Artificial Neural Network Based Synthesis of 12-Lead ECG Signal from Three Predictor Leads

Abstract

In clinical practice, continuous recording and monitoring of the standard 12-lead electrocardiogram (ECG) is often not feasible. The emerging technology and advancement to record the ECG signal without the help of the medical expert’s in-home care or ambulatory conditions with minimal complexity have become more common in recent times. We aim to devise a model to obtain the 12-lead ECG from a reduced number of leads to reduce the intricacy and enhance patient comfort and care. We propose a discrete wavelet transform (DWT) based artificial neural network (ANN) model that transforms a 3-lead ECG into a standard 12-lead ECG without losing diagnostic information. Prominent distortion measures, namely, correlation coefficient, R2 statistics, and wavelet energy diagnostic distortion (WEDD) are employed to evaluate the quality of the synthesis by the proposed model. The performance of the suggested model is compared with the antecedent models. The experimental result shows that the proposed technique can successfully synthesize the standard 12-lead ECG from the reduced lead sets.