Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14365/3562
Title: | Classification of Holter registers by dynamic clustering using multi-dimensional particle swarm optimization | Authors: | Kiranyaz S. İnce, Türker Pulkkinen J. Gabbouj M. |
Keywords: | Benchmark database Classification system Dynamic clustering ECG signals Feature space Heart disease High dimensional data Key factors Local optima Master key Number of clusters Optimization problems Visual inspection Clustering algorithms Electrocardiography Electrochromic devices Speech recognition Particle swarm optimization (PSO) algorithm article automated pattern recognition cluster analysis computer assisted diagnosis electrocardiography expert system heart arrhythmia human methodology reproducibility sensitivity and specificity Algorithms Arrhythmias, Cardiac Cluster Analysis Diagnosis, Computer-Assisted Electrocardiography, Ambulatory Expert Systems Humans Pattern Recognition, Automated Reproducibility of Results Sensitivity and Specificity |
Abstract: | In this paper, we address dynamic clustering in high dimensional data or feature spaces as an optimization problem where multi-dimensional particle swarm optimization (MD PSO) is used to find out the true number of clusters, while fractional global best formation (FGBF) is applied to avoid local optima. Based on these techniques we then present a novel and personalized long-term ECG classification system, which addresses the problem of labeling the beats within a long-term ECG signal, known as Holter register, recorded from an individual patient. Due to the massive amount of ECG beats in a Holter register, visual inspection is quite difficult and cumbersome, if not impossible. Therefore the proposed system helps professionals to quickly and accurately diagnose any latent heart disease by examining only the representative beats (the so called master key-beats) each of which is representing a cluster of homogeneous (similar) beats. We tested the system on a benchmark database where the beats of each Holter register have been manually labeled by cardiologists. The selection of the right master key-beats is the key factor for achieving a highly accurate classification and the proposed systematic approach produced results that were consistent with the manual labels with 99.5% average accuracy, which basically shows the efficiency of the system. © 2010 IEEE. | Description: | 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 -- 31 August 2010 through 4 September 2010 -- Buenos Aires -- 83008 | URI: | https://doi.org/10.1109/IEMBS.2010.5626423 https://hdl.handle.net/20.500.14365/3562 |
ISBN: | 9.78142E+12 |
Appears in Collections: | PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
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