Automated Myocardial Wall Motion Classification using Handcrafted Features vs a Deep CNN-based mapping.

Compared to other modalities such as computed tomography or magnetic resonance imaging, the appearance of ultrasound images is highly dependent on the expertise of the sonographer or clinician making the image acquisition, as well as the machine used, making it a challenge to analyze due to the frequent presence of artefacts, missing boundaries, attenuation, shadows, and speckle. In addition, manual contouring of the epicardial and endocardial walls exhibits large inconsistencies and variations as it is strongly dependent on the sonographer's training and expertise. Hence, in this paper we propose a fully automated image analysis framework to ultimately perform wall motion abnormality classification in 2D+T images.

Improving Visual Detection of Wall Motion Abnormality with Echocardiographic Image Enhancing Methods.

Analysis of wall motion abnormality using echocardiography is an established method for detecting myocardial ischemia. We describe a hybrid approach of enhancing 2D+T echo datasets with border detection and Eulerian motion magnification to improve the visual assessment of wall motion. We implemented a local phase-based approach using the monogenic signal and its derived features, either feature asymmetry (FA) or oriented feature symmetry (OFS), to detect boundaries of the heart structure.

A review of current sleep screening applications for smartphones.

Sleep disorders are a common problem and contribute to a wide range of healthcare issues. The societal and financial costs of sleep disorders are enormous. Sleep-related disorders are often diagnosed with an overnight sleep test called a polysomnogram, or sleep study involving the measurement of brain activity through the electroencephalogram.