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Ensemble empirical mode decomposition-based optimised power line interference removal algorithm for electrocardiogram signal

机译：基于集合经验模式分解的优化心电信号电力线干扰消除算法

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摘要

This study proposes an optimised algorithm to remove power line interference (PLI) from electrocardiogram (ECG) signal based on ensemble empirical mode decomposition (EEMD). A computationally efficient algorithm is one of the important requirements for real-time monitoring of cardio activities and diagnosis of arrhythmias. Computational complexity in EEMD is significantly reduced by using the EMD as the preprocessing stage. The noisy ECG signal is decomposed into intrinsic mode functions (IMFs) using EMD. ECG signals which are affected by PLI are automatically identified based on the simple ratio of the zero crossing number of IMF components. EEMD is used to decompose only ECG segments constructed from the noisy IMF components. The proposed algorithm is evaluated by real ECG signals available in MIT-BIH arrhythmia database in terms of signal-to-noise ratio and root mean square error. The computational efficiency of this new framework is measured using MATLAB profiling functions and compared with EMD, EEMD, sign-based adaptive and EMD with wavelet-based methods. Results show that the proposed algorithm performs better than the EMD, EEMD, sign-based adaptive and EMD with wavelet-based methods and it is computationally more efficient than EMD and EEMD methods.
机译：本研究提出了一种基于整体经验模态分解（EEMD）的从心电图（ECG）信号中消除电力线干扰（PLI）的优化算法。计算效率高的算法是实时监测心脏活动和诊断心律不齐的重要要求之一。通过将EMD用作预处理阶段，可以大大降低EEMD中的计算复杂性。使用EMD将嘈杂的ECG信号分解为固有模式函数（IMF）。根据IMF分量的零交叉数的简单比率，自动识别受PLI影响的ECG信号。 EEMD仅用于分解由嘈杂的IMF组件构成的ECG段。 MIT-BIH心律失常数据库中可用的真实ECG信号根据信噪比和均方根误差对算法进行了评估。该新框架的计算效率使用MATLAB配置文件功能进行了测量，并与EMD，EEMD，基于符号的自适应和基于小波的EMD进行了比较。结果表明，该算法的性能优于基于小波的EMD，EEMD，基于符号的自适应和EMD，并且在计算上比EMD和EEMD更有效。

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