A predictive model using recurrence plot features of heart rate variability signals achieved 96.55% sensitivity, 100% specificity, and 100% positive predictivity for predicting the onset of PAF.
Can a method based on heart rate variability recurrence plot features and SVM accurately predict the onset of paroxysmal atrial fibrillation?
An algorithm using HRV recurrence plot features and SVM classification can predict the onset of paroxysmal atrial fibrillation with high sensitivity and specificity.
This paper aims to propose an effective paroxysmal atrial fibrillation (PAF) predictor which is based on the analysis of the heart rate variability (HRV) signal. Predicting the onset of PAF, based on non-invasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic interventions and to minimize the risks for the patients. This method consists of four steps: Preprocessing, feature extraction, feature reduction, and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the HRV signal is extracted. In the next step, the recurrence plot (RP) of HRV signal is obtained and six features are extracted to characterize the basic patterns of the RP. These features consist of length of longest diagonal segments, average length of the diagonal lines, entropy, trapping time, length of longest vertical line, and recurrence trend. In the third step, these features are reduced to three features by the linear discriminant analysis (LDA) technique. Using LDA not only reduces the number of the input features, but also increases the classification accuracy by selecting the most discriminating features. Finally, a support vector machine-based classifier is used to classify the HRV signals. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database which consists of both 30-minutes ECG recordings end just prior to the onset of PAF and segments at least 45 min distant from any PAF events. The obtained sensitivity, specificity, and positive predictivity were 96.55%, 100%, and 100%, respectively.
Ghassemian et al. (Sat,) conducted a other in Paroxysmal atrial fibrillation. Recurrence plot of HRV signal with LDA and SVM classifier was evaluated on Prediction of PAF episodes. A predictive model using recurrence plot features of heart rate variability signals achieved 96.55% sensitivity, 100% specificity, and 100% positive predictivity for predicting the onset of PAF.