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.
This study investigates the reliability of self-applied smartphone mechanocardiography (sMCG) for the detection of atrial fibrillation (AFib). The results show that sMCG can accurately differentiate AFib from sinus rhythm in both physician- and self-applied recording scenarios.
This paper presents a comprehensive time-frequency pattern analysis approach for automated detection of AFib from smartphone-derived seismocardiography (SCG) and gyrocardiography (GCG) signals. The experimental results showed high accuracy, sensitivity, and specificity for both cross-validation and cross-database tests.
This paper investigates a range of daily life activities and uses a random forest classifier to detect them based on wrist motions and optical heart rate. The highest accuracy was achieved with a forest of 64 trees and 13-s signal segments.