基于ECG的可电击复律心律自动判别算法研究

doi:10.3969/j.issn.0258-8021.2023.05.007

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Abstract Automatic external defibrillator (AED) is an important device in saving patients with cardiac arrest (SCA). Shockable advice algorithm (SAA) is the key technology of AED. In this work, our model was trained with a data set including 2 024 segments of shockable rhythms (SHR) and 7 884 segments of non-shockable rhythms (NSHR). We proposed a SAA based on machine learning. Combining 6 effective features selected from 32 features such as time domain, frequency domain and complexity, support vector machine was employed to classify SHR and NSHR. After 500 experiments, the mean value ±standard deviation of sensitivity of the algorithm was 97.62±0.18%, the specificity was 99.15±0.04%, and the accuracy was 98.79±0.08%. The results showed that the SAA proposed in this paper met the requirements of American Heart Association for SAA performance in AED, and it can be used as an AED algorithm module for automatic discrimination of SHR.

Key words： shockable advice algorithm ECG machine learning feature extraction feature selection

Received: 22 February 2022

PACS:

R318

Corresponding Authors: ^*E-mail: xiaomeiwu@fudan.edu.cn

About author:: ^#Member, Chinese Society of Biomedical Engineering

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	Zheng Yue
	Hou Xingyu
	Wu Xiaomei

Cite this article:

Zheng Yue,Hou Xingyu,Wu Xiaomei. Research on Shockable Rhythm Detection Algorithm Based on Machine Learning[J]. Chinese Journal of Biomedical Engineering, 2023, 42(5): 572-582.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2023.05.007 OR http://cjbme.csbme.org/EN/Y2023/V42/I5/572

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