title: Neural networks for classification of ECG ST-T segments.
abstract: The usefulness of neural networks for pattern recognition in electrocardiographic (ECG) ST-T segments was assessed. Two thousand ST-T segments from the 12-lead ECG were visually classified singly into 7 different groups. The material was divided into a training set and a test set. Computer-measured ST-T data for each element in the training set, paired with the corresponding classification, was input to various configurations of software-based neural networks during a learning process. Thereafter, the networks correctly classified 90-95% of the individual ST-T segments in the test set. The importance of the size and composition of the training set in determining the performance of a network was clearly demonstrated. In conclusion, neural networks can be used for classification of ST-T segments. If carefully incorporated into a conventional ECG interpretation program, neural networks may well be of value for automated ECG interpretation in the near future.
authors.affiliation: Department of Clinical Physiology, University of Lund, Sweden.
authors.forename: L
authors.initials: L
authors.lastname: Edenbrandt
authors.forename: B
authors.initials: B
authors.lastname: Devine
authors.forename: P W
authors.initials: PW
authors.lastname: Macfarlane
mesh_terms: D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted