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设计一种用于高效心电图QRS波峰检测和心律失常数据分类的最优降阶滤波器。

Designing an Optimum and Reduced Order Filter for Efficient ECG QRS Peak Detection and Classification of Arrhythmia Data.

作者信息

Amhia Hemant, Wadhwani A K

机构信息

Electrical Engineering, MITS, Gwalior, MP, India.

出版信息

J Healthc Eng. 2021 Dec 22;2021:6542290. doi: 10.1155/2021/6542290. eCollection 2021.

DOI:10.1155/2021/6542290
PMID:39296538
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11410446/
Abstract

Electrocardiogram (ECG) is commonly used biological signals that show an important role in cardiac analysis. The interpretation and acquisition of QRS complex are significant measures of ECG data dispensation. The wave has a vital character in the analysis of cardiac rhythm irregularities as well as in the determination of heart rate variability (HRV). This manuscript is proposed to design a new artificial-intelligence-based approach of QRS peak detection and classification of the ECG data. The design of reduced order IIR filter is proposed for the low pass smoothening of the ECG signal data. The min-max optimization is used for optimizing the filter coefficient to design the reduced order filter. In this research paper, elimination of baseline wondering and the power line interferences from the ECG signal is of main attention. The result presented that the accuracy is increased by around 13% over the basic Pan-Tompkins method and around 8% over the existing FIR-filter-based classification rules.

摘要

心电图（ECG）是常用的生物信号，在心脏分析中发挥着重要作用。QRS波群的解读和采集是心电图数据处理的重要措施。T波在心律失常分析以及心率变异性（HRV）测定中具有重要特征。本文旨在设计一种基于人工智能的新型QRS波峰检测及心电图数据分类方法。提出设计降阶IIR滤波器用于心电图信号数据的低通平滑处理。采用最小-最大优化方法来优化滤波器系数以设计降阶滤波器。在本研究论文中，消除心电图信号中的基线漂移和电力线干扰是主要关注点。结果表明，与基本的潘-汤普金斯方法相比，准确率提高了约13%，与现有的基于FIR滤波器的分类规则相比提高了约8%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5138/11410446/7a5ca1520f3d/JHE2021-6542290.013.jpg
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An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm.一种自适应且高效的 ECG R 波峰检测算法。
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