diagnosis of diabetes using adaptive neuro‐fuzzy inference systems

Abstract: A new approach based on an adaptive neuro‐fuzzy inference system (ANFIS) is presented for diagnosis of diabetes diseases. The Pima Indians diabetes data set contains records of patients with known diagnosis. The ANFIS classifiers learn how to differentiate a new case in the domain by being given a training set of such records. The ANFIS classifier is used to detect diabetes diseases when eight features defining diabetes indications are used as inputs. The proposed ANFIS model combines neural network adaptive capabilities and the fuzzy logic qualitative approach. The conclusions concerning the impacts of features on the diagnosis of diabetes disease are obtained through analysis of the ANFIS. The performance of the ANFIS model is evaluated in terms of training performances and classification accuracies and the results confirm that the proposed ANFIS model has potential in detecting diabetes diseases.     

Simulation study for automatic generation control of a multi-area power system by ANFIS approach

This paper deals with the application of artificial neural network (ANN) based ANFIS approach to automatic generation control (AGC) of a three unequal area hydrothermal system. The proposed ANFIS controller combines the advantages of fuzzy controller as well as quick response and adaptability nature of ANN. Appropriate generation rate constraints (GRC) have been considered for the thermal and hydro plants. The hydro area is considered with an electric governor and thermal area is considered with reheat turbine. The design objective is to improve the frequency and tie-line power deviations of the interconnected system. 1% step load perturbation has been considered occurring either in any individual area or occurring simultaneously in all the areas. It is a maiden application of ANFIS approach to a three unequal area hydrothermal system with GRC considering perturbation in a single area as well as in all areas. The performance of the ANFIS controller is compared with the results of integral squared error (ISE) criterion based integral controller published previously. Simulation results are presented to show the improved performance of ANFIS controller in comparison with the conventional integral controller. The results indicate that the controllers exhibit better performance. In fact, ANFIS approach satisfies the load frequency control requirements with a reasonable dynamic response.