Deep learning model to identify potential occurrence of reduced LVEF in patients with implantable loop recorders

Abstract

Abstract Background Early detection of left ventricular systolic dysfunction (LVSD) using minimally invasive electrocardiographic assessment in an ambulatory setting may be important considering the increasing economic burden of HF. Recently artificial intelligence (AI) algorithms have been reported to classify reduced ejection fraction using snapshot 12-lead ECG measurements, however the ability of AI to identify LVSD using only a single lead ambulatory ECG is unknown. Purpose We aimed to develop a convolution neural network (CNN) based deep learning algorithm using single lead ECG to predict ejection fraction less than 40% in ambulatory patients implanted with implantable loop recorders (ILRs). Methods We linked ILR patients with LVEF measurements from a de-identified database of aggregated electronic health record (EHR) data during the period of 2007-2021 to a manufacturer’s device database with ambulatory single lead ECGS. The routine ECG transmissions from the ILR devices were paired with LVEF measured within a week. The data was pre-processed to obtain the time-frequency components of ECG using wavelet transform. The wavelet coefficients were then used as input to train and validate a 2D-CNN model. An independent test dataset consisting of data from patient not included in training/validation dataset was used to assess the performance of CNN model to classify LVEF< = 40% using the metrics area under the curve (AUC), sensitivity and specificity. Results A total of 35,741 unique LVEF-ECG pairs collected from 2,249 ILR patients were used to train the model; an independent validation dataset consisted of 6,721 unique LVEF ECG pairs from 750 patients and independent test dataset consisted of 6,611 unique LVEF ECG pairs from 750 patients. In our CNN model, cross entropy was used as loss function and adaptive moment estimation (ADAM) was used as the optimizer. The initial learning rate was selected as 0.0001, epochs as 20 and mini batch size as 64. The threshold to delineate reduced ejection fraction (LVEF< = 40%) was set as 0.1 (Figure 1). The model yielded accuracy, sensitivity, specificity, AUC of 75%,70%, 76% and 0.8 respectively in the independent test set (Figure 2). Conclusion A deep learning algorithm applied to ambulatory single lead ECGs acquired by ILR can detect LVEF< = 40%. This continuous ambulatory AI-ECG monitoring may allow to identify longitudinal changes in LVEF post ILR implant via remote monitoring.Figure 1:Model output vs Actual EF rangeFigure 2:ROC