Adaptive Neuro-fuzzy Inference System Classifier Research Articles

Detection and Segmentation of Meningioma Brain Tumors in MRI brain Images using Curvelet Transform and ANFIS

The detection of abnormal tumor region brain Magnetic Resonance Images (MRI) is complex task due to its similar structures between tumor and its surrounding regions. In this paper, Adaptive Neuro Fuzzy Inference System (ANFIS) classification method-based meningioma brain tumor detection is proposed. The proposed method consists of the following stages as preprocessing, transformation, feature extraction and classifications. The brain MR images are enhanced in preprocessing stage and this spatial domain image is converted into multi resolution image using Curvelet transform. The texture and statistical features are extracted from the transformed coefficients. These features are trained and classified by ANFIS classifier and further morphological operations are applied on the classified brain image to segment the tumor regions. This proposed meningioma tumor detection approach is analyzed in terms of sensitivity, specificity, Jaccard Similarity Index (JSI), Dice Similarity Index DSI) and accuracy. The reported results showed that an accuracy of 98.5%, sensitivity 91.5% and specificity 98.6 %was achieved from the finely Curvelet Transform and ANFIS Model.

Fusing clinical and image data for detecting the severity level of hospitalized symptomatic COVID-19 patients using hierarchical model

Based on medical reports, it is hard to find levels of different hospitalized symptomatic COVID-19 patients according to their features in a short time. Besides, there are common and special features for COVID-19 patients at different levels based on physicians’ knowledge that make diagnosis difficult. For this purpose, a hierarchical model is proposed in this paper based on experts’ knowledge, fuzzy C-mean (FCM) clustering, and adaptive neuro-fuzzy inference system (ANFIS) classifier. Experts considered a special set of features for different groups of COVID-19 patients to find their treatment plans. Accordingly, the structure of the proposed hierarchical model is designed based on experts’ knowledge. In the proposed model, we applied clustering methods to patients’ data to determine some clusters. Then, we learn classifiers for each cluster in a hierarchical model. Regarding different common and special features of patients, FCM is considered for the clustering method. Besides, ANFIS had better performances than other classification methods. Therefore, FCM and ANFIS were considered to design the proposed hierarchical model. FCM finds the membership degree of each patient’s data based on common and special features of different clusters to reinforce the ANFIS classifier. Next, ANFIS identifies the need of hospitalized symptomatic COVID-19 patients to ICU and to find whether or not they are in the end-stage (mortality target class). Two real datasets about COVID-19 patients are analyzed in this paper using the proposed model. One of these datasets had only clinical features and another dataset had both clinical and image features. Therefore, some appropriate features are extracted using some image processing and deep learning methods. According to the results and statistical test, the proposed model has the best performance among other utilized classifiers. Its accuracies based on clinical features of the first and second datasets are 92% and 90% to find the ICU target class. Extracted features of image data increase the accuracy by 94%. The accuracy of this model is even better for detecting the mortality target class among different classifiers in this paper and the literature review. Besides, this model is compatible with utilized datasets about COVID-19 patients based on clinical data and both clinical and image data, as well. • A new hierarchical model is proposed using ANFIS classifiers and FCM clustering method in this paper. Its structure is designed based on experts’ knowledge and real medical process. FCM reinforces the ANFIS classification learning phase based on the features of COVID-19 patients. • Two real datasets about COVID-19 patients are studied in this paper. One of these datasets has both clinical and image data. Therefore, appropriate features are extracted based on its image data and considered with available meaningful clinical data. Different levels of hospitalized symptomatic COVID-19 patients are considered in this paper including the need of patients to ICU and whether or not they are in end-stage. • Well-known classification methods including case-based reasoning (CBR), decision tree, convolutional neural networks (CNN), K-nearest neighbors (KNN), learning vector quantization (LVQ), multi-layer perceptron (MLP), Naive Bayes (NB), radial basis function network (RBF), support vector machine (SVM), recurrent neural networks (RNN), fuzzy type-I inference system (FIS), and adaptive neuro-fuzzy inference system (ANFIS) are designed for these datasets and their results are analyzed for different random groups of the train and test data; • According to unbalanced utilized datasets, different performances of classifiers including accuracy, sensitivity, specificity, precision, F-score, and G-mean are compared to find the best classifier. ANFIS classifiers have the best results for both datasets. • To reduce the computational time, the effects of the Principal Component Analysis (PCA) feature reduction method are studied on the performances of the proposed model and classifiers. According to the results and statistical test, the proposed hierarchical model has the best performances among other utilized classifiers.

