Deep learning enabled classification of real-time respiration signals acquired by MoSSe quantum dot-based flexible sensors.

Respiration rate is a vital parameter which is useful for the earlier identification of diseases. In this context, various types of devices have been fabricated and developed to monitor different breath rates. However, the disposability and biocompatibility of such sensors and the poor classification of different breath rates from sensor data are significant issues in medical services.

AFCNNet: Automated detection of AF using chirplet transform and deep convolutional bidirectional long short term memory network with ECG signals.

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia and is characterized by the heart's beating in an uncoordinated manner. In clinical studies, patients often do not have visible symptoms during AF, and hence it is harder to detect this cardiac ailment. Therefore, automated detection of AF using the electrocardiogram (ECG) signals can reduce the risk of stroke, coronary artery disease, and other cardiovascular complications.