title: Automatic detection of the electrocardiogram T-wave end.
abstract: Various methods for automatic electrocardiogram T-wave detection and Q-T interval assessment have been developed. Most of them use threshold level crossing. Comparisons with observer detection were performed due to the lack of objective measurement methods. This study followed the same approach. Observer assessments were performed on 43 various T-wave shapes recorded: (i) with 100 mm s-1 equivalent paper speed and 0.5 mV cm-1 sensitivity; and (ii) with 160 mm s-1 paper speed and vertical scaling ranging from 0.07 to 0.02 mV cm-1, depending on the T-wave amplitude. An automatic detection algorithm was developed by adequate selection of the T-end search interval, improved T-wave peak detection and computation of the angle between two 10 ms long adjacent segments along the search interval. The algorithm avoids the use of baseline crossing and direct signal differentiation. It performs well in cases of biphasic and/or complex T-wave shapes. Mean differences with respect to observer data are 13.5 ms for the higher gain/speed records and 14.7 ms for the lower gain/speed records. The algorithm was tested with 254 various T-wave shapes. Comparisons with two other algorithms are presented. The lack of a 'gold standard' for the T-end detection, especially if small waves occur around it, impeded adequate interobserver assessment and evaluation of automatic methods. It is speculated that a simultaneous presentation of normal and high-gain records might turn more attention to this problem. Automatic detection methods are in fact faced with 'high-gain' data, as high-resolution analogue-to-digital conversion is already widely used.
authors.affiliation: Centre of Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria. ikdas@argo.bas.bg
authors.forename: I K
authors.initials: IK
authors.lastname: Daskalov
authors.forename: I I
authors.initials: II
authors.lastname: Christov
mesh_terms: D000465:Algorithms; D002318:Cardiovascular Diseases / Q000175:diagnosis; D004562:Electrocardiography; D001330:Electronic Data Processing; D006801:Humans; D012815:Signal Processing, Computer-Assisted