Methodology for Epilepsy and Epileptic Seizure Recognition using Chaos Analysis of Brain Signals

2013 ◽  
pp. 20-36 ◽  
Author(s):  
Seyyed Abed Hosseini ◽  
Mohammed-Reza Akbarzadeh-T ◽  
Mohammed-Bagher Naghibi-Sistani
Keyword(s):  
Epileptic Seizure ◽  
Fuzzy Inference ◽  
Largest Lyapunov Exponent ◽  
Inference System ◽  
Qualitative And Quantitative ◽  
Neuro Fuzzy ◽  
Ictal Eeg ◽  
Quantitative Results ◽  
Anfis Classifier ◽  
Two Phases

A novel combination of chaotic features and Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed for epileptic seizure recognition. The non-linear dynamics of the original EEGs are quantified in the form of the Hurst exponent (H), Correlation dimension (D2), Petrosian Fractal Dimension (PFD), and the Largest lyapunov exponent (?). The process of EEG analysis consists of two phases, namely the qualitative and quantitative analysis. The classification ability of the H, D2, PFD, and ? measures is tested using ANFIS classifier. This method is evaluated with using a benchmark EEG dataset, and qualitative and quantitative results are presented. The inter-ictal EEG-based diagnostic approach achieves 98.6% accuracy with using 4-fold cross validation. Diagnosis based on ictal data is also tested in ANFIS classifier, reaching 98.1% accuracy. Therefore, the method can be successfully applied to both inter-ictal and ictal data.

Classification of EEG Signals for Motor Imagery Based on Mutual Information and Adaptive Neuro Fuzzy Inference System

Fuzzy Systems ◽  
2017 ◽  
pp. 347-366
Author(s):  
Shereen A. El-aal ◽  
Rabie A. Ramadan ◽  
Neveen Ghali
Keyword(s):  
Mutual Information ◽  
Motor Imagery ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Imagery Task ◽  
Eeg Signals ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Anfis Classifier ◽  
Maximal Relevance

Electroencephalogram (EEG) signals based Brain Computer Interface (BCI) is employed to help disabled people to interact better with the environment. EEG signals are recorded through BCI system to translate it to control commands. There are a large body of literature targeting EEG feature extraction and classification for Motor Imagery tasks. Motor imagery task have several features can be extracted to use in classification. However, using more features consume running time and using irrelevant and redundant features affect the performance of the used classifier. This paper is dedicated to extracting the best feature vector for motor imagery task. This work suggests two feature selection methods based on Mutual Information (MI) including Minimum Redundancy Maximal Relevance (MRMR) and maximal Relevance (MaxRel). Adaptive Neuro Fuzzy Inference System (ANFIS) classifier with Subtractive clustering method is utilized for EEG signals classifications. The suggested methods are applied to BCI Competition III dataset IVa and IVb and BCI Competition II dataset III.

scholarly journals Mammogram Image Retrieval using Ipso Optimized Anfis Classifier

2019 ◽  
Vol 8 (9S2) ◽  
pp. 799-804
Keyword(s):  
Image Retrieval ◽  
Fuzzy Inference ◽  
Noise Removal ◽  
Research Area ◽  
Query Image ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Input Dataset ◽  
Mammogram Image ◽  
Anfis Classifier

Content-based image retrieval (CBIR) is an research area over the past years that has attracted research. In various medical applications like mammogram analysis CBIR techniques helps the medical team to get similar set of images from a large medical records to help in diagnosis of a disease. This paper proposes an efficient Content-Based Mammogram Image Retrieval method by using an Optimized Classifier. Initially, the input dataset is preprocessed, in which noise removal and contrast enhancement are done. Next, pectoral muscles of the mammogram images are removed using Single Sided Edge Marking (SSEM). Now, feature extraction is done, in which GLCM features, Gabor features and the Local Pattern with Binary features are being removed. The features that are being removed are classified into three classes namely benign, malignant and normal. An optimized classifier named as Adaptive Neuro Fuzzy Inference System (ANFIS), which is optimized by using the Improved Particle Swarm Optimization (IPSO) technique, is used for classification purpose. Finally, similarity is assessed between the trained feature distance vectors and the feature distance vectors of the input query image. Similarity assessment is done using Euclidean Distance metric and the image that has the lowest distance compared with the query is retrieved. The experimental results are obtained for the proposed system and they are compared with the existing techniques.

