spectrum, wavelets, IMF, etc. We found the intrinsic mode function (IMF) approach of Huang et al. (17) most appropriate. The practical computation can be done on an existing software which analyses the digital signals into intrinsic modes of oscillations, each mode has the character that the local oscillations about the modal curve has an average of zero. Each given signal has a finite number of modes: successive modes have fewer and fewer zero crossings; the last mode has no zero-crossing at all, and it represents a trend. A pulmonary arterial pulse pressure record typically has 8-18 intrinsic modes, with the total number of models depending largely on the total length of time. Each complete record is resolved into a complete set of intrinsic modes. Thus we can define an average signal, or mean signal as the sum of a certain number of last intrinsic modes depending on an arbitrarily chosen number of zero-crossings you may wish to allow. The total signal is the sum of the mean signal and a signal of oscillations about the mean. Figure 2 illustrates the process. Other features and details are given in Huang et al (18, 19).
The advantage of the intrinsic mode approach is that we can get a much better understanding of the mean signals, the oscillations about the mean signals, and arrhythmia. Such understanding of signals will allow us to study tissue remodeling as a dynamic process. We would like to distinguish tissue remodeling in response to slowly varying mean stresses, from the remodeling of tissues in response to the oscillations about the mean.
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