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一种基于总体经验模态分解的1H-MRS信号基线校正新方法。

A novel approach for baseline correction in 1H-MRS signals based on ensemble empirical mode decomposition.

作者信息

Parto Dezfouli Mohammad Ali, Dezfouli Mohsen Parto, Rad Hamidreza Saligheh

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:3196-9. doi: 10.1109/EMBC.2014.6944302.

Abstract

Proton magnetic resonance spectroscopy ((1)H-MRS) is a non-invasive diagnostic tool for measuring biochemical changes in the human body. Acquired (1)H-MRS signals may be corrupted due to a wideband baseline signal generated by macromolecules. Recently, several methods have been developed for the correction of such baseline signals, however most of them are not able to estimate baseline in complex overlapped signal. In this study, a novel automatic baseline correction method is proposed for (1)H-MRS spectra based on ensemble empirical mode decomposition (EEMD). This investigation was applied on both the simulated data and the in-vivo (1)H-MRS of human brain signals. Results justify the efficiency of the proposed method to remove the baseline from (1)H-MRS signals.

摘要

质子磁共振波谱法((1)H-MRS)是一种用于测量人体生化变化的非侵入性诊断工具。采集到的(1)H-MRS信号可能会因大分子产生的宽带基线信号而受到干扰。近年来,已经开发了几种方法来校正这种基线信号,然而,其中大多数方法无法在复杂的重叠信号中估计基线。在本研究中,提出了一种基于总体经验模态分解(EEMD)的新型(1)H-MRS谱自动基线校正方法。该研究应用于模拟数据和人脑信号的体内(1)H-MRS。结果证明了所提出方法从(1)H-MRS信号中去除基线的有效性。

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