scholarly journals PREDIKSI PENGGUNA BUS TRANS SARBAGITA DENGAN METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM

2013 ◽  
Vol 2 (3) ◽  
pp. 46
Author(s):  
SLAMET SAMSUL HIDAYAT ◽  
I PUTU EKA NILA KENCANA ◽  
KETUT JAYANEGARA
Keyword(s):  
Least Squares ◽  
Public Transportation ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Hybrid Learning ◽  
Inference System ◽  
Transportation Services ◽  
Neuro Fuzzy ◽  
Two Phases

Trans Sarbagita is a public transportation services people at Denpasar, Badung, Gianyar and Tabanan. Trans Sarbagita is aimed to resolve a problems caused by accretion volume of vehicles in Bali. This study conducted to forecast the number of Trans Sarbagita passengers in 2013 using ANFIS. The ANFIS system composed by five layers where each layers has a different function and its divide in two phases, i.e. forward and backward phases. The ANFIS uses a hybrid learning algorithm which is a combination of Least Squares Estimator (LSE) on forwards phases and Error Backpropagation (EBP) on the backward phases. The results show, ANFIS with six inputs with M.F of  Pi  produces smallest error, compared to seven and eight input and M.F gauss and generalizedbell. Forecast of Trans Sarbagita passenger numbers in 2013 have to fluctuated every day and the average of passenger’s Trans Sarbagita for a day is 1627 passengers with MSE equal to 10210 and MAPE is 4.01%.

A Novel Approach of Restoration of Digital Images Degraded by Impulse Noise

2014 ◽  
Vol 4 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Rashmi Kumari ◽  
Anupriya Asthana ◽  
Vikas Kumar
Keyword(s):  
Impulse Noise ◽  
Fuzzy Inference ◽  
Digital Images ◽  
Window Size ◽  
Qualitative And Quantitative Analysis ◽  
Inference System ◽  
Qualitative And Quantitative ◽  
Novel Approach ◽  
Neuro Fuzzy ◽  
Restoration Method

Restoration of digital images degraded by impulse noise is still a challenge for researchers. Various methods proposed in the literature suffer from common drawbacks: such as introduction of artifacts and blurring of the images. A novel idea is proposed in this paper where presence of impulsive pixels are detected by ANFIS (Adaptive Neuro-Fuzzy Inference System) and mean of the median of suitable window size of noisy image is taken for the removal of the detected corrupted pixels. Experimental results show the effectiveness of the proposed restoration method both by qualitative and quantitative analysis.

scholarly journals High Impedance Fault Detection in MV Distribution Network using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System

2018 ◽  
Author(s):  
Veerapandiyan Veerasamy ◽  
Noor Izzri Abdul Wahab ◽  
Rajeswari Ramachandran ◽  
Muhammad Mansoor ◽  
Mariammal Thirumeni
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Discrete Wavelet ◽  
Inference System ◽  
High Impedance ◽  
Neuro Fuzzy ◽  
High Impedance Fault ◽  
Anfis Classifier

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using Matlab software and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault accurately from other power system faults in the system.

scholarly journals Hybrid Linear Moments and ANFIS-GA to Predict Groundwater Salinity

2016 ◽  
Vol 11 (3) ◽  
pp. 767-777
Author(s):  
Amir Jalalkamali
Keyword(s):  
Correlation Coefficient ◽  
Fuzzy Inference ◽  
Groundwater Resources ◽  
Regional Analysis ◽  
Quantitative Information ◽  
Inference System ◽  
Site Analysis ◽  
Qualitative And Quantitative ◽  
Neuro Fuzzy ◽  
Optimum Model

