title: [A research on real-time ventricular QRS classification methods for single-chip-microcomputers].
abstract: Ventricular QRS classification is key technique of ventricular arrhythmias detection in single-chip-microcomputer based dynamic electrocardiogram real-time analyser. This paper adopts morphological feature vector including QRS amplitude, interval information to reveal QRS morphology. After studying the distribution of QRS morphology feature vector of MIT/BIH DB ventricular arrhythmia files, we use morphological feature vector cluster to classify multi-morphology QRS. Based on the method, morphological feature parameters changing method which is suitable to catch occasional ventricular arrhythmias is presented. Clinical experiments verify missed ventricular arrhythmia is less than 1% by this method.
authors.affiliation: Huazhong University of Science and Technology.
authors.forename: L
authors.initials: L
authors.lastname: Peng
authors.forename: Z
authors.initials: Z
authors.lastname: Yang
authors.forename: L
authors.initials: L
authors.lastname: Li
authors.forename: H
authors.initials: H
authors.lastname: Chen
authors.forename: E
authors.initials: E
authors.lastname: Chen
authors.forename: J
authors.initials: J
authors.lastname: Lin
mesh_terms: D001145:Arrhythmias, Cardiac / Q000145:classification / Q000175:diagnosis*; D016000:Cluster Analysis; D015716:Electrocardiography, Ambulatory / Q000295:instrumentation; D006801:Humans; D008433:Mathematics; D008838:Microcomputers