title: Model-based rhythm analysis of the ECG. Evaluation of a prototype implementation.
abstract: Current computer algorithms that interpret cardiac rhythms based solely on the surface electrocardiogram are limited, yet offer many benefits to health care. To address the limitations, novel computer algorithms for the automatic diagnosis of complex cardiac rhythms based solely on the surface electrocardiogram are presented. Using the hypothesize-and-test paradigm, a physiologic model of the cardiac conduction system and production rule-based knowledge are combined to reason about the time- and space-varying characteristics of complex heart rhythms. In addition, an evaluation of a prototype implementation of the algorithms is presented. A database of the time of onset, width, and shape classifications of each P wave, QRS complex, and T wave from 59 electrocardiographic strips was developed from an introductory textbook by hand-annotation using calipers. The database was not used in the development of the prototype. The prototype's diagnoses were reviewed by a clinical cardiac electrophysiologist who was not involved in the development process. Pair-wise comparisons among the prototype, textbook, and cardiac electrophysiologist, assuming either the textbook or electrophysiologist as the gold standard, were performed. The specific comparisons performed were prototype versus textbook, electrophysiologist versus textbook, prototype versus electrophysiologist, and textbook versus electrophysiologist. For all diagnostic categories, sensitivities of 88.0%, 97.2%, 78.6%, and 82.1%, respectively, and specificities of 99.2%, 98.5%, 99.7%, and 99.8%, respectively, were attained. When accounting for design and implementation limitations of the prototype, sensitivities of 93.0%, 98.5%, 89.1%, and 92.7%, respectively, and specificities of 99.4%, 99.2%, 99.6%, and 99.8%, respectively, were attained. The results indicate that these algorithms offer clinical advantages over currently available arrhythmia analysis systems.
authors.affiliation: Department of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City.
authors.forename: D A
authors.initials: DA
authors.lastname: Tong
authors.forename: K
authors.initials: K
authors.lastname: Beckman
authors.forename: L E
authors.initials: LE
authors.lastname: Widman
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D004562:Electrocardiography; D006329:Heart Conduction System / Q000502:physiology; D006801:Humans; D008955:Models, Cardiovascular; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012984:Software; D013610:Tachycardia / Q000175:diagnosis