Multiscale based nonlinear dynamics analysis of heart rate variability signals

被引:1
|
作者
Kazmi, Syed Zaki Hassan [1 ]
Habib, Nazneen [2 ]
Riaz, Rabia [1 ]
Rizvi, Sanam Shahla [3 ]
Abbas, Syed Ali [1 ]
Chung, Tae-Sun [4 ]
机构
[1] Univ Azad Jammu & Kashmir, Dept Comp Sci & Informat Technol, Muzaffarabad, Pakistan
[2] Univ Azad Jammu & Kashmir, Dept Sociol & Rural Dev, Muzaffarabad, Pakistan
[3] Raptor Interact Pty Ltd, Centurion, South Africa
[4] Ajou Univ, Dept Software, Suwon, South Korea
来源
PLOS ONE | 2020年 / 15卷 / 12期
基金
新加坡国家研究基金会;
关键词
TIME-SERIES; ENTROPY; HEALTHY; INDEX;
D O I
10.1371/journal.pone.0243441
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Acceleration change index (ACI) is a fast and easy to understand heart rate variability (HRV) analysis approach used for assessing cardiac autonomic control of the nervous systems. The cardiac autonomic control of the nervous system is an example of highly integrated systems operating at multiple time scales. Traditional single scale based ACI did not take into account multiple time scales and has limited capability to classify normal and pathological subjects. In this study, a novel approach multiscale ACI (MACI) is proposed by incorporating multiple time scales for improving the classification ability of ACI. We evaluated the performance of MACI for classifying, normal sinus rhythm (NSR), congestive heart failure (CHF) and atrial fibrillation subjects. The findings reveal that MACI provided better classification between healthy and pathological subjects compared to ACI. We also compared MACI with other scale-based techniques such as multiscale entropy, multiscale permutation entropy (MPE), multiscale normalized corrected Shannon entropy (MNCSE) and multiscale permutation entropy (IMPE). The preliminary results show that MACI values are more stable and reliable than IMPE and MNCSE. The results show that MACI based features lead to higher classification accuracy.
引用
收藏
页数:14
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