Abstract
Heart rate variations reveal current or impending heart/cardiac diseases. A non-stationary signal — heart rate — is measured using Electrocardiogram (ECG) to assess cardiac Arrhythmia. But studying ECG reports is both tedious and time consuming when you have to locate abnormalities from the collected data. This is overcome by computer based analytical tools for in-depth data study/classification for diagnosis. Automatic arrhythmia assessment is easy due to the existence of image processing techniques. Many algorithms exist for ECG signals detection/classification. This paper investigates RR interval based ECG classification procedures for arrhythmic beat classification, a process based on RR interval beat extraction using Symlet on ECG data. Extracted RR data is used as a classification feature with beats being classified through a boosting algorithm and Fuzzy Unordered Rule Induction Algorithm (FURIA). Classification efficiency evaluation was through the MITBIH arrhythmia database.
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