A deep learning modular ECG approach for cardiologist assisted adjudication of atrial fibrillation and atrial flutter episodes.

Background: Detection of atrial tachyarrhythmias (ATA) on long-term electrocardiogram (ECG) recordings is a prerequisite to reduce ATA-related adverse events. However, the burden of editing massive ECG data is not sustainable. Deep learning (DL) algorithms provide improved performances on resting ECG databases.

Sleep apnea diagnosis using an ECG Holter device including a nasal pressure (NP) recording: validation of visual and automatic analysis of nasal pressure versus full polysomnography.

Background: New simplified techniques for diagnosing sleep apnea should be specially tailored for easy use in cardiologic practice.

Objectives: e dedicated one of the channels of a Holter Electrocardiogram (ECG) device (SpiderView() ELA Medical, France) to nasal pressure (NP) recordings. We also developed an automatic analysis of NP signal providing an apnea-hypopnea index (AHI) for physicians without the know-how in sleep medicine.