LSTM based Synthesis of 12-lead ECG Signal from a Reduced Lead Set

Abstract

This paper presents a patient-specific approach for reconstructing the standard 12-lead ECG from a minimal lead set. The 12-lead ECG signal acquisition impediment using ten electrodes comprises ambulatory monitoring, personalized healthcare, remote healthcare, and pediatric ECG lead placement. Furthermore, concurrently processing signals from multiple electrodes enhances the intricacy as well as the cost. Synthesizing 12-lead ECG from a reduced lead set becomes a better solution. This study proposes a recurrent neural network (RNN) long short-term memory (LSTM) to synthesize standard 12-lead ECG from the three predictor leads. The four performance metrics, namely correlation coefficient (cc), root mean square error (RMSE), and wavelet energy diagnostic distortion (WEDD), are employed to evaluate the performance of the proposed method. The proposed model obtained fine reconstruction quality and achieved better performance than most of the previously established works without compromising diagnostic information.