Programmatic Theme: Data Science

Abstract: Atrial fibrillation (AF) is the most common cardiac arrhythmia as well as a significant risk factor in heart failure and coronary artery disease. AF can be detected by using a short ECG recording. However, discriminating atrial fibrillation from normal sinus rhythm, other arrhythmia and strong noise, given a short ECG recording, is challenging. Towards this end, we propose MultiFusionNet, a deep learning network that uses a multiplicative fusion method to combine two deep neural networks trained on different sources of knowledge, i.e., extracted features and raw data. Thus, MultiFusionNet can exploit the relevant extracted features to improve upon the utilization of the deep learning model on the raw data. Our experiments show that this approach offers the most accurate AF classification and outperforms recently published algorithms that either use extracted features or raw data separately. Finally, we show that our multiplicative fusion method for combining the two sub-networks outperforms several other combining methods.

Learning Objective: With this study, the readers will achieve:
- ECG classification problem and its applications in Atrial Fibrillation detection
- State-of-the-art approaches for ECG classification
- Proposed fusion approach for ECG classification which combines sub-network for raw data and extracted features.
- Tradeoffs between training time and accuracy of a deep learning approach

Authors:

LUAN TRAN (Presenter)
University of Southern California

Yanfang Li, University of Southern California
Luciano Nocera, University of Southern California
Cyrus Shahabi, University of Southern California
Li Xiong, Emory University