Fuzzy Soft System and Arrhythmia Classification

Artificial Intelligence in Medicine, 2007

Objective: In the current work we propose a methodology for the automated creation of fuzzy expert systems, applied in ischaemic and arrhythmic beat classification. Methods: The proposed methodology automatically creates a fuzzy expert system from an initial training dataset. The approach consists of three stages: (a) extraction of a crisp set of rules from a decision tree induced from the training dataset, (b) transformation of the crisp set of rules into a fuzzy model and (c) optimization of the fuzzy model's parameters using global optimization. Material: The above methodology is employed in order to create fuzzy expert systems for ischaemic and arrhythmic beat classification in ECG recordings. The fuzzy expert system for ischaemic beat detection is evaluated in a cardiac beat dataset that was constructed using recordings from the European Society of Cardiology ST-T database. The arrhythmic beat classification fuzzy expert system is evaluated using the MIT-BIH arrhythmia database.

2018

The design of an intelligent system for identification of cardiac arrhythmias is considered using ECG data set. The arrhythmia identification system that includes feature selection and classification stages is proposed. Cardiac arrhythmias are characterized by many input data. In the paper, a sequential feature selection method is used to reduce the size of the input feature space. The idea behind this study is to find out a reduced feature space so that a classifier built using this tiny dataset can perform better than a classifier built using the original dataset. After feature selection, fuzzy neural networks (FNNs) is applied for the classification purpose. The structure of FNNs is proposed and training algorithm is developed. The classification performance has been evaluated using 10-fold cross-validation. The proposed algorithms have been implemented and evaluated on the UCI ECG dataset. The proposed FNN based approach has provided attractive classification accuracy.

Expert Systems with Applications, 2012

In this paper we describe a hybrid intelligent system for classification of cardiac arrhythmias. The hybrid approach was tested with the ECG records of the MIT-BIH Arrhythmia Database. The samples considered for classification contained arrhythmias of the following types: LBBB, RBBB, PVC and Fusion Paced and Normal, as well as the normal heartbeats. The signals of the arrhythmias were segmented and transformed for improving the classification results. Three methods of classification were used: Fuzzy K-Nearest Neighbors, Multi Layer Perceptron with Gradient Descent and momentum Backpropagation, and Multi Layer Perceptron with Scaled Conjugate Gradient Backpropagation. Finally, a Mamdani type fuzzy inference system was used to combine the outputs of the individual classifiers, and a very high classification rate of 98% was achieved.

Pattern Recognition, 2003

The electrocardiogram is a representative signal containing information about the condition of the heart. The shape and size of the P-QRS-T wave, the time intervals between its various peaks, etc. may contain useful information about the nature of disease a icting the heart. However, these subtle details cannot be directly monitored by the human observer. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the signal parameters, extracted and analysed using computers, are highly useful in diagnostics. This paper deals with the classiÿcation of certain diseases using artiÿcial neural network (ANN) and fuzzy equivalence relations. The heart rate variability is used as the base signal from which certain parameters are extracted and presented to the ANN for classiÿcation. The same data is also used for fuzzy equivalence classiÿer. The feedforward architecture ANN classiÿer is seen to be correct in about 85% of the test cases, and the fuzzy classiÿer yields correct classiÿcation in over 90% of the cases. ?

Jurnal Ilmu Komputer dan Informasi, 2016

Arrhythmia is a cardiovascular disease that can be diagnosed by doctors using an electrocardiogram (ECG). The information contained on the ECG is used by doctors to analyze the electrical activity of the heart and determine the type of arrhythmia suffered by the patient. In this study, ECG arrhythmia classification process was performed using Support Vector Machine based fuzzy logic. In the proposed method, fuzzy membership functions are used to cope with data that are not classifiable in the method of Support Vector Machine (SVM) one-against-one. An early stage of the data processing is the baseline wander removal process on the original ECG signal using Transformation Wavelet Discrete (TWD). Afterwards then the ECG signal is cleaned from the baseline wander segmented into units beat. The next stage is to look for six features of the beat. Every single beat is classified using SVM method based fuzzy logic. Results from this study show that ECG arrhythmia classification using propos...

