Identification of Premature Ventricular Contraction (PVC) Caused by Disturbances in Calcium and Potassium Ion Concentrations Using Artificial Neural Networks

Abstract

Abnormalities in the concentrations of metallic ions such as calcium and potassium can, in principle, lead to cardiac arrhythmias. Unbalance of these ions can alter the electrocardiogram (ECG) signal. Changes in the morphology of the ECG signal can occur due to changes in potassium concentration, and shortening or extension of this signal can occur due to calcium excess or deficiency, respectively. The diagnosis of these disorders can be complicated, making the modeling of such a system complex. In the present work an artificial neural network (ANN) is proposed as a model for pattern recognition of the ECG signal. The procedure can be, in principle, used to identify changes in the morphology of the ECG signal due to alterations in calcium and potassium concentrations. An arrhythmia database of a widely used experimental data was considered to simulate different ECG signals and also for training and validation of the methodology. The proposed approach can recognize premature ventricular contractions (PVC) arrhythmias, and tests were performed in a group of 47 individuals, showing significant quantitative results, on average, with 94% of confidence. The model was also able to detect ions changes and showed qualitative indications of what ion is affecting the ECG. These results indicate that the method can be efficiently applied to detect arrhythmias as well as to identify ions that may contribute to the development of cardiac arrhythmias. Accordingly, the actual approach might be used as an alternative tool for complex studies involving modifications in the morphology of the ECG signal associated with ionic changes.

Classification of cardiac arrhythmias using deep learning

“…If an arbitrary position between the RnRn+1 interval and the RnRn+1 interval is set as the ECG test segment, the duration range of PVC may be interpreted incorrectly. Therefore, the scope of the ECG test data to be evaluated for classification is fed to the input neurons so as to include the n th ECG rhythm, [x n first, x n end] covering three to seven RR intervals as described in equation (1) and (2).As the number of intervals increases, the starting position equation (1) is placed to the point of the RR interval at the position before the interval included in the range, and the ending position equation 2is always set between the two intervals appearing after the reference interval:…”

“…Particularly, Conway, J.C.D. et al [1] developed artificial neural network (ANN) by resampling QRS complex of an ECG data to detect PVC beat that was caused by disturbances in calcium and potassium ion concentrations. Gothwal, H.A et al [2] also proposed a feedforward network with one hidden layer to find the presence of tachycardia, bradycardia, super ventricular tachycardia, bundle branch block and ventricular tachycardia arrhythmia by supplying heart rate and QRS features into the input layer.…”

Premature Ventricular Contraction (PVC) Recognition Using DCT-CWT Based Discriminant and Optimized RBF Neural Network

“…After that, the normal beats are distinguished from PVCs beats using RR interval duration. Additionally, a technique involving a neural network was built to identify PVC that has been presented in [21]. Moreover, the support vector machine and Gaussian process, to detect PVC, have been suggested by researchers in [22].…”

Automatic Premature Ventricular Contraction Detection Using Deep Metric Learning and KNN

“…Conway et al used an ANN to detect PVC without manually extracting features [ 31 ]. The ANN’s input corresponds to the 30 points of the QRS complex.…”