一种改进的隐马尔可夫模型QRS检测方法

2017 6th International Conference on Systems and Control (ICSC) Pub Date : 2017-05-01 DOI:10.1109/ICOSC.2017.7958696

M. Belkadi, A. Daamouche

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引用次数: 2

摘要

隐马尔可夫模型在语音识别中是非常有效的。基于机器状态，hmm结合贝叶斯概率和决策来将每个输出近似到适当的类别。在本文中，我们提出将hmm用于心电QRS检测。为了更好地区分QRS复杂度和噪声，我们选择了一组模型来表示QRS复杂度和噪声。对于MIT/BIH心律失常数据库的44510个节拍，我们实现了0.741%的错误率。

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An improved QRS detection method using Hidden Markov Models

Hidden Markov Models are very efficient in speech recognition. Based on machine states, HMMs combine Bayesian probability and decision making to approximate each output to its appropriate class. In this paper, we propose to use HMMs for ECG QRS detection. We select a set of models to represent QRS complex and noise aiming to a better discrimination between them. For a total of 44510 beats of the MIT/BIH arrhythmia database, we achieved 0.741% of error rate.

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来源期刊

2017 6th International Conference on Systems and Control (ICSC)

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