Key result
An artificial neural network utilizing RR intervals from ECG time domain analysis was developed to categorize arrhythmias.
Artificial neural networks using RR intervals can be applied as a computational approach for classifying arrhythmias from ECG signals.
Hypothesis-generating for arrhythmia classification from RR intervals; prospective validation needed before clinical adoption.
The ECG signal is formed of the P wave, the QRS complex and the T wave. The P wave appears as a result of QRS complex ventricle contraction because of electrical stimulation initiation at the sinoatrial node and spreading in the cardiac muscles. The T wave appears as a result of ventricle relaxation. In this study the ECG signal is analyzed in the time domain. For this purpose, arrhythmia are determined by taking related mean time intervals of ECG as feature extraction. Arrhythmia are categorized by applying time elapsed between two R waves (RR intervals), appearing in ECG signals, as the input for artificial neural networks.
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Karhk et al. (2002) studied Arrhythmia. Artificial neural networks using RR intervals was evaluated on Arrhythmia categorization. An artificial neural network utilizing RR intervals from ECG time domain analysis was developed to categorize arrhythmias.
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