Key result
Wavelet transform with cubic spline interpolation effectively extracted five characteristic frequency components (metabolic, neurogenic, myogenic, respiratory, and cardiac) from blood flow signals.
Wavelet transform is an effective method for extracting and reconstructing specific physiological frequency components from peripheral blood flow signals.
Wavelet analysis extracts five LDF frequency components; leaves open clinical validation for microvascular assessment.
The objective of this paper is to propose a method for exacting the characteristic frequency components of blood flow signals based on wavelet transform. The wavelet transform technique, a time-frequency method with logarithmic frequency resolution, was used to analyze oscillations in human peripheral blood flow measured by laser Doppler flowmetry (LDF). In the frequency interval from 0.008 to 2.0 Hz, the LDF signal consists of components with five different characteristic frequenciesmetabolic (0.008-0.02Hz), neurogenic (0.02-0.06Hz), myogenic (0.06-0.15Hz), respiratory (0.15-0.4Hz) and cardiac (0.4-2.0Hz). The five frequency components were extracted in time domain and reconstructed using cubic spline interpolation in this study. The results showed that it was an effective way to extract each component of blood flow signals.
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Cui et al. (2013) studied this question. Wavelet transform technique was evaluated on Extraction of characteristic frequency components of blood flow signals. Wavelet transform with cubic spline interpolation effectively extracted five characteristic frequency components (metabolic, neurogenic, myogenic, respiratory, and cardiac) from blood flow signals.
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