Modified ant colony clustering method in long-term electrocardiogram processing
Language English Country United States Media print
Document type Evaluation Study, Journal Article, Research Support, Non-U.S. Gov't
- MeSH
- Algorithms MeSH
- Biomimetics methods MeSH
- Behavior, Animal MeSH
- Diagnosis, Computer-Assisted methods MeSH
- Electrocardiography, Ambulatory methods MeSH
- Ants physiology MeSH
- Humans MeSH
- Signal Processing, Computer-Assisted * MeSH
- Reproducibility of Results MeSH
- Pattern Recognition, Automated methods MeSH
- Sensitivity and Specificity MeSH
- Cluster Analysis * MeSH
- Arrhythmias, Cardiac diagnosis physiopathology MeSH
- Heart Rate * MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Evaluation Study MeSH
- Research Support, Non-U.S. Gov't MeSH
The paper presents an application of a clustering technique inspired by ant colony metaheuristics. The paper addresses the problem of long-term (Holter) electrocardiogram data processing. Long-term recording produces a huge amount of biomedical data, which must be preprocessed prior to its presentation to the specialist. The paper also discusses relevant aspects improving the robustness, stability and convergence criteria of the method. The method is compared with well known clustering techniques (both classical and nature-inspired), first testing on the known dataset and finally applying them to the real ECG data records from the MIT-BIH database and outperforms the standard methods. Electrocardiogram data clustering can effectively reduce the amount of data presented to the cardiologist: cardiac arrhythmia and significant morphology changes in the ECG can be visually emphasized in a reasonable time. The final evaluation of the ECG recording must still be made by an expert.
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