Classifying Multiple Cardiac Signals Simultaneously Using Radial Basis Function Neural Networks

Introduction: Classifying multiple cardiac signals simultaneously is still a challenge for computational methods. We propose a computational algorithm that performs a multi-class classification. We use a combination of global features of the electrocardiogram (ECG) signal, where we statistically analyze the complete signal, as well as include the local features of the cardiac cycles. Methodology: 286 ECG signals obtained from the MIT-BIH Database were evaluated:18 healthy, 48 with arrhythmia, 23 with Atrial Fibrillation, 70 with apnea, 78 with supraventricular arrhythmias, 15 with congestive heart failure, 11 with epilepsy, 5 with fetal electrocardiogram, 18 with ventricular tachyarrhythmia. For each of the 286 signals, 1200 cardiac cycles from a total of one second measurement were used, characterized by 400 milliseconds before and 600 milliseconds after the R wave. We calculated variance, skewness and kurtosis from the 286 signals, extracting 1200 features from the cardiac cycles for each of the ECG signals. These hyperparameters were used as input for the Radial Basis Function Neural Networks (RBF), where 80% were for training and 20% for testing, with cross-validation of K-fold = 10. The RBF had one hidden layer with error correction by mean squared error, number of iterations equal to 1000, number of neurons equal to 25 and learning rate 0.01. Results: Each group of cardiac signals evaluated in this study had its own features and, therefore, had unique parameters for each of the different pathologies in a three dimensional plot, as illustrated in the figure. The average accuracy of the proposed method for multi-class classification of cardiac signals was 99.98 %. Conclusion: The high success rate in the multi-class classification for eleven classes of cardiac signals makes the proposed methodology an promising alternative to aid in the diagnosis of cardiac pathologies.