Applications of multifractal analysis to white matter structure changes on magnetic resonance imaging（MRI） have recently received increasing attentions. Although some progresses have been made, there is no evident study on applying multifractal analysis to evaluate the white matter structural changes on MRI for Alzheimer＇s disease（AD） research. In this paper, to explore multifractal analysis of white matter structural changes on 3D MRI volumes between normal aging and early AD, we not only extend the traditional box-counting multifractal analysis（BCMA） into the 3D case, but also propose a modified integer ratio based BCMA（IRBCMA） algorithm to compensate for the rigid division rule in BCMA. We verify multifractal characteristics in 3D white matter MRI volumes. In addition to the previously well studied multifractal feature,△α, we also demonstrated △ f as an alternative and effective multifractal feature to distinguish NC from AD subjects.Both △α and △ f are found to have strong positive correlation with the clinical MMSE scores with statistical significance.Moreover, the proposed IRBCMA can be an alternative and more accurate algorithm for 3D volume analysis. Our findings highlight the potential usefulness of multifractal analysis, which may contribute to clarify some aspects of the etiology of AD through detection of structural changes in white matter.
A series of experiments are conducted to confirm whether the vectors calculated for an early section of a continuous non-invasive fetal electrocardiogram （fECG） recording can be directly applied to subsequent sections in order to reduce the computation required for real-time monitoring. Our results suggest that it is generally feasible to apply the initial optimal maternal and fetal ECG combination vectors to extract the fECG and maternal ECG in subsequent recorded sections.
We collected 343 groups of abdominal electrocardiogram(ECG) data from 78 pregnant women and deleted the channels unable for experts to determine R-wave peaks from them; then, based on these filtered data, the statistics of position difference of corresponding R-wave peaks for different maternal ECG components from different points were studied. The resultant statistics showed the regularity that the position difference of corresponding maternal R-wave peaks between different abdominal points does not exceed the range of 30 ms. The regularity was also proved using the fECG data from MIT–BIH PhysioBank. Additionally, the paper applied the obtained regularity, the range of position differences of the corresponding maternal R-wave peaks, to accomplish the automatic detection of maternal R-wave peaks in the recorded all initial 343 groups of abdominal signals, including the ones with the largest fetal ECG components, and all 55 groups of ECG data from MIT–BIH PhysioBank, achieving the successful separation of the maternal ECGs.