2011

An automatic system integrating the artificial intelligent methods could classify the normal sinus rhythm (NSR) and three arrhythmic types from the continuous ECG signals obtained from the MIT-BIH arrhythmia database. In this system, a support vector machine (SVM) was used to mark the heart beats of ECG with Lead II and its slope signals. An algorithm according the markers extracted segment's waveforms of Lead II and V1 as the pattern's features of classification. A selfconstructing neural fuzzy inference network (SoNFIN) was used to classify NSR and three arrhythmic types including premature ventricular contraction (PVC), left bundle branch block (LBBB), and right bundle branch block (RBBB). The results indicated the accuracy achieved 98.9%. The accuracy of heart beat detection could be arisen to 99.3%.

NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society, 2007

An electrocardiogram (ECG) arrhythmia detection system has been developed. Piecewise continuous trapezoidal fuzzy membership functions and defuzzification schemes have been designed to be used in a fuzzy classifier. Fourteen types of arrhythmias and abnormalities can be detected implementing the classifier. We have evaluated the algorithm on MIT-BIH Database. The classifier achieved a sensitivity of 99.18% ± 2.75 and a positive predictivity of 98.00% ± 4.45 in detecting twelve out of fourteen arrhythmias, but a sensitivity of 53.12% ± 34.04 and a positive predictivity of 36.80% ± 40.26 are designated to the other two. Due to the acceptable results, the novelty of the classification procedure and its fast application, the method is recommended for further study and practical implementation.

Computers in Cardiology 1999. Vol.26 (Cat. No.99CH37004), 1999

A good fuzzy classijier for a three-class arrhythmia problem is analyzed "a posteriori ", in order to estimate the importance of the ECG features for the final system performance. A set offuzzy rules is automatically built on forty MIT-BIH database's files, using fourteen ECG measures to characterize each beat. The implemented fuzzy model correct& classifies the 92% of normal beats, the 80% of PVBs and the 56% of SVPBs of the test set, The information contained in the fuzzy model is then quanti$ed and the gain in information, derived from the application of the fuzzy rules along a given input dimension, is measured. Only three ECG features (QRS width, PR segment and prematuriy degree) seem to c a r v the main responsibility for the correct classijlcation of the three arrhythmic classes. The uninformative character of the ECG measures with lowest information gain is confirmed by the almost unalteredwhen not improvedperformance of the classiJier, when these features are removedfrom the input vector. Computers in Cardiology 1999;26:679-682.

2017

Electrocardiography (ECG or EKG) uses electrodes to measure an electrical activity of heart. These heart signal allows comprehensive analysis of the heart condition. Acquisition of ECG signal from the human body is a non-invasive process that opens the door to new possibilities for the application of advanced signal processing and data analysis techniques in the diagnosis of heart diseases. With the availability and help of large database of ECG signal, a computationally intelligent system can be built and can take place of a cardiologist. The various abnormalities in the patient heart can be detected to identify various cardiac disease by use of Adaptive Neuro-Fuzzy Inference System (ANFIS) preprocessed by subtractive clustering. In this paper, six types of heartbeats are classified: Normal sinus rhythm, premature ventricular contraction (PVC), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Atrial Premature Contraction (APC) and paced beats. The research aimed a...

2010

Arrhythmia diagnosis is commonly conducted through visual analysis of human electrocardiograms, a very resource consuming task for physicians. In this paper we present a computational approach for arrhythmia detection based on heart rate variability signal analysis and the application of a neuro-fuzzy classification model called SONFIS. The aforementioned method generates a set of linguistically interpretable inference rules for pattern classification and outperforms artificial neural networks and support vector machines in accuracy and several other performance indicators.