There is, unfortunately, a lack of exhaustive qualitative and quantitative information about Iran groundwater resources. That is why various models are used in estimation of qualitative and quantitative groundwater parameters. The present paper presents a comparison of the hybrid of Adaptive Neuro Fuzzy Inference System (ANFIS) with Genetic Algorithm (GA) model and L-moments regarding their power and efficiency in regional and at-site anticipation of salinity of groundwater at Kerman plain. In doing so, electrical conductivity is considered the dependent variable, while, through regression analysis, total cat ions, magnesium ion, sodium percentage, and level of groundwater are assumed to be independent parameters. The correlation coefficient between input values and anticipated ones is the criterion the study takes into account in comparisons as well as in the election of the optimum model. Wells of study area were classified into three homogenous regions. Hass-King Heterogeneity and Incongruity Criterion were calculated for each site. The best result for regional analysis is achieved in well No.17 with correlation coefficient (C.C) 0.9958 whereas the best result for at-site analysis is calculated in well No.2 with C.C 0.9787. Results showed that, in regions with lower heterogeneity criterion, ANFIS-GA regional anticipations were slightly more accurate than at-site anticipations.

scholarly journals Mammography Images Segmentation Based on Fuzzy Set and Thresholding

2019 ◽  
Vol 29 (3) ◽  
pp. 168
Author(s):  
Ali Mohammed Salih ◽  
Mohammed Y. Kamil
Keyword(s):  
Fuzzy Set ◽  
Fuzzy Inference ◽  
Early Recognition ◽  
Data Set ◽  
Inference System ◽  
Breast Masses ◽  
Qualitative And Quantitative ◽  
Breast Malignancy ◽  
The World ◽  
Quantitative Results

Breast cancer is the most widespread cancer that influences ladies about the world. Early recognition of breast tumor is a standout amongst the hugest variables influencing the probability of recuperation from the illness. Hence, mammography remains the most precise and best device for distinguishing breast malignancy. This paper presents a method for segment the boundary of breast masses regions in mammograms via a proposed algorithm based on fuzzy set techniques. Firstly, it was used data set (mini-MIAS) for evaluate algorithm. it was preprocessing the data set to remove noise and propose a fuzzy set by using fuzzy inference system by generated two input parameters (employs image gradient), then used thresholding filter. Then it was evaluated this proposed method, qualitative and quantitative results were obtained to demonstrate the efficiency of this method and confirm the possibility of its use in improving the diagnosis.

scholarly journals High Impedance Fault Detection in Medium Voltage Distribution Network Using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3330 ◽  
Author(s):  
Veerapandiyan Veerasamy ◽  
Noor Abdul Wahab ◽  
Rajeswari Ramachandran ◽  
Muhammad Mansoor ◽  
Mariammal Thirumeni ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Discrete Wavelet ◽  
Inference System ◽  
Medium Voltage ◽  
High Impedance ◽  
Neuro Fuzzy ◽  
High Impedance Fault ◽  
Anfis Classifier

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage (MV) distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using MATLAB software R2014b and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three-phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault from other faults in the power system.

scholarly journals A METHOD OF BEARING FAULT DIAGNOSIS USING SINGULAR SPECTRUM ANALYSIS, SPARSE FILTERING AND ANFIS

2018 ◽  
Vol 33 (3) ◽  
pp. 213-228
Author(s):  
Dung Sy Nguyen ◽  
Van Hiep Nguyen
Keyword(s):  
Spectrum Analysis ◽  
Fuzzy Inference ◽  
Singular Spectrum Analysis ◽  
Inference System ◽  
Singular Spectrum ◽  
Sparse Filtering ◽  
Neuro Fuzzy ◽  
Two Phases ◽  
The One ◽  
Bearing Damage

Bearing is an important machine detail participating in almost mechanical systems. Estimating online its operating condition to exploit actively the systems, therefore, is one of the most urgent requirements. This paper presents an online bearing damage identifying method named ASBDIM based on ANFIS (Adaptive Neuro-Fuzzy Inference System), Singular Spectrum Analysis (SSA) and sparse filtering. This is an online estimating process operated via two phases, offline and online one. In the offline period, by using SSA and sparse filtering, a database signed Off_DaB is built whose inputs are features extracted from the measured data stream typed big data, while its outputs are values encoding the surveyed bearing damage statuses. The ANFIS is then employed to identify the dynamic response of the mechanical system corresponding to the bearing damage statuses reflected by the Off_DaB. In the online period, first, at each estimating time, another database called On_DaB is established using the way similar to the one used for building the Off_DaB. The On_DaB participates as inputs of the ANFIS to generate its outputs which are then compared with the corresponding encoded outputs to specify bearing real status at this time. Survey results based on different data sources showed the effectiveness of the proposed method.

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