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首页> 外文会议>Instrumentation and Measurement Technology Conference (I2MTC), 2012 IEEE International >Use of multi scale PCA for extraction of respiratory activity from photoplethysmographic signals
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Use of multi scale PCA for extraction of respiratory activity from photoplethysmographic signals

机译：多尺度PCA从光体积描记图中提取呼吸活动的用途

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摘要

The fact that the photoplethysmographic (PPG) signal caries respiratory information in addition to arterial blood oxygen saturation attracted the researchers to extract the respiratory information from it. In this current work, we present an efficient algorithm, based on the multi scale principal component analysis (MSPCA) technique to extract the respiratory activity from the PPG signals. MSPCA is a powerful combination of wavelets and principal component analysis (PCA). In MSPCA technique, PCA is used in computing coefficients of wavelet at each scale, and finally combining all the results at relevant scales. Experiments carried on the data records drawn from the MIMIC database of Physionet archives revealed a very high degree of coherence between the PPG derived respiratory (PDR) signal and the recorded respiratory signal. Results demonstrated that MSPCA performed exceptionally well for extraction of respiratory activity from PPG signals with high correlation coefficient and accuracy rates of above 98%.
机译：除了动脉血氧饱和度外，光电容积描记器（PPG）信号还带有龋齿呼吸信息，这一事实吸引了研究人员从中提取呼吸信息。在当前的工作中，我们提出了一种基于多尺度主成分分析（MSPCA）技术的有效算法，可以从PPG信号中提取呼吸活动。 MSPCA是小波和主成分分析（PCA）的强大组合。在MSPCA技术中，PCA用于计算每个尺度上的小波系数，并最终将所有结果组合在相关尺度上。从Physionet档案的MIMIC数据库中提取的数据记录进行的实验表明，PPG派生的呼吸（PDR）信号与记录的呼吸信号之间具有很高的连贯性。结果表明，MSPCA在从PPG信号提取呼吸活动方面表现出色，具有较高的相关系数，准确率超过98％。

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