Multiclass Classification of Sinus Rhythm, Atrial Fibrillation Phenotypes, and Acquisition Modality via ECG Morphological Normalization

Several studies have proposed decision-support systems for atrial fibrillation (AF) diagnosis using binary classification, distinguishing only healthy from diseased patients. We propose a four-class method that separates AF phenotypes from signal acquisition modality: normal sinus rhythm (SR), paroxysmal AF, persistent AF (LTAF), and intracardiac-acquisition AF (IAF). SR is the non-AF reference class rather than an AF subtype; IAF is the same arrhythmia recorded via intracardiac electrograms instead of surface ECG, a difference in acquisition modality, not an additional clinical phenotype. Each cardiac cycle is standardized by z-score and amplified by its own Shannon entropy, and four statistical measures (mean, variance, skewness, kurtosis) feed a k-nearest neighbors classifier. On the independent four-class test set, this representation reached 98.0% accuracy, with balanced accuracy and macro-F1 also at 98.0%. An ablation study, cross-checked with an SVM classifier, confirmed this gain comes from the representation itself, not from a specific classifier. Because each class came from a single database, we further tested whether the separability reflects AF phenotype or the source database: restricting sinus rhythm and AF to one database (AFDB) gave 90.9% balanced accuracy, and training on one database while testing on another (LTAFDB to AFDB) gave 84.2%. Both results are well above chance but below the four-class figure, showing that the representation carries a genuine physiological signal while confirming that part of the original accuracy reflects database-specific characteristics rather than the AF phenotype alone.