School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.
Artif Intell Med. 2020 Mar;103:101788. doi: 10.1016/j.artmed.2019.101788. Epub 2019 Dec 31.
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 BIH 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.
快速识别心律失常对于预防突发和意外死亡至关重要。本研究提出了一个完整的分析心电图（ECG）信号的框架。分析过程包括三个阶段：1）通过专用滤波器组合抑制噪声来增强 ECG 信号质量；2）通过专门设计的小波进行特征提取；3）提出了一种隐藏马尔可夫模型（HMM），用于将心律失常分类为正常（N）、右束支传导阻滞（RBBB）、左束支传导阻滞（LBBB）、室性早搏（PVC）和房性早搏（APC）。本研究中提取的主要特征包括最小值、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 BIH 噪声应激测试数据库中的四十五份心电图记录上进行。所提出的模型总体准确率为 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。本文还研究了使用物联网医疗（IoMT）方法进行心律失常识别。
