Bimodal classification algorithm for atrial fibrillation detection from m-health ECG recordings

• Published in: Computers in biology and medicine Vol. 104; pp. 310 - 318
• Main Authors: Kruger, Grant H., Latchamsetty, Rakesh, Langhals, Nicholas B., Yokokawa, Miki, Chugh, Aman, Morady, Fred, Oral, Hakan, Berenfeld, Omer
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.01.2019; Elsevier Limited
• Subjects: Age; Algorithms; Arrhythmia; Atrial fibrillation; Atrial Fibrillation - diagnosis; Atrial Fibrillation - physiopathology; Atrial Fibrillation - therapy; Audio frequencies; Automation; Cardiac arrhythmia; Classification; Correlation analysis; Databases, Factual; Datasets; Diagnosis, Computer-Assisted; E-health; EKG; Electrocardiography; Ethics; Female; Fibrillation; Frequency domain; Humans; Laboratories; Male; Monitoring, Physiologic; Patients; Physicians; Recording; Rhythm classification; Risk analysis; Statistical analysis; Stroke; Telemedicine; Thromboembolism; Time domain; Waveforms; United States > US; Frequency domain; Time domain; E-health; Rhythm classification; Atrial fibrillation

Atrial Fibrillation (AF) is the most common cardiac arrhythmia, presenting a significant independent risk factor for stroke and thromboembolism. With the emergence of m-Health devices, the importance of automatic detection of AF in an off-clinic setting is growing. This study demonstrates the perfor...