title: AFW extraction based on MCA.
abstract: This paper improves the learning dictionary construction method for morphological component analysis (MCA) to separate the atrial and ventricular signals. The incoherence is added into the objective function to reduce the sparsity ratio between the atrial and ventricular dictionaries. By using the dictionaries, atrial and ventricular activities are separated from the location of the coefficients. We test the methods on both the synthetic and real atrial data. While extracting AFW from synthetic data, we use the Poisson relation as the measure. The result shows that we can obtain greater relation value using the method this paper presents than using the methods of ABS and PCA. We also conduct spectral analysis on AFW extracted from real atrial data.
authors.affiliation: Department of Mechanical Engineering, Huazhong University of Science and Technology, Luoyu Road 1037, Hongshan District, Wuhan, Hubei, China.
authors.forename: Junjiang
authors.initials: J
authors.lastname: Zhu
authors.affiliation: Department of Mechanical Engineering, Huazhong University of Science and Technology, Luoyu Road 1037, Hongshan District, Wuhan, Hubei, China.
authors.forename: Lingsong
authors.initials: L
authors.lastname: He
authors.affiliation: Department of Mechanical Engineering, Huazhong University of Science and Technology, Luoyu Road 1037, Hongshan District, Wuhan, Hubei, China.
authors.forename: Jianhao
authors.initials: J
authors.lastname: Du
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001281:Atrial Fibrillation / Q000175:diagnosis*; D006325:Heart Atria / Q000473:pathology*; D006352:Heart Ventricles / Q000473:pathology*; D006801:Humans; D016012:Poisson Distribution
keywords: Atrial fibrillation wave; learned dictionary; morphological component analysis; sparse