胎児心磁図における独立成分分析とWiener filterを用いたノイズ除去

概要

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We measured fetal magnetocardiograms (fMCGs) to analyze variability in the heart rate of a fetus. The heart rate variability was calculated from the interval of the R wave peak of fMCGs. However, the fMCGs had a very weak signal that was buried in noise, and noise rejection was necessary. Independent component analysis (ICA) is a useful method of distinguishing signal and noise components. However, it is difficult to separate noise from the signal, when the measured data have a low signal-to-noise ratio. We propose a method that uses a Wiener filter for post processing in ICA. The Wiener filter can reduce the amount of noise present in a signal by comparison with estimates of the required noiseless signal. The required signal in this study was created by measuring the data. The proposed method was evaluated for 15 fMCGs. As a result, the accuracy of R wave peak detection was improved by using the Wiener filter for post processing in ICA.