Inflection point analysis: A machine learning approach for extraction of IEGM active intervals during atrial fibrillation |
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| Affiliation: | 1. Cardiac Arrhythmia Signal Analysis Laboratory, School of Medicine, Queen''s University, K7L 3N6 Kingston, Ontario, Canada;2. The Advanced Multi-Dimensional Signal Processing Laboratory, Department of Electrical and Computer Engineering, Queen''s University, K7L 3N6 Kingston, Ontario, Canada;1. Children''s Hospital and Pediatric Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA;2. Pediatric and Adolescent Comparative Effectiveness Research, Indiana University, Riley Child Development Center, 705 Riley Hospital Drive, Room 5837, Indianapolis, IN 46202, USA;3. Regenstrief Institute, Inc., 410 West 10th Street, Suite 2000, Indianapolis, IN 46202-3012, USA;4. Children''s Health Services Research, Indiana University, 410 West 10th Street, Suite 1000, Indianapolis, IN 46202-3012, USA;1. University Panthéon-Sorbonne, 90 Rue de Tolbiac, 75634 Paris, France;2. University of Lorraine, Cité Universitaire, 57000 Metz, France;3. University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia;1. Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta GA, 30332, USA;2. Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Cefalù, PA, Italy;3. Department of Industrial and Digital Innovation (DIID) – University of Palermo, PA, Italy;4. Medical Physics Unit, Cannizzaro Hospital, Catania, Italy;5. Nuclear Medicine Department, Cannizzaro Hospital, Catania, Italy |
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| Abstract: | ObjectiveIn this paper, we propose a novel algorithm to extract the active intervals of intracardiac electrograms during atrial fibrillation.MethodsFirst, we show that the characteristics of the signal waveform at its inflection points are prominent features that are implicitly used by human annotators for distinguishing between active and inactive intervals of IEGMs. Then, we show that the natural logarithm of features corresponding to active and inactive intervals exhibits a mixture of two Gaussian distributions in three dimensional feature space. An Expectation Maximization algorithm for Gaussian mixtures is then applied for automatic clustering of the features into two categories.ResultsThe absolute error in onset and offset estimation of active intervals is 6.1 ms and 10.7 ms, respectively, guaranteeing a high resolution. The true positive rate for the proposed method is also 98.1%, proving the high reliability.ConclusionThe proposed method can extract the active intervals of IEGMs during AF with a high accuracy and resolution close to manually annotated results.SignificanceIn contrast with some of the conventional methods, no windowing technique is required in our approach resulting in significantly higher resolution in estimating the onset and offset of active intervals. Furthermore, since the signal characteristics at inflection points are analyzed instead of signal samples, the computational time is significantly low, ensuring the real-time application of our algorithm. The proposed method is also robust to noise and baseline variations thanks to the Laplacian of Gaussian filter employed for extraction of inflection points. |
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| Keywords: | Intra-cardiac electrogram Atrial fibrillation Inflection point analysis Gaussian mixture model Expectation Maximization |
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