Adaptive neuro-fuzzy inference system classifier with interpretability for cancer diagnostic

Clinical outcome analysis using patient medical data facilitates clinical decision-making and increases prognostic accuracy. Recently, deep learning (DL) with learning big data features has shown expert-level accuracy in predicting clinical outcomes. Many of these sophisticated machine learning models, however, lack interpretability, creating significant trust-related healthcare issues. This necessarily requires the need for interpretable AI systems capable of explaining their decisions. In this respect, the paper proposes an interpretable classifier of the adaptive neuro-fuzzy inference method (iANFIS), which combines the fuzzy inference system with critical rule selection by attention mechanism. The rule-based processing of ANFIS helps the user to understand the behavior of the proposed model. The essential activated rule and the most important input features that predict the outcome are identified by the attention-based rule selector. We conduct two experiments with two cancer diagnostic datasets for verifying the performance of the proposed iANFIS. By using recursive rule elimination (RRE) to prune fuzzy rules, the model’s complexity is significantly reduced while preserving system efficiency that makes it more interpretable.

A proposed approach for diabetes diagnosis using neuro-fuzzy technique

Diabetes is a chronic disease characterized by a decrease in pancreatic insulin production. The immune system will be harmed due to this condition, which will raise blood sugar levels. However, early detection of diabetes enables patients to begin treatment on time, therefore reducing or eliminating the risk of severe consequences. One of the most significant challenges in the healthcare unit is disease diagnosis. Traditional techniques of disease diagnosis are manual and prone to inaccuracy. This paper proposed an approach for diagnosing diabetes using the adaptive neuro-fuzzy inference system (ANFIS) based on Pima Indians diabetes dataset (PIDD). The three stages of the proposed approach are pre-processing classification and evaluation. Normalization, imputation, and anomaly detection are part of the pre-processing stage. The pre-processing was done by normalizing the data, replacing the missing values, and using the local outlier factor (LOF) technique. In the classification stage, ANFIS classifiers were trained using the hybrid learning algorithm of the neural network. Finally, the evaluation procedures use the last stage’s sensitivity, specificity, and accuracy metrics. The obtained classification accuracy was 92.77%, and it seemed rather promising compared to the other classification applications for this topic found in the literature.

Fault diagnosis in industrial rotating equipment based on permutation entropy, signal processing and multi-output neuro-fuzzy classifier

Rotating equipment is considered as a key component in several industrial sectors. In fact, the continuous operation of many industrial machines such as sub-sea pumps and gas turbines relies on the correct performance of their rotating equipment. In order to reduce the probability of malfunctions in this equipment, condition monitoring, and fault diagnosis systems are essential. In this work, a novel approach is proposed to perform fault diagnosis in rotating equipment based on permutation entropy, signal processing, and artificial intelligence. To that aim, vibration signals are employed for an indication of bearing performance. In order to facilitate fault diagnosis, fault detection and isolation are performed in two separate steps. As first, once a vibration signal is received, the faulty state of the bearing is determined by permutation entropy. In case a faulty state is detected, the fault type is determined using an approach based on signal processing and artificial intelligence. Wavelet packet transform and envelope analysis of the vibration signals are utilized to extract the frequency components of the fault. The proposed approach allows for the automatic selection of a frequency band that includes the characteristic resonance frequency of the fault, which is subject to change in different operational conditions. The method works by extracting the proper features of the signals that are used to decide about the faulty bearing’s condition by a multi-output adaptive neuro-fuzzy inference system classifier. The effectiveness of the approach is assessed by the Case Western Reserve University dataset: the analysis demonstrates the proposed method’s capabilities in accurately diagnosing faults in rotating equipment as compared to existing approaches.

