基于极限学习机的室性早搏判别算法的实现

doi:10.3969/j.issn.0258-8021. 2017. 02.005

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Abstract Premature ventricular contraction (PVC) is a common heart rhythm disorders, which threatens humanity’s health, therefore accurate diagnosis of abnormal heart rhythms plays an important role to help humanity prevent cardiovascular disease. This paper proposed a diagnosis method based on ELM (extreme learning machine, ELM) to realize the discrimination of PVC from normal ECG (electrocardiograph) using the data from the MIT-BIH database as analysis object, and process of the method includes signal preprocessing, feature extraction and classification. The first step was to apply the wavelet transform combined with morphological filtering method for signal preprocessing to get the relatively clean signal, Then extracted feature parameters of QRS complex by using K-means clustering algorithm. Meanwhile, the calibration samples and prediction samples were established according to the feature parameters, and finally the ELM classifier for sample training match and classification recognition was adopted. 1260 cycles of signal were chosen to do experiment, and the results demonstrated that this algorithm could accurately diagnose the PVC, whose positive detection rate was up to 95% and sensitivity was up to 96% on average. Compared with other algorithms in the condition of similar detection accuracy, this algorithm can improve the real-time performance of the algorithm, which has high research value and certain practical value in mobile medical treatment and clinical medical treatment.

Key words： extreme learning machine premature ventricular contraction ECG MIT-BIH

Received: 26 May 2016

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R318

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	Articles by authors
	Wang Ruirong
	Yu Xiaoqing
	Wang Min
	Ye Yang

Cite this article:

Wang Ruirong,Yu Xiaoqing,Wang Min, et al. Implementation of the Algorithm for Premature Ventricular Contraction Discrimination Based on Extreme Learning Machine[J]. Chinese Journal of Biomedical Engineering, 2017, 36(2): 158-164.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021. 2017. 02.005 OR http://cjbme.csbme.org/EN/Y2017/V36/I2/158

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