title: A novel approach for arrhythmia diagnosis: Self-adaptive and distribution-free mode.
abstract: Arrhythmia diagnosis is very significant to ensure human health. In this paper, a new model is developed for arrhythmia diagnosis. A salient feature of the algorithm is a synergistic combination of statistical and fuzzy set-based techniques. It is distribution-free and is realized in an unsupervised mode. Arrhythmia diagnosis is viewed as a certain statistical hypothesis testing. 'Abnormal' is typically a much complex concept, so it can be described with the technology of fuzzy sets which bring a facet of robustness to the overall scheme and play an important role in the successive step of hypothesis testing. Intensive fuzzification is engaged in parameters determination which is self-adaptive and no parameter needs to be specified by the user. The algorithm is validated with a number of experiments, which prove its effectiveness for arrhythmia diagnosis.
authors.affiliation: MOE-MS Key Laboratory of Natural Language Processing and Speech, Harbin Institute of Technology, 150001, Harbin, PR China.
authors.forename: Fenghuan
authors.initials: F
authors.lastname: Li
authors.affiliation: MOE-MS Key Laboratory of Natural Language Processing and Speech, Harbin Institute of Technology, 150001, Harbin, PR China.
authors.forename: Dequan
authors.initials: D
authors.lastname: Zheng
authors.affiliation: MOE-MS Key Laboratory of Natural Language Processing and Speech, Harbin Institute of Technology, 150001, Harbin, PR China.
authors.forename: Tiejun
authors.initials: T
authors.lastname: Zhao
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis*; D003198:Computer Simulation; D004562:Electrocardiography / Q000379:methods*; D017143:Fuzzy Logic; D006801:Humans; D015233:Models, Statistical
keywords: Arrhythmia diagnosis; distribution-free; fuzzy sets; self-adaptive; statistical testing