MultiFusionNet: Atrial Fibrillation Detection With Deep Neural Networks

Area of Science:

• Cardiology
• Artificial Intelligence
• Biomedical Engineering

Background:

• Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia, posing significant risks for heart failure and coronary artery disease.
• Detecting AF via short electrocardiogram (ECG) recordings is crucial but challenging due to noise and similar rhythms.
• Accurate discrimination of AF from normal sinus rhythm and other arrhythmias requires advanced analytical methods.

Purpose of the Study:

• To develop and evaluate MultiFusionNet, a deep learning network for accurate atrial fibrillation classification from short ECG recordings.
• To investigate the efficacy of a multiplicative fusion method combining extracted features and raw ECG data.
• To compare the performance of MultiFusionNet against existing algorithms that utilize features or raw data independently.

Main Methods:

• Proposed MultiFusionNet, a deep learning architecture employing multiplicative fusion of two networks.
• Trained sub-networks on distinct data sources: extracted ECG features and raw ECG data.
• Experimentally validated the classification accuracy and performance against state-of-the-art methods.

Main Results:

• MultiFusionNet achieved superior accuracy in classifying atrial fibrillation compared to methods using only extracted features or raw data.
• The multiplicative fusion strategy significantly enhanced the model's ability to leverage both knowledge sources.
• The proposed fusion method outperformed other combination techniques evaluated in the study.

Conclusions:

• MultiFusionNet offers a highly accurate and effective deep learning solution for atrial fibrillation detection from short ECGs.
• Combining extracted features and raw data through multiplicative fusion is a promising strategy for improving arrhythmia classification.
• This approach holds potential for enhancing diagnostic tools in clinical cardiology and digital health applications.