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  • Title: Automatic atrial tachyarrhythmia detection from intracardiac electrograms.
    Author: Rossi P, Casaleggio A, Morando M, Corbucci G, Reggiani M, Sartori G, Borgo E.
    Journal: Ital Heart J; 2000 Jun; 1(6):412-9. PubMed ID: 10929742.
    Abstract:
    BACKGROUND: Automatic atrial tachyarrhythmia recognition is crucial in order to allow a correct switching-mode function of dual-chamber pacemakers and to avoid inappropriate shocks of ventricular implantable cardioverter-defibrillators. In this paper we considered three algorithms suitable for implantable devices. The first was based on the atrial cycle length; the others analyze different morphologic characteristics of atrial signals. METHODS: Intracardiac bipolar electrogram recordings were obtained from the high right atrium during electrophysiological study. Twenty patients were considered, some of them presenting with different types of cardiac rhythm at different intervals of the study. Cardiac rhythms were divided into three groups: sinus rhythm consisting of 2,196 s obtained from 12 subjects, atrial fibrillation consisting of 771 s obtained from 7 subjects, and atrial flutter consisting of 1,793 s obtained from 7 subjects. The automatic detection was performed on each electrogram segment lasting 1 or 4 s. Atrial segments were separated into two subgroups: the first for the training of the algorithm and the second for testing and validation of results. We considered two types of statistical analysis: comparison between pairs of rhythm (paired classification), and classification among the three different groups (direct classification). RESULTS: The combination of the cycle length algorithm with a morphological method achieved the best performance for both statistical analyses. Paired classification resulted in the following: atrial fibrillation vs sinus rhythm was detected with no error; atrial flutter vs sinus rhythm with a total accuracy of 99.3% (sensitivity 99.4%, specificity 99.2%); atrial fibrillation vs atrial flutter with a total accuracy of 99.1% (sensitivity 98.5%, specificity 99.4%). The total accuracy achieved for the direct classification was 98.6% (average sensitivity 98.5%, specificity 98.8%). CONCLUSIONS: Our results support the association of algorithms for future enhancement of atrial tachyarrhythmia detection in dual-chamber devices, thanks to the limited computational effort.
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