An Optimized and Hybrid Energy Aware Routing Model for Effective Detection of Flooding Attacks in a Manet Environment

Ad Hoc networks for communication have completely replaced the existing communication technologies which are dependant on infrastructure. An attractive and widely utilized field in communication systems is Mobile Ad Hoc Networks or the MANETs which are a derivative of the conventional Ad Hoc Networks. Security of information communicated through MANETs as well as the robust nature of the network is a prime issue of concern and research in recent times. MANET has given rise to several applications such as data networks, sensor networks, military, commercial, virtual conference and supporting private sectors, and doctors in hospitals, etc. Amongst various attacks prevalent on MANET environment, packet flooding is a common attack and causes a devastating effect on MANET nodes which if left undetected may lead to consequent crashing of the entire network. Floodding attacks also tend to consume enormous energy well above the prescribed energy consumption limits per node resulting in lifetime reduction. Hence, detection of these malicious nodes and their differentiation from trustworthy nodes is taken as the research objective in this paper. This paper presents feature extraction anda classification model based on ANFIS (Adaptive Neuro-Fuzzy Inference System). By using ANFIS classifier, the extracted featureis trained and then classified.Further to counter the flooding cum energy preserving routing, this paper proposes a SMA integration with AODVprotocol called SMA2AODV to detect flooding attacks for MANETs. After detecting, the hybrid model ACO combined with FDR PSO for optimizing energy. ACO-FDR PSO identifies the energy-efficient routeand minimizes energy consumption in the network, toincrease node lifetime that ensures energy-efficient routing. The performance metrics like throughput, packet delivery ratio, attack detection ratio, and energy consumption areanalyzedby using the NS-2 simulator with existing benchmark methods.

RETRACTED ARTICLE: Detection and classification of mechanical faults of three phase induction motor via pixels analysis of thermal image and adaptive neuro-fuzzy inference system

The fault diagnosis in motor is substantial as it results in breakdown of production line and the faults may damage motor and results in economic losses. Bearing failure, rotor eccentricity, shaft misalignment and load related faults are the most frequent failures under mechanical fault category. This paper addresses three such faults that may increase the stator temperature namely air gap eccentricity, shaft misalignment and cooling system failure. The thermography technique has been used widely for fault detection in induction motor. In three phase induction motor the thermal images are analyzed for healthy condition and the above mentioned faults conditions. This paper presents thermal pixels counting algorithm to calculate the diagnosis indicators and adaptive neuro-fuzzy inference system classifier is used to classify the faults based on the diagnosis indicator data base. Laboratory based experimental investigation are carried out to verify the accuracy of the proposed method. This method provides accurate diagnosis indicator that will be used as a bench mark value for preparing the maintenance schedule under non-destructive mode.

RETRACTED ARTICLE: Machine learning method based detection and diagnosis for epilepsy in EEG signal

The epileptic seizure can be detected using electroencephalogram (EEG) signals. The detection of epileptogenic region in brain is important for the detection of epilepsy disease. The signals from epileptogenic region in brain are focal signal and the signal from normal regions in brain is non-focal signal. Hence, the detection of focal signal is important for epilepsy disease detection. This paper proposes an automatic detection and diagnosis of EEG signals for epilepsy disease using soft computing approaches as adaptive neuro fuzzy inference system (ANFIS) and neural networks (NN). In this paper, the features from decomposed coefficients as bias (B), weight feature (W), entropy(E), activity feature (AF), mobility feature (MF), complexity feature (CF), skewness (S) and kurtosis (K) are extracted for the classification of EEG signals into either focal or non-focal signals for epilepsy disease detection and diagnosis. The detection of focal signal is achieved by ANFIS classifier and the diagnosis of the severity levels in focal signal is achieved by NN classification approach. The proposed method is used in many clinical diagnosis.

Automated Detection of Multiple Sclerosis Lesions Using Texture-based Features and a Hybrid Classifier

Background: Multiple Sclerosis (MS) is the most frequent non-traumatic neurological disease capable of causing disability in young adults. Detection of MS lesions with magnetic resonance imaging (MRI) is the most common technique. However, manual interpretation of vast amounts of data is often tedious and error-prone. Furthermore, changes in lesions are often subtle and extremely unrepresentative. Objectives: To develop an automated non-subjective method for the detection and quantification of MS lesions. Materials & Methods: This paper focuses on the automatic detection and classification of MS lesions in brain MRI images. Two datasets, one simulated and the other one recorded in hospital, are utilized in this work. A novel hybrid algorithm combining image processing and machine learning techniques is implemented. To this end, first, intricate morphological patterns are extracted from MRI images via texture analysis. Then, statistical textures-based features are extracted. Afterward, two supervised machine learning algorithms, i.e., the Hidden Markov Model (HMM) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are employed within a hybrid platform. The hybrid system makes decisions based on ensemble learning. The stacking technique is used to apply predictions from both models o train a perceptron as a decisive model. Results: Experimental results on both datasets indicate that the proposed hybrid method outperforms HMM and ANFIS classifiers with reducing false positives. Furthermore, the performance of the proposed method compared with the state-of-the-art methods, was approved. Conclusion: Remarkable results of the proposed method motivate advanced detection systems employing other MRI sequences and their combination.

