Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database
Computers and Biomedical Research
Machine Learning
ECG beat classification using neuro-fuzzy network
Pattern Recognition Letters
Advanced Methods And Tools for ECG Data Analysis
Advanced Methods And Tools for ECG Data Analysis
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Expert Systems with Applications: An International Journal
Pattern Recognition Letters
Evolving a Bayesian classifier for ECG-based age classification in medical applications
Applied Soft Computing
Adaptive wavelet network for multiple cardiac arrhythmias recognition
Expert Systems with Applications: An International Journal
Integration of independent component analysis and neural networks for ECG beat classification
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A new arrhythmia clustering technique based on Ant Colony Optimization
Journal of Biomedical Informatics
Selection of significant independent components for ECG beat classification
Expert Systems with Applications: An International Journal
A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network
Expert Systems with Applications: An International Journal
Supervised pattern classification based on optimum-path forest
International Journal of Imaging Systems and Technology - Contemporary Challenges in Combinatorial Image Analysis
Expert Systems with Applications: An International Journal
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
ECG beat classification using particle swarm optimization and radial basis function neural network
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Robust multiple cardiac arrhythmia detection through bispectrum analysis
Expert Systems with Applications: An International Journal
Efficient supervised optimum-path forest classification for large datasets
Pattern Recognition
Analyzing ECG for cardiac arrhythmia using cluster analysis
Expert Systems with Applications: An International Journal
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Evaluating the use of ECG signal in low frequencies as a biometry
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e., cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graph-based pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e., support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e., there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis.