Multirate systems and filter banks
Multirate systems and filter banks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
ECG beat classification using neuro-fuzzy network
Pattern Recognition Letters
ECG beats classification using multiclass support vector machines with error correcting output codes
Digital Signal Processing
Pattern Recognition Letters
Piecewise linear correction of ECG baseline wander: a curve simplification approach
Computer Methods and Programs in Biomedicine
A modified mixture of experts network structure for ECG beats classification with diverse features
Engineering Applications of Artificial Intelligence
An arrhythmia classification system based on the RR-interval signal
Artificial Intelligence in Medicine
Block-Based Neural Networks for Personalized ECG Signal Classification
IEEE Transactions on Neural Networks
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Selection of effective features for ECG beat recognition based on nonlinear correlations
Artificial Intelligence in Medicine
Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients
Computer Methods and Programs in Biomedicine
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Objective: This paper presents a noise-tolerant electrocardiogram (ECG) beat classification method based on higher order statistics (HOS) of subband components. Methods and material: Five levels of discrete wavelet transform (DWT) were applied to decompose the signal into six subband components. Higher order statistics proceeded to calculate four sets of HOS features from the three midband components, which together with three RR interval-related features constructed the primary feature set. A feature selection algorithm based on correlation coefficient and Fisher discriminality was then exploited to eliminate redundant features from the primary feature set. A feedforward backpropagation neural network (FFBNN) was employed as the classifier. Two sample selection strategies and four categories of noise artifacts were utilized to justify the capacity of the method. Results: More than 97.5% discrimination rate was achieved, no matter which of the two sampling selection strategies was used. By using the feature selection method, the feature dimension can be readily reduced from 30 to 18 with negligible decrease in accuracy. Compared with other method in the literature, the proposed method improves the sensitivities of most beat types, resulting in an elevated average accuracy. The proposed method is tolerant to environmental noises; as high as 91% accuracies were retained even when contaminated with serious noises, 10dB signal-to-noise ration (SNR), of different kinds. Conclusion: The results demonstrate the effectiveness and noise-tolerant capacities of the proposed method in ECG beat classification.