title: Atrial activity extraction for atrial fibrillation analysis using blind source separation.
abstract: This contribution addresses the extraction of atrial activity (AA) from real electrocardiogram (ECG) recordings of atrial fibrillation (AF). We show the appropriateness of independent component analysis (ICA) to tackle this biomedical challenge when regarded as a blind source separation (BSS) problem. ICA is a statistical tool able to reconstruct the unobservable independent sources of bioelectric activity which generate, through instantaneous linear mixing, a measurable set of signals. The three key hypothesis that make ICA applicable in the present scenario are discussed and validated: 1) AA and ventricular activity (VA) are generated by sources of independent bioelectric activity; 2) AA and VA present non-Gaussian distributions; and 3) the generation of the surface ECG potentials from the cardioelectric sources can be regarded as a narrow-band linear propagation process. To empirically endorse these claims, an ICA algorithm is applied to recordings from seven patients with persistent AF. We demonstrate that the AA source can be identified using a kurtosis-based reordering of the separated signals followed by spectral analysis of the sub-Gaussian sources. In contrast to traditional methods, the proposed BSS-based approach is able to obtain a unified AA signal by exploiting the atrial information present in every ECG lead, which results in an increased robustness with respect to electrode selection and placement.
authors.affiliation: Bioengineering Electronic and Telemedicine Research Group, Electronic Engineering Department, Polytechnic University of Valencia, EPSG, Carretera Nazaret Oliva s/n, 46730, Gandía, Valencia, Spain. jjrieta@eln.upv.es
authors.forename: José Joaquín
authors.initials: JJ
authors.lastname: Rieta
authors.forename: Francisco
authors.initials: F
authors.lastname: Castells
authors.forename: César
authors.initials: C
authors.lastname: Sánchez
authors.forename: Vicente
authors.initials: V
authors.lastname: Zarzoso
authors.forename: José
authors.initials: J
authors.lastname: Millet
mesh_terms: D000465:Algorithms; D001281:Atrial Fibrillation / Q000175:diagnosis* / Q000503:physiopathology*; D018780:Body Surface Potential Mapping / Q000379:methods; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006325:Heart Atria / Q000503:physiopathology*; D006329:Heart Conduction System / Q000503:physiopathology; D006352:Heart Ventricles / Q000503:physiopathology; D006801:Humans; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity