Quantitative Analysis Using Consecutive Time Window for Unobtrusive Atrial Fibrillation Detection Based on Ballistocardiogram Signal.

Atrial fibrillation (AF) is the most common clinically significant arrhythmia; therefore, AF detection is crucial. Here, we propose a novel feature extraction method to improve AF detection performance using a ballistocardiogram (BCG), which is a weak vibration signal on the body surface transmitted by the cardiogenic force. In this paper, continuous time windows (CTWs) are added to each BCG segment and recurrence quantification analysis (RQA) features are extracted from each time window.

Attention-based multi-scale features fusion for unobtrusive atrial fibrillation detection using ballistocardiogram signal.

Background: Atrial fibrillation (AF) represents the most common arrhythmia worldwide, related to increased risk of ischemic stroke or systemic embolism. It is critical to screen and diagnose AF for the benefits of better cardiovascular health in lifetime. The ECG-based AF detection, the gold standard in clinical care, has been restricted by the need to attach electrodes on the body surface.

The role of ultrasonography in the diagnosis of talocalcaneal coalitions.

Background: Patients with talocalcaneal coalitions (TCC) often undergo computed tomography (CT). However, ultrasonography diagnosis of TCC has been seldom done according to the literature.

Purpose: To investigate the accuracy of ultrasonography in diagnosing TCC compared to CT.