AIMS: This study presents an automatic diagnostic method for the discrimination between persistent and long-standing atrial fibrillation (AF) based on the surface electrocardiogram (ECG). METHODS AND RESULTS: Standard 12-lead ECG recordings were acquired in 53 patients with either persistent (N= 20) or long-standing AF (N= 33), the latter including both long-standing persistent and permanent AF. A combined frequency analysis of multiple ECG leads followed by the computation of standard complexity measures provided a method for the quantification of spatiotemporal AF organization. All possible pairs of precordial ECG leads were analysed by this method and resulting organization measures were used for automatic classification of persistent and long-standing AF signals. Correct classification rates of 84.9% were obtained, with a predictive value for long-standing AF of 93.1%. Spatiotemporal organization as measured in lateral precordial leads V5 and V6 was shown to be significantly lower during long-standing AF than persistent AF, suggesting that time-related alterations in left atrial electrical activity can be detected in the ECG. CONCLUSION: Accurate discrimination between persistent and long-standing AF based on standard surface recordings was demonstrated. This information could contribute to optimize the management of sustained AF, permitting appropriate therapeutic decisions and thereby providing substantial clinical cost savings.
AIMS: This study presents an automatic diagnostic method for the discrimination between persistent and long-standing atrial fibrillation (AF) based on the surface electrocardiogram (ECG). METHODS AND RESULTS: Standard 12-lead ECG recordings were acquired in 53 patients with either persistent (N= 20) or long-standing AF (N= 33), the latter including both long-standing persistent and permanent AF. A combined frequency analysis of multiple ECG leads followed by the computation of standard complexity measures provided a method for the quantification of spatiotemporal AF organization. All possible pairs of precordial ECG leads were analysed by this method and resulting organization measures were used for automatic classification of persistent and long-standing AF signals. Correct classification rates of 84.9% were obtained, with a predictive value for long-standing AF of 93.1%. Spatiotemporal organization as measured in lateral precordial leads V5 and V6 was shown to be significantly lower during long-standing AF than persistent AF, suggesting that time-related alterations in left atrial electrical activity can be detected in the ECG. CONCLUSION: Accurate discrimination between persistent and long-standing AF based on standard surface recordings was demonstrated. This information could contribute to optimize the management of sustained AF, permitting appropriate therapeutic decisions and thereby providing substantial clinical cost savings.
Authors: Giorgio Luongo; Luca Azzolin; Steffen Schuler; Massimo W Rivolta; Tiago P Almeida; Juan P Martínez; Diogo C Soriano; Armin Luik; Björn Müller-Edenborn; Amir Jadidi; Olaf Dössel; Roberto Sassi; Pablo Laguna; Axel Loewe Journal: Cardiovasc Digit Health J Date: 2021-04
Authors: Matthias Daniel Zink; Rita Laureanti; Ben J M Hermans; Laurent Pison; Sander Verheule; Suzanne Philippens; Nikki Pluymaekers; Mindy Vroomen; Astrid Hermans; Arne van Hunnik; Harry J G M Crijns; Kevin Vernooy; Dominik Linz; Luca Mainardi; Angelo Auricchio; Stef Zeemering; Ulrich Schotten Journal: Front Physiol Date: 2022-03-04 Impact factor: 4.566
Authors: Giorgio Luongo; Felix Rees; Deborah Nairn; Massimo W Rivolta; Olaf Dössel; Roberto Sassi; Christoph Ahlgrim; Louisa Mayer; Franz-Josef Neumann; Thomas Arentz; Amir Jadidi; Axel Loewe; Björn Müller-Edenborn Journal: Front Cardiovasc Med Date: 2022-02-28