A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy

Authors

Zhuo He, Michigan Technological University
Hongjin Si, The First Affiliated Hospital of Nanjing Medical University
Xinwei Zhang, The First Affiliated Hospital of Nanjing Medical University
Qing-Hui Chen, Michigan Technological UniversityFollow
Jiangang Zou, The First Affiliated Hospital of Nanjing Medical University
Weihua Zhou, Michigan Technological UniversityFollow

Document Type

Article

Publication Date

6-2-2023

Department

Department of Applied Computing; Department of Kinesiology and Integrative Physiology; Joint Center of Biocomputing and Digital Health; Institute of Computing and Cybersystems; Health Research Institute

Abstract

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.

Publisher's Statement

© 2023. Publisher’s version of record:

Publication Title

arXiv

Recommended Citation

He, Z., Si, H., Zhang, X., Chen, Q., Zou, J., & Zhou, W. (2023). A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy. arXiv. http://doi.org/https://doi.org/10.48550/arXiv.2306.01210
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p2/2279

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Version

Publisher's PDF