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

doi:10.3969/j.issn.0258-8021.2023.06.004

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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.

Key words： paroxysmal atrial fibrillation heart rate variability analysis integral mean mode decomposition sample entropy of intrinsic mode function support vector machine

Received: 15 December 2021

PACS:

R318

Corresponding Authors: ^*E-mail: zhangxu@ccmu.edu.cn

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	Lu Lirong
	Niu Xiaodong
	Wang Jian
	Zhang Xu

Cite this article:

Lu Lirong,Niu Xiaodong,Wang Jian, et al. Identification of Paroxysmal Atrial Fibrillation Based on Integral Mean Mode Decompositionand Sample Entropy of Intrinsic Modal Functions[J]. Chinese Journal of Biomedical Engineering, 2023, 42(6): 668-676.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2023.06.004 OR http://cjbme.csbme.org/EN/Y2023/V42/I6/668

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