Key result
The proposed modified general-likelihood ratio method accurately identified active intervals of intracardiac electrograms during atrial fibrillation, achieving a true positive rate of 97.8%.
Why the study?
Do the proposed MGLR and separating-function-estimation tests accurately identify active intervals of intracardiac electrograms during atrial fibrillation compared to manual annotation?
Do the proposed MGLR and separating-function-estimation tests accurately identify active intervals of intracardiac electrograms during atrial fibrillation compared to manual annotation?
The proposed MGLR-based methods accurately identify the onset and duration of active intervals in intracardiac electrograms during atrial fibrillation, enabling real-time automated analysis.
High accuracy in small cohort supports automated AF electrogram analysis; leaves open need for clinical validation trials.
OBJECTIVE: We introduce novel methods to identify the active intervals (AIs) of intracardiac electrograms (IEGMs) during complex arrhythmias, such as atrial fibrillation (AF). METHODS: We formulate the AI extraction problem, which consists of estimating the beginning and duration of the AIs, as a sequence of hypothesis tests. In each test, we compare the variance of a small portion of the bipolar IEGM with its adjacent segments. We propose modified general-likelihood ratio (MGLR) and separating-function-estimation tests; we derive five test statistics (TSs), and show that the AIs can be obtained by threshold crossing the TSs. We apply the proposed methods to the IEGM segments collected from the left atrium of 16 patients (62.4 ± 8.2-years old, four females, four paroxysmal, and twelve persistent AF) prior to catheter ablation. The accuracy of our methods is evaluated by comparing them with previously developed methods and manual annotation (MA). RESULTS: Our results show a high level of similarity between the AIs of the proposed methods and MA, e.g., the true and false positive rates of one of the MGLR-based methods were, respectively, 97.8% and 1.4%. The mean absolute error from estimation of the onset and end of AIs and also for the estimation of the mean cycle length for that approach was 8.7 ± 10.5, 13 ± 15.5, and 4.2 ± 9.4 ms, respectively. CONCLUSION: The proposed methods can accurately identify onset and duration of AI of the IEGM during AF. SIGNIFICANCE: The proposed methods can be used for real-time automated analysis of AF, the most challenging complex arrhythmia.
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Shariat et al. (2016) studied Atrial fibrillation (n=16). Modified general-likelihood ratio (MGLR) and separating-function-estimation tests vs. Manual annotation and previously developed methods was evaluated on True positive rate of active interval identification. The proposed modified general-likelihood ratio method accurately identified active intervals of intracardiac electrograms during atrial fibrillation, achieving a true positive rate of 97.8%.
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