Abstract

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Title:: Prediction of paroxysmal atrial fibrillation based on heart rate variability analysis

Author(s):: NIU Xiaodong1; 2; CHAI Guoqiang3; WANG Dawei3; LU Lirong1; HAN Lingna4; LIAN Yajun5; 1. Department of Biomedical Engineering, Changzhi Medical College, Changzhi 046000, China 2. Shanxi Engineering Research Center for Intelligent Data Assisted Diagnosis and Treatment, Changzhi Medical College, Changzhi 046000, China 3. School of Physics and Information Engineering, Shanxi Normal University, Taiyuan 030000, China 4. Department of Physiology, Changzhi Medical College, Changzhi 046000, China 5. Department of General Practice, Heping Hospital Affiliated to Changzhi Medical College, Changzhi 046000, China

Keywords:: Keywords: paroxysmal atrial fibrillation heart rate variability entropy scale integral mean mode decomposition

Abstract:: Abstract: Based on the analysis of heart rate variability (HRV), a prediction method for paroxysmal atrial fibrillation (PAF) attacks is proposed. A new adaptive filtering technique is used for smoothing and coarse graining of HRV, followed by entropy-based quantification of HRV complexity at multiple adaptive scales. After the features are normalized by Min-Max, feature subsets are selected by sequential forward selection method, and then input to support vector machine to identify HRV types and predict PAF attacks. Through 5-fold cross-validation on a set of 50 HRV sequences each lasting 5 minutes, the optimal prediction results are obtained: 98% accuracy, 100% sensitivity, 96% specificity, demonstrating excellent performance. In addition, the experiment shows significant changes (P<0.05) in the complexity eigenvalues of HRV far away from and close to PAF at different frequency bands, reflecting alterations in nervous system regulation of cardiac rhythm and a decline in the ability to adapt to external environmental changes such as stress regulation.