Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
Theoretical Computer Science
Independent component analysis: algorithms and applications
Neural Networks
ECG beats classification using multiclass support vector machines with error correcting output codes
Digital Signal Processing
Pattern Recognition Letters
Usage of eigenvector methods in implementation of automated diagnostic systems for ECG beats
Digital Signal Processing
Integration of independent component analysis and neural networks for ECG beat classification
Expert Systems with Applications: An International Journal
Selection of significant independent components for ECG beat classification
Expert Systems with Applications: An International Journal
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining recurrent neural networks with eigenvector methods for classification of ECG beats
Digital Signal Processing
Feature selection algorithm for ECG signals using Range-Overlaps Method
Expert Systems with Applications: An International Journal
A modified mixture of experts network structure for ECG beats classification with diverse features
Engineering Applications of Artificial Intelligence
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
The classification of heartbeats is crucial to identify an arrhythmia. This paper proposes a new method that combines independent component analysis (ICA) with sparse representation-based classification (SRC) to distinguish eight types of heartbeats. We use ICA to extract useful features from heartbeats. A feature vector consists of 100 ICA features along with a RR interval. We use SRC to compute a sparse representation of a test feature vector with respect to all training feature vectors. The type of a test feature vector is determined using the concentration degree of sparse coefficients on each heartbeat type. For experimental purposes, 9800 heartbeats are extracted from the MIT-BIH electrocardiogram (ECG) database. The results show that our proposed method performs better than conventional methods, with 98.35% accuracy and 94.49%---100% sensitivities to several heartbeat types.