Automatic and heuristic complete design for ANFIS classifier

ABSTRACTThere is a variety of fuzzy classifiers, one of which is Adaptive Neuro-Fuzzy Inference system (ANFIS) classifier. One of the main challenges in designing such data classifiers is selection of effective and appropriate type and location of membership functions and its training method to reduce the classification error. In this paper, a new technique (based on intelligent methods) is presented and implemented to select and locate the membership functions and simultaneous training using a new method based on Inclined Planes System Optimization (IPO) to minimize errors of an ANFIS classifier for the first time. The presented method is evaluated for classification of data sets with different reference classes and different length feature vectors, which have acceptable complexity. According to the results of the research, the presented method has a higher level of accuracy and efficiency in selecting the type and location of membership functions (based on intelligent methods) and simultaneous training with IPO, compared to other methods, such as particle swarm optimization, genetic algorithm, differential evolution, and ACOR algorithms.

Combined Fuzzy-logic and Neural Network Classifiers for Uterine Fibroid in vulnerable women

The diagnosis of uterine myoma often referred to as fibroid is poor mostly as women with the ailment present fewer symptoms at the early stage and get less clinical attention. Most times, the tumour is undiagnosed till it gets to an advanced stage, and may also be as a result of the relatively expensive diagnostic equipment used. Over the years the increase in uteri myoma has called for a model that could rapidly recognise the neoplasm at an early stage. In this study, we design a relatively specific model for the detection of uterine myoma. Matrix Laboratory (MATLAB version 7.5.0, R2007b) was used to implement the combined fuzzy logic classifier called adaptive neuro-fuzzy inference system (ANFIS) model. The dataset that was used in training the ANFIS model consist of 90 cases, which involve 78% (70 cases) of the entire dataset that was used in the training process of the system. The remaining 22% (20 cases) of the data were also used in the testing process of the system, which the system will get adapted to for subsequent recognition of similar cases. The ANFIS program was designed using a bell membership function that utilises a hybrid optimisation method with an error tolerance of 0.05. The training dataset was passed through the ANFIS for 30 epochs. At the end of the simulation, the system had a training error of 0.016169 with the training dataset and an average testing error of 0.010315. The system was able to classify approximately 99% of the test data set accurately. Therefore, we have successfully classified the symptoms of uterine fibroid using combined fuzzy-logic and neural network classifiers for vulnerable women. Keywords : Artificial intelligence, ANFIS classifier, Fuzzy-logic, Fibroid & Uterine myoma.

Anfis Classifier Based Moving Object Detection and Segmentation in Indoor and Outdoor Environments

Background: Moving object detection in dynamic environment video is more complex than the static environment videos. In this paper, moving objects in video sequences are detected and segmented using feature extraction based Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier approach. The proposed moving object detection methodology is tested on different video sequences in both indoor and outdoor environments. Methods: This proposed methodology consists of background subtraction and classification modules. The absolute difference image is constructed in background subtraction module. The features are extracted from this difference image and these extracted features are trained and classified using ANFIS classification module. Results: The proposed moving object detection methodology is analyzed in terms of Accuracy, Recall, Average Accuracy, Precision and F-measure. The proposed moving object segmentation methodology is executed on different Central Processing Unit (CPU) processor as 1.8 GHz and 2.4 GHz for evaluating the performance during moving object segmentation. At present, some moving object detection systems used 1.8 GHz CPU processor. Recently, many systems for moving object detection are using 2.4 GHz CPU processor. Hence, CPU processors 1.8 GHz and 2.4 GHz are used in this paper for detecting the moving objects in video sequences. Table 1 shows the performance evaluation of proposed moving object detection on CPU processor 1.8 GHz (100 sequence). Table 2 shows the performance evaluation of proposed moving object detection on CPU processor 2.8 GHz (100 sequence). The average moving object detection time on CPU processor 1.8 GHz for fountain sequence is 62.5 seconds, for airport sequence is 64.7 seconds, for meeting room sequence is 71.6 seconds and for Lobby sequence is 73.5 seconds, respectively, as depicted in Table 3. The average elapsed time for moving object detection on 100 sequences is 68.07 seconds. The average moving object detection time on CPU processor 2.4 GHz for fountain sequence is 56.5 seconds, for airport sequence is 54.7 seconds, for meeting room sequence is 65.8 seconds and for Lobby sequence is 67.5 seconds, respectively, as depicted in Table 4. The average elapsed time for moving object detection on 100 sequences is 61.12 seconds. It is very clear from Table 3 and Table 4; the moving object detection time is reduced when the frequency of the CPU processor increases. Conclusion: In this paper, moving object is detected and segmented using ANFIS classifier. The proposed method initially segments the background image and then features are extracted from the threshold image. These features are trained and classified using ANFIS classification method. The proposed moving object detection method is tested on different video sequences which are obtained from different indoor and outdoor environments. The performance of the proposed moving object detection and segmentation methodology is analyzed in terms of Accuracy, Recall, Average Accuracy, Precision and F-measure.

