An intelligent learning approach for improving ECG signal classification and arrhythmia analysis
An intelligent learning approach for improving ECG signal classification and arrhythmia analysis
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DOI：
10.1016/j.artmed.2019.101788
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发表时间：
2020-03-01
影响因子：
7.5
通讯作者：
Bian, Gui-Bin
中科院分区：
文献类型：
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作者：
Sangaiah, Arun Kumar;Arumugam, Maheswari;Bian, Gui-Bin
在最短的时间内识别心律失常对防止猝死和过早死亡很重要。拟议的工作包括一个完整的框架，用于分析心电图（ECG）信号。分析的三个阶段包括：1）通过专用滤波器组合的噪声抑制来增强ECG信号质量; 2）通过专用小波设计进行特征提取，以及3）用于心律失常分类为正常（N）、右束分支块（RBBB）、左束分支块（LBBB）的所提出的隐马尔可夫模型（HMM），室性早搏（PVC）和房性早搏（APC）。在拟议的工作中提取的主要特征是最小值，最大值，平均值，标准差和中位数。实验在MIT BIH心律失常数据库和MIT BIN噪声应激测试数据库中的45个ECG记录上进行。该模型的总体准确率为99.7%，灵敏度为99.7%，阳性预测值为100%。该模型的检测错误率为0.0004。本文还包括使用IoMT（医疗物联网）方法进行心律失常识别的研究。
The recognition of cardiac arrhythmia in minimal time is important to prevent sudden and untimely deaths. The proposed work includes a complete framework for analyzing the Electrocardiogram (ECG) signal. The three phases of analysis include 1) the ECG signal quality enhancement through noise suppression by a dedicated filter combination; 2) the feature extraction by a devoted wavelet design and 3) a proposed hidden Markov model (HMM) for cardiac arrhythmia classification into Normal (N), Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Premature Ventricular Contraction (PVC) and Atrial Premature Contraction (APC). The main features extracted in the proposed work are minimum, maximum, mean, standard deviation, and median. The experiments were conducted on forty-five ECG records in MIT BIH arrhythmia database and in MIT BIN noise stress test database. The proposed model has an overall accuracy of 99.7 % with a sensitivity of 99.7 % and a positive predictive value of 100 %. The detection error rate for the proposed model is 0.0004. This paper also includes a study of the cardiac arrhythmia recognition using an IoMT (Internet of Medical Things) approach.