OBJECTIVE: Rhythmical changes in microvascular perfusion of the skin depend on various influences, which appear continuously but not in a predictable manner. For identifying and quantifying different physiological influences the authors used wavelet transformation, analyzing continuously and simultaneously measured data. METHODS: A total of 34 healthy volunteers were included in the study. At the dorsum of the left hand, skin perfusion was measured by laser Doppler fluxmetry (LDF) and skin temperature was measured. Simultaneously, the electrocardiogram and the respiration were recorded. The recorded time series were analyzed with wavelet transformation and scale correlation (S-correlation). RESULTS: Semilinear analysis with wavelet transformation allowed a visualization of temporal changes in LDF frequency and amplitude in a color-coded quasi three-dimensional diagram. The authors found that tissue perfusion over an observation period of 327.68 s is governed by 6 closely connected, overlying waves with different degrees of freedom. The major determinants are low frequencies in LDF, which correlates with changes in skin temperature, responsible for 68.5% of the influence. Surprisingly, though indispensable for blood flow, respiration and heartbeat contributed to less then 2.5% of the rhythmic changes. CONCLUSIONS: When wavelet transformation is used in analyzing LDF time series, the different rhythms of cutaneous blood flow are made visible and quantifiable and can be assigned to different physiological origins. The application of this novel analysis method allows identifying mechanisms regulating skin perfusion, which will greatly facilitate the diagnosis of a variety of important vascular diseases, such as chronic venous insufficiency, diabetes, or neurotrophic disorders.
OBJECTIVE: Rhythmical changes in microvascular perfusion of