An accurate and efficient method to train classifiers for atrial fibrillation detection in ECGs: Learning by asking better questions

Background: An increasing number of wearables are capable of measuring electrocardiograms (ECGs), which may help in early detection of atrial fibrillation (AF). Therefore, many studies focus on automated detection of AF in ECGs. A major obstacle is the required amount of manually labelled data. This study aimed to provide an efficient and...

Digital biomarkers and algorithms for detection of atrial fibrillation using surface electrocardiograms: A systematic review: Digital Biomarkers for AF in Surface ECGs

Aims: Automated detection of atrial fibrillation (AF) in continuous rhythm registrations is essential in order to prevent complications and optimize treatment of AF. Many algorithms have been developed to detect AF in surface electrocardiograms (ECGs) during the past few years. The aim of this systematic review is to gain more insight into...

Automated classification of brain tissue: Comparison between hyperspectral imaging and diffuse reflectance spectroscopy

In neurosurgery, technical solutions for visualizing the border between healthy brain and tumor tissue is of great value, since they enable the surgeon to achieve gross total resection while minimizing the risk of damage to eloquent areas. By using real-time non-ionizing imaging techniques, such as hyperspectral imaging (HSI), the spectral...