Image Registration based Cervical Cancer Detection and Segmentation Using ANFIS Classifier

Cervical cancer is the leading cancer in women around the world. In this paper, Adaptive Neuro Fuzzy Inference System (ANFIS) classifier based cervical cancer detection and segmentation methodology is proposed. This proposed system consists of the following stages as Image Registration, Feature extraction, Classifications and Segmentation. Fast Fourier Transform (FFT) is used for image registration. Then, Grey Level Co-occurrence Matrix (GLCM), Grey level and trinary features are extracted from the registered cervical image. Next, these extracted features are trained and classified using ANFIS classifier. Morphological operations are now applied over the classified cervical image to detect and segment the cancer region in cervical images. Simulations on large cervical image dataset demonstrate that the proposed cervical cancer detection and segmentation methodology outperforms the state of-the-art methods in terms of sensitivity, specificity and accuracy.

RETRACTED ARTICLE: Recognition of autism in children via electroencephalogram behaviour using particle swarm optimization based ANFIS classifier

Autism spectrum disorders (ASD) are pervasive neuro developmental conditions portrayed by disabilities in social intercommunication, besides stereotyped conduct. Since Electroencephalogram (EEG) recording together with analysis stands one among the basic devices in diagnosing along with recognizing the issue in neurophysiology, utilized the signals of EEG aimed at diagnosing persons with ASD. These signals have heaps of data which mirror the conduct of brain functions which thusly catches the marker for autism, help to early analyze and speed the treatment. To beat such disadvantage, this given work proposes an Adaptive Neuro-Fuzzy Inference System classifier joined with Particle Swarm Optimization that is named as PSO-ANFIS for classifying the diagnosing signals of EEG. To start with, utilizing Savitzky Golay (S-G) filter pre-processed the input signal, after that by variational mode decomposition (VMD) disintegrated the signal. Presently, features are extracted; additionally, these are trained and also characterized utilizing PSO-ANFIS, which classifies whether the signal seems normal or else autism signal. The proposed strategy classified the abnormal besides normal signal, all the more precisely, when contrasted with the current ones are established through the experiment.

Adaptive clustering based breast cancer detection with ANFIS classifier using mammographic images

Breast cancer is the most invasive cancer in women and second leading cause of mortality in women. Advances in detection and treatment have improved survival rates dramatically in patients. Now-a-days mammographic image based analysis is widely used to predict the breast cancer. The diagnosis is simple and survivals of the patient are high if the breast cancer is predicted at the early stage accurately. This paper heavily focuses on methodologies which provides accurate detection of cancerous tissues and thereby reduces the death rates. The algorithms are non-subsampled Shearlet transform (NSST) for image preprocessing, Adaptive clustering for image segmentation and finally adaptive neuro-fuzzy inference system (ANFIS) for image classification. NSST is an extension of wavelet transform in multidimensional and multidirectional case wherein the components are processed with the help of thresholding scheme. Adaptive clustering algorithm separates the pixels in the image into clusters based on both their intensity and sensitivity and thereby makes the segmentation much simpler and easier. ANFIS algorithm is the classifier which is a combination of both neural network adaptive capabilities and the fuzzy logic qualitative approach which is used to classify the normal and abnormal image accurately. The robustness of the proposed methodology is examined using classification accuracy, sensitivity and specificity.

