title: Detection of frequently overlooked electrocardiographic lead reversals using artificial neural networks.
abstract: Artificial neural networks can be used to recognize lead reversals in the 12-lead electrocardiogram at very high specificity, and the sensitivity is much higher than that of a conventional interpretation program. The neural networks developed in this and an earlier study for detection of lead reversals, in combination with an algorithm for the right arm/right foot lead reversal, would recognize approximately 75% of lead reversals encountered in clinical practice.
authors.affiliation: Department of Clinical Physiology and Theoretical Physics, Lund University, Sweden.
authors.forename: B
authors.initials: B
authors.lastname: Hedén
authors.forename: M
authors.initials: M
authors.lastname: Ohlsson
authors.forename: H
authors.initials: H
authors.lastname: Holst
authors.forename: M
authors.initials: M
authors.lastname: Mjöman
authors.forename: R
authors.initials: R
authors.lastname: Rittner
authors.forename: O
authors.initials: O
authors.lastname: Pahlm
authors.forename: C
authors.initials: C
authors.lastname: Peterson
authors.forename: L
authors.initials: L
authors.lastname: Edenbrandt
mesh_terms: D004562:Electrocardiography; D004566:Electrodes; D006801:Humans; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity