  Background: Cardiac resynchronization therapy (CRT) has emerged as an
effective treatment for heart failure patients with electrical dyssynchrony.
However, accurately predicting which patients will respond to CRT remains a
challenge. This study explores the application of deep transfer learning
techniques to train a predictive model for CRT response. Methods: In this
study, the short-time Fourier transform (STFT) technique was employed to
transform ECG signals into two-dimensional images. A transfer learning approach
was then applied on the MIT-BIT ECG database to pre-train a convolutional
neural network (CNN) model. The model was fine-tuned to extract relevant
features from the ECG images, and then tested on our dataset of CRT patients to
predict their response. Results: Seventy-one CRT patients were enrolled in this
study. The transfer learning model achieved an accuracy of 72% in
distinguishing responders from non-responders in the local dataset.
Furthermore, the model showed good sensitivity (0.78) and specificity (0.79) in
identifying CRT responders. The performance of our model outperformed clinic
guidelines and traditional machine learning approaches. Conclusion: The
utilization of ECG images as input and leveraging the power of transfer
learning allows for improved accuracy in identifying CRT responders. This
approach offers potential for enhancing patient selection and improving
outcomes of CRT.
