Signal-quality-aware multisensor fusion for atrial fibrillation detection.

This letter introduces a novel method to enhance atrial fibrillation detection accuracy in healthcare monitoring. Wearable devices often face inconsistent signal quality due to noise. To address this, a multimodal data fusion technique that improves signal reliability during continuous monitoring is proposed. The method improves the precision of detecting R-R intervals by integrating wavelet coefficients from electrocardiogram, photoplethysmogram, and arterial blood pressure signals, weighted according to the quality of each signal. Furthermore, a bi-directional long short-term memory network is developed to accurately detect AF based on the derived heartrate or R-R intervals. Unlike prior studies, this work uniquely evaluates the system's performance under noisy conditions, demonstrating significant accuracy improvements over single-channel methods. The system's generalizability is confirmed by evaluating the classifier's performance as the number of sensor inputs increases. At a signal-to-noise ratio of -10 dB, the accuracy improves by 4.51% with two sensor inputs and by 10.92% with three inputs, compared to using a single input.