End-to-end sensor fusion and classification of atrial fibrillation using deep neural networks and smartphone mechanocardiography.

. The purpose of this research is to develop a new deep learning framework for detecting atrial fibrillation (AFib), one of the most common heart arrhythmias, by analyzing the heart's mechanical functioning as reflected in seismocardiography (SCG) and gyrocardiography (GCG) signals. Jointly, SCG and GCG constitute the concept of mechanocardiography (MCG), a method used to measure precordial vibrations with the built-in inertial sensors of smartphones.

Atrial Fibrillation Detection via Accelerometer and Gyroscope of a Smartphone.

We present a smartphone-only solution for the detection of atrial fibrillation (AFib), which utilizes the built-in accelerometer and gyroscope sensors [inertial measurement unit, (IMU)] in the detection. Depending on the patient's situation, it is possible to use the developed smartphone application either regularly or occasionally for making a measurement of the subject. The smartphone is placed on the chest of the patient who is adviced to lay down and perform a noninvasive recording, while no external sensors are needed.

Effect of digital edge-enhancement on the visibility of normal parenchymal lung markings.

Digital 'conventional-like' (C-L) and edge-enhanced (E-E) posteroanterior chest roentgenograms of 42 healthy individuals were ranked twice (interval of at least 5 days) in the order of increasing lung parenchymal markings (a total of four rankings). This was done by three radiologists, two residents, one medical student and one radiographer. There was a good general consistency of rankings for both the C-L and E-E images.

Observed lung markings in normal chest roentgenograms.

The digital chest posterior-anterior roentgenograms of 42 healthy individuals were ranked twice (interval of at least 5 days) in the order of increasing lung parenchymal markings. The evaluations were made by three radiologists, two residents, a medical student and a radiographer. All observers regardless of their radiological experience showed good intraobserver correlations between their two subsequent rankings (p < 0.