Beat-to-beat P-wave morphology as a predictor of paroxysmal atrial fibrillation. (November 2017) - Filos, Dimitrios; Chouvarda, Ioanna; Tachmatzidis, Dimitris; Vassilikos, Vassilios; Maglaveras, Nicos
Highlights: Systematic beat to beat analysis of the P-wave characteristics in vectorcardiography during sinus rhythm, following time domain and time-frequency domain analysis. Adaptive clustering method for the identification of multiple P-wave morphologies in beat to beat analysis. The percentage of P-waves that match a main P-wave morphology is higher in healthy subjects' ECG recordings, compared with patients prone to PAF. A distinct secondary P-wave morphology was detected in PAF patients. The combination of wavelet and morphological characteristics enables discrimination of PAF patients from healthy subjects. Abstract: Background and objectives: Atrial Fibrillation (AF) is the most common cardiac arrhythmia. The initiation and the perpetuation of AF is linked with phenomena of atrial remodeling, referring to the modification of the electrical and structural characteristics of the atrium. P-wave morphology analysis can reveal information regarding the propagation of the electrical activity on the atrial substrate. The purpose of this study is to investigate patterns on the P-wave morphology that may occur in patients with Paroxysmal AF (PAF) and which can be the basis for distinguishing between PAF and healthy subjects. Methods: Vectorcardiographic signals in the three orthogonal axes (X, Y and Z), of 3–5 min duration, were analyzed during SR. In total 29 PAF patients and 34 healthy volunteers were included in the analysis. These data were divided into two distinct datasets, one for the training and one for the testing of the proposed approach. The method is based on the identification of the dominant and the secondary P-wave morphology by combining adaptive k-means clustering of morphologies and a beat-to-beat cross correlation technique. The P-waves of the dominant morphology were further analyzed using wavelet transform whereas time domain characteristics were also extracted. Following a feature selection step, a SVM classifier was trained, for the discrimination of the PAF patients from the healthy subjects, while its accuracy was tested using the independent testing dataset. Results: In the cohort study, in both groups, the majority of the P-waves matched a main and a secondary morphology, while other morphologies were also present. The percentage of P-waves which simultaneously matched the main morphology in all three leads was lower in PAF patients (90.4 ± 7.8%) than in healthy subjects (95.5 ± 3.4%, p= 0.019). Three optimal scale bands were found and wavelet parameters were extracted which presented statistically significant differences between the two groups. Classification between the two groups was based on a feature selection process which highlighted 7 features, while an SVM classifier resulted a balanced accuracy equal to 93.75%. The results show the virtue of beat-to-beat analysis for PAF prediction. Conclusion: The difference in the percentage of the main P-wave-morphology and in the P-wave time-frequency characteristics suggests a higher electrical instability of the atrial substrate in patients with PAF and different conduction patterns in the atria.