Identification of Paroxysmal Atrial Fibrillation Based on Integral Mean Mode Decompositionand Sample Entropy of Intrinsic Modal Functions
Lu Lirong1, Niu Xiaodong1,2, Wang Jian3, Zhang Xu4*
1(Department of Biomedical Engineering, Changzhi Medical College, Changzhi 046000, Shanxi, China) 2(Engineering Research Centre for Intelligent Data Assisted Diagnosis and Treatment in Shanxi Province, Changzhi 046000, Shanxi, China) 3(Key Laboratory of Information Detection and Processing, North University of China, Taiyuan 030051, China) 4(School of Biomedical Engineering,Capital Medical University, Beijing 100069, China)
Abstract：In order to solve the problem that the short duration of paroxysmal atrial fibrillation (PAF) can easily lead to false detection and missed detection, an identification algorithm based on integral mean mode decomposition (IMMD) and sample entropy of intrinsic mode function (IMFSE) was proposed in this paper. In this work, heart rate variability (HRV) signal fragments with a duration of 20 minutes were subjected to IMMD to obtain a series of intrinsic mode functions (IMFs). Then, the IMFSE was calculated, and next, the feature of PAF identification was extracted by statistical analysis of the IMFSE results. Finally, PAF identification was achieved by support vector machine and cross-validation. The PAF Prediction Challenge Database (AFPDB) provides ECG signals of normal subjects, patients with PAF attacks and patients far away from PAF attacks. From these signals, 25 HRV signal segments with a duration of 20 minutes were obtained, which constituted normal group, PAF attack group and PAF non-attack group. The performance of the proposed method in identification PAF episodes was evaluated with these 75 signals. Our proposed method obtained the values of 94%, 96% and 92% for the evaluation criteria of correct rate, sensitivity, and specificity, respectively. The experimental results showed that the PAF identification algorithm proposed in this paper provided a method basis for further automatic detection of PAF, and had a great application prospect in the long-term automatic detection and identification of PAF in wearable devices.
卢莉蓉, 牛晓东, 王鉴, 张旭. 基于积分均值模式分解和固有模态函数样本熵的阵发性房颤识别[J]. 中国生物医学工程学报, 2023, 42(6): 668-676.
Lu Lirong, Niu Xiaodong, Wang Jian, Zhang Xu. Identification of Paroxysmal Atrial Fibrillation Based on Integral Mean Mode Decompositionand Sample Entropy of Intrinsic Modal Functions. Chinese Journal of Biomedical Engineering, 2023, 42(6): 668-676.
[1] Nattel S. New ideas about atrial fibrillation 50 years on[J]. Nature, 2002, 415(6868): 219-226. [2] Zhao Lina, Li Jianqing, Wan Xiangkui, et al. Determination of parameters for an entropy-based atrial fibrillation detector[J]. Entropy, 2021, 23: 1199. [3] Almeida TP, Soriano DC, Mase M, et al. Unsupervised classification of atrial electrograms for electroanatomic mapping of human persistent atrial fibrillation[J]. IEEE Trans Biomed Eng, 2021, 68(4):1131-1141. [4] Wang Zengwu, Chen Zuo, Wang Xin, et al. The disease burden of atrial fibrillation in China from a national cross-sectional survey[J]. Am J Cardiol, 2018, 122(5):793-798. [5] 黄从新,张澍,黄德嘉,等. 心房颤动:目前的认识和治疗的建议-2018[J]. 中国心脏起搏与心电生理杂志,2018, 32(4): 315-368. [6] 陈煜,辛怡,王振宇,等. 基于多尺度小波熵的阵发性房颤识别方法[J]. 航天医学与医学工程,2013, 26(5): 352-355. [7] Rienstra M, Lubitz SA, Mahida S, et al. Symptoms and functional status of patients with atrial fibrillation: state of the art and future research opportunities[J]. Circulation, 2012, 125(23): 2933- 2943. [8] Asgari S, Mehrnia A, Moussavi M. Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine[J]. Comput Biol Med, 2015, 60: 132-142. [9] García M, Ródenas J, Alcaraz R, et al. Application of the relative wavelet energy to heart rate Independent detection of atrial fibrillation[J]. Comput Methods Programs Biomed, 2016, 131: 157-168. [10] 廖丽红,黄顺红,刘甲兴,等. 48 h长时程动态心电图检测在阵发性房颤中的应用[J]. 中西医结合心脑血管病杂志, 2016, 14(5): 535-537. [11] Andersen RS, Peimankar A, Puthusserypady S. A deep learning approach for real-time detection of atrial fibrillation[J]. Expert Syst Appl, 2019, 115: 465-473. [12] Petr?nas A, Sörnmo L, Lukoševi?ius A, et al. Detection of occult paroxysmal atrial fibrillation[J]. Med Biol Eng Comput, 2015, 53(4): 287-297. [13] 李陈璇,丁晓梅,朱红军. 心率变异性分析在心血管疾病中应用的研究进展[J]. 中西医结合心脑血管病杂志,2020, 18(1): 82-84. [14] 冯应君,杨汉东,闵新文,等. 自主神经功能异常与阵发性心房颤动的关系[J]. 陕西医学杂志,2006, 35(8): 937-937. [15] 李程,汤大侃,方勇,等. 心率变异性谱成分的非线性本质[J]. 物理学报, 2009, 58(2): 1348-1352. [16] 陈灵敏,吴晓羽. Lorenz 散点图在心律失常诊断中的应用[J] . 中华心律失常学杂志, 2020, 24 (1): 79-81. [17] 刘学森,张俊仕.基于多特征融合的阵发性房颤自动检测算法的研究[J]. 生物医学工程研究,2020, 39(4): 358-362. [18] Singh V, Gupta A, Sohal JS, et al. A unified non-linear approach based on recurrence quantification analysis and approximate entropy: application to the classification of heart rate variability of age-stratified subjects[J]. Med Biol Eng Comput, 2019, 57(3): 741-755. [19] Álvarez D, Sánchez-Fernández A, Andrés-Blanco AM, et al. Influence of chronic obstructive pulmonary disease and moderate-to-severe sleep apnoea in overnight cardiac autonomic modulation: time, frequency and non-linear analyses [J]. Entropy (Basel). 2019, 21(4): 381. [20] 万永利. 基于多尺度熵的常见心脏疾病特征研究[D]. 成都:电子科技大学, 2017. [21] Richman JS, Rmoorman J. Physiological time-series analysis using approximate entropy and sample entropy[J]. Am J Physiol Heart Circ Physiol, 2000, 278(6): H2039-H2049. [22] Meng Hu, Liang HL. Adaptive multiscale entropy analysis of multivariate neural data[J]. IEEE Trans Biomed Eng, 2012, 59(1): 12-15. [23] Chen Mingjing, He Aodi, Feng Kaicheng, et al. Empirical mode decomposition as a novel approach to study heart rate variability in congestive heart failure assessment[J]. Entropy, 2019, 21(12):1169. [24] 邵士亮,王挺,宋纯贺,等. 一种新的心率变异性度量方法[J]. 物理学报, 2019, 68(17): 178701. [25] 牛晓东,卢莉蓉,王鉴,等. 基于改进经验模态分解域内心动物理特征识别模式分量的心电信号重建[J]. 物理学报, 2021, 70(3): 311-319. [26] Moody GB, Goldberger AL, McClennen S, et al. Predicting the onset of paroxysmal atrial fibrillation: the computers in cardiology challenge 2001[J]. Computers in Cardiology 2001, 28: 113-116. [27] Niu Xiaodong, Lu Lirong, Wang Jian, et al. An improved empirical mode decomposition based on local integral mean and its application in signal processing[J]. Math Probl Eng, 2021, 2021: 8891217. [28] Camm AJ, Malik M, Bigger JT, et al. Heart rate variability. Standards of measurement, physiological interpretation, and clinical use[J]. Eur Heart J. 1996, 17(3):354-381. [29] Xin Yi, Zhao Yizhang. Paroxysmal atrial fibrillation recognition based on multi-scale wavelet α-entropy[J]. BioMed Eng Online, 2017, 16(1): 121. [30] Lake DE, Richman JS, Griffin MP, et al. Sample entropy analysis of neonatal heart rate variability[J]. American Journal of Physiology, 2002, 283(3): R789-R797. [31] 许冬秀,朴晶燕,郭宏,等. 阵发性房颤与自主神经功能失衡及其临床对策[J]. 中国实用内科杂志, 2003, 23(8): 496-496. [32] Hadase M, Azuma A, Zen K, et al. Very low frequency power of heart rate variability is a powerful predictor of clinical prognosis in patients with congestive heart failure[J]. Jpn Cir J, 2004, 68(4): 343-347. [33] 李静, 王步青, 王卫东. 两种计算正常成年人短时心率变异性方法的比较研究[J]. 北京生物医学工程, 2017, 36(5): 502-506. [34] 王步青, 柴晓珂, 张政波,等.心电图和光电容积脉搏波计算心率变异性的比较研究[J]. 中国医疗器械杂志, 2015, 39(4): 249-252, 264.