Performance analysis of malicious node detection in MANET using ANFIS classification approach

Security threaten is the primary issue in mobile ad hoc networks (MANET). The efficiency of the MANET system is affected by presence of malicious nodes. It is very difficult task to identify the malicious nodes from the trusty nodes in MANET system due to similar characteristics between malicious and trusty node. This paper proposes an efficient feature extraction based malicious node detection system using adaptive neuro fuzzy inference system (ANFIS) classification approach. In this paper, trust function features and service trust features are extracted from trusty and malicious nodes. These extracted features are trained and classified using ANFIS classifier. The performance of the proposed malicious node detection in MANET system is analyzed in terms of throughput, average packet loss ratio, energy consumption and detection ratio.

Classification of masses in digital mammograms using Biogeography-based optimization technique

Breast cancer is a leading cause of death in women in both developed and developing countries. Design and development of computer-based systems can assist radiologists in the effective treatment of breast cancer. For the design of an efficient classification system, efficient feature selection techniques must be used to reduce complexity of feature space in digital mammogram classification. The proposed methodology aims to explore use of Biogeography-based optimization to select a subset of features. Adaptive neuro-fuzzy inference system and artificial neural network are employed to evaluate fitness of the selected features. The features selected are used to train and test adaptive neuro-fuzzy inference system and artificial neural network classifiers. The experiment employed over 651 mammograms. The classification results shows that Biogeography-based optimization with adaptive neuro-fuzzy inference system is superior to Biogeography-based optimization with artificial neural network. Adaptive neuro-fuzzy inference system classifier achieve an accuracy of 98.92% with sensitivity of 99.10%, specificity of 98.72% and area under curve AZ = 0.999 ± 0.000. Outcomes achieved with the proposed Biogeography-based optimization with adaptive neuro-fuzzy inference system are far better as compared to some recent work.

Performance analysis of computer aided brain tumor detection system using ANFIS classifier

AbstractThe abrupt changes in brain cells due to the environmental effects or genetic disorders leads to form the abnormal lesions in brain. These abnormal lesions are combined as mass and known as tumor. The detection of these tumor cells in brain image is a complex task due to the similarities between normal cells and tumor cells. In this paper, an automated brain tumor detection and segmentation methodology is proposed. The proposed method consists of feature extraction, classification and segmentation. In this paper, Grey Level Co‐Occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT) and Law's texture features are used as features. These features are fed to Adaptive Neuro Fuzzy Inference System (ANFIS) classifier as input pattern, which classifies the brain image. Morphological operations are now applied on the classified abnormal brain image to segment the tumor regions. The proposed system achieves 95.07% of sensitivity, 99.84% of specificity and 99.80% of accuracy for tumor segmentation.

Detection and diagnosis of meningioma brain tumor using ANFIS classifier

AbstractTumors are formed in brain due to the uncontrolled development of cells. These tumors can be cured if it is timely detected and by proper medication. This article proposes a computer‐aided automatic detection and diagnosis of meningioma brain tumors in brain images using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier. The proposed system consists of feature extraction, classification, and segmentation and diagnosis sections. In this article, Grey level Co‐occurrence Matric (GLCM) and Grid features are extracted from the brain image and these features are classified using ANFIS classifier into normal or abnormal. Then, morphological operations are used to segment the abnormal regions in brain image. Based on the location of these abnormal regions in brain tissues, the segmented tumor regions are diagnosed.

A Diagnostic System for Detection of Atrial and Ventricular Arrhythmia Episodes from Electrocardiogram

Cardiac arrhythmias, with each passing day, are becoming highly prevalent in modern society. The ever increasing number of patients added to the sheer amount of data that is recorded from a patient during monitoring limits the ability of a medical practitioner to diagnose these problems quickly. Therefore, an intelligent system is required which can produce a reliable diagnosis in a short period of time. In this paper, a new intelligent diagnostic system for detection of atrial arrhythmia (atrial flutter and atrial fibrillation) and ventricular arrhythmia (premature ventricular contractions, ventricular bigeminy, ventricular escape rhythm, ventricular tachycardia and ventricular fibrillation) episodes from Electrocardiogram (ECG) is proposed. This system is based on the non-linear analysis of the variational modes of ECG. Variational mode decomposition is used for decomposition of ECG signal into several modes. Then, the non-linear features namely, the distribution entropy and the sample entropy are evaluated from the modes of ECG. The performance of variational mode distribution entropy (VMDE) and variational mode sample entropy (VMSE) features is assessed using support vector machine (SVM) and adaptive neuro-fuzzy inference system classifiers. Experimental results reveal that, the combination of VMDE and VMSE features, and the radial basis function kernel based multi-class SVM classifier are suitable for detection of arrhythmia episodes from ECG with an average accuracy value of 95.60%.