title: An algorithm to detect low incidence arrhythmic events in electrocardiographic records from ambulatory patients.
abstract: An algorithm was devised to detect low incidence arrhythmic events in electrocardiograms obtained during ambulatory monitoring. The algorithm incorporated baseline correction and R wave detection. The RR interval was used to identify tachycardia, bradycardia, and premature ventricular beats. Only a few beats before and after the arrhythmic event were stored. The software was evaluated on a prototype hardware system which consisted of an Intel 86/30 single board computer with a suitable analog pre-processor and an analog to digital converter. The algorithm was used to determine the incidence and type of arrhythmia in records from an ambulatory electrocardiogram (ECG) database and from a cardiac exercise laboratory. These results were compared to annotations on the records which were assumed to be correct. Standard criteria used previously to evaluate algorithms designed for arrhythmia detection were sensitivity, specificity, and diagnostic accuracy. Sensitivities ranging from 77 to 100%, specificities from 94 to 100%, and diagnostic accuracies from 92 to 100% were obtained on the different data sets. These results compare favourably with published results based on more elaborate algorithms. By circumventing the need to make a continuous record of the ECG, the algorithm could form the basis for a compact monitoring device for the detection of arrhythmic events which are so infrequent that standard 24-h Holter monitoring is insufficient.
authors.affiliation: Faculty of Science and Technology, Griffith University, Nathan, Australia.
authors.forename: S K
authors.initials: SK
authors.lastname: Hungenahally
authors.forename: R J
authors.initials: RJ
authors.lastname: Willis
mesh_terms: D000465:Algorithms; D000703:Analog-Digital Conversion; D001145:Arrhythmias, Cardiac / Q000175:diagnosis*; D003199:Computer Systems; D003936:Diagnosis, Computer-Assisted; D015716:Electrocardiography, Ambulatory; D006801:Humans; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D012984:Software; D012985:Software Design