Background: Patients with atrial fibrillation (AF) often exhibit abnormalities of P wave morphology during sinus rhythm. We examined a novel method for automatic P wave analysis in the 24-hour-Holter-ECG of 60 patients with paroxysmal or persistent AF and 12 healthy subjects.
Methods: Recorded ECG signals were transferred to the analysis program where 5-10 P and R waves were manually marked. A wavelet transform performed a time-frequency decomposition to train neural networks. Afterwards, the detected P waves were described using a Gauss function optimized to fit the individual morphology and providing amplitude and duration at half P wave height.
Results: >96% of P waves were detected, 47.4 +/- 20.7% successfully analyzed afterwards. In the patient population, the mean amplitude was 0.073 +/- 0.028 mV (mean variance 0.020 +/- 0.008 mV(2)), the mean duration at half height 23.5 +/- 2.7 ms (mean variance 4.2 +/- 1.6 ms(2)). In the control group, the mean amplitude (0.105 +/- 0.020 ms) was significantly higher (P < 0.0005), the mean variance of duration at half height (2.9 +/- 0.6 ms(2)) significantly lower (P < 0.0085).
Conclusions: This method shows promise for identification of triggering factors of AF.