A k-nearest neighbors classifier using wavelet transform methods achieved about 72% performance in predicting paroxysmal atrial fibrillation episodes 12.5 minutes before occurrence.
Do wavelet transform methods and k-nearest neighbors classifier on heart rate variability data predict paroxysmal atrial fibrillation episodes before they occur?
Wavelet transform methods applied to heart rate variability data with a k-nn classifier can predict paroxysmal atrial fibrillation episodes approximately 12.5 minutes before onset with 72% performance.
Paroxysmal Atrial fibrillation is one of the most common complaints of heart disorders that occur as a result of random vibrations of the atria. PAF episode show a serious increase with age, and the next steps are more difficult especially for the elderly. So, diagnosing in the early stages of this disorder is very important for the PAF patients to stop the progression of the disease and to improve the quality of life. For his reason, in this studyitisaimedtobedetectedwhichin5minutesbeforethePAF episodes. The 30-minute data is divided into 10 parts in 5 minutes with 50% overlap. For each part, wavelet transform methods and wavelet entropy are calculated over heart rate variability data. Using these measurements, it is determined whether there is a statistically significant difference between the parts and the early detection performance of PAF was obtained using the k-nearest neighbors classifier. As a result, PAF episode can be statistically distinguished before it occurs and it is determined that the k-nn classifier has about 72% performance 12.5 minutes earlier than a PAF episode.
Narin et al. (Fri,) conducted a other in Paroxysmal Atrial Fibrillation. Wavelet transform methods and k-nearest neighbors classifier was evaluated on Early detection performance of PAF. A k-nearest neighbors classifier using wavelet transform methods achieved about 72% performance in predicting paroxysmal atrial fibrillation episodes 12.5 minutes before occurrence.