Atrial fibrillation classification and association between the natural frequency and the autonomic nervous system. (1st November 2016) - Abdul-Kadir, Nurul Ashikin; Mat Safri, Norlaili; Othman, Mohd Afzan
Abstract: Background: The feasibility study of the natural frequency ( ω ) obtained from a second-order dynamic system applied to an ECG signal was discovered recently. The heart rate for different ECG signals generates different ω values. The heart rate variability (HRV) and autonomic nervous system (ANS) have an association to represent cardiovascular variations for each individual. This study further analyzed the ω for different ECG signals with HRV for atrial fibrillation classification. Methods: This study used the MIT-BIH Normal Sinus Rhythm ( nsrdb ) and MIT-BIH Atrial Fibrillation ( afdb ) databases for healthy human (NSR) and atrial fibrillation patient (N and AF) ECG signals, respectively. The extraction of features was based on the dynamic system concept to determine the ω of the ECG signals. There were 35, 031 samples used for classification. Results: There were significant differences between the N & NSR, N & AF, and NSR & AF groups as determined by the statistical t -test ( p < 0.0001). There was a linear separation at 0.4 s − 1 for ω of both databases upon using the thresholding method. The feature ω for afdb and nsrdb falls within the high frequency (HF) and above the HF band, respectively. The feature classification between the nsrdb and afdb ECG signals was 96.53% accurate. Conclusions: This study found that features of the ω of atrial fibrillation patients and healthy humans were associated with the frequency analysis of the ANS during parasympathetic activity. The feature ω is significant for different databases, and the classification between afdb and nsrdb was determined.