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Biological signals, such as intracardiac electrograms during atrial fibrillation (AF), can contain multiple periodic components or peaks. We propose a method for identifying individual periodic peak trains in signals containing multiple such periodic sequences. We use dominant frequency-based periodicity detection along with a graph search algorithm to identify the most dominant periodic activation...
Abnormal components in the QRS complex on the surface electrocardiogram have been used to predict sudden cardiac death in patients with heart disease. We propose a novel method to automate detection of abnormal peaks within the QRS complex. The approach involves identification of such peaks from consecutive unfiltered 10-beat QRS averages. A simulation using synthetic QRS peaks is conducted to assess...
A novel method for automatic detection of peaks corresponding to the periodic activations in complex periodic signals is proposed. The approach involves dominant frequency-based periodicity detection combined with a graph search algorithm to identify periodic activations or peaks of interest. The performance of the proposed method is demonstrated in human atrial fibrillation electrograms with simulated...
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