End-to-end sensor fusion and classification of atrial fibrillation using deep neural networks and smartphone mechanocardiography
This paper presents a deep learning framework for detecting atrial fibrillation (AFib) by analyzing the heart’s mechanical functioning using smartphone mechanocardiography. The model achieves high accuracy in classifying sinus rhythm, AFib, and Noise categories.