Multi-Class Classification of Sleep Apnea/Hypopnea Events Based on Long Short-Term Memory Using a Photoplethysmography Signal.

In this study, we proposed a new method for multi-class classification of sleep apnea/hypopnea events based on a long short-term memory (LSTM) using photoplethysmography (PPG) signals. The three-layer LSTM model was used with batch-normalization and dropout to classify the multi-class events including normal, apnea, and hypopnea. The PPG signals, which were measured by the nocturnal polysomnography with 7 h from 82 patients suffered from sleep apnea, were used to model training and evaluation.

Amplitude based beat detection for atrial fibrillation in pacemaker.

Bradycardia is defined as a sinus rhythm of less than 60 beats per minute and atrial tachyarrhythmia including atrial fibrillation (AF) is frequently associated with bradycardia. Pacemaker is the only effective treatment for symptomatic bradycardia and automatic mode switching (AMS) function is built in pacemaker to switch mode in the presence of atrial tachyarrhythmia. AMS algorithms consider appropriate mode switching in case of undersensing or oversensing and this consideration makes their onset time and resynchronization time late.