Wavelet applications in medicine
IEEE Spectrum
Expert systems for time-varying biomedical signals using eigenvector methods
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
ECG beats classification using multiclass support vector machines with error correcting output codes
Digital Signal Processing
A novel large-memory neural network as an aid in medical diagnosis applications
IEEE Transactions on Information Technology in Biomedicine
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
This paper presented the usage of statistics over the set of the features representing the electrocardiogram (ECG) signals. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of variabilities of the ECG signals. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were classified. The selected Lyapunov exponents, wavelet coefficients and the power levels of power spectral density (PSD) values obtained by eigenvector methods of the ECG signals were used as inputs of the MLPNN trained with Levenberg-Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the variabilities of the ECG signals.