Prediction of paroxysmal atrial fibrillation based on heart rate variability analysis
10.3969/j.issn.1005-202X.2024.05.008
- VernacularTitle:基于心率变异性的阵发性心房颤动预测方法
- Author:
Xiaodong NIU
1
,
2
;
Guoqiang CHAI
;
Dawei WANG
;
Lirong LU
;
Lingna HAN
;
Yajun LIAN
Author Information
1. 长治医学院生物医学工程系,山西长治 046000
2. 长治医学院山西省智能数据辅助诊疗工程研究中心,山西长治 046000
- Keywords:
paroxysmal atrial fibrillation;
heart rate variability;
entropy;
scale;
integral mean mode decomposition
- From:
Chinese Journal of Medical Physics
2024;41(5):579-587
- CountryChina
- Language:Chinese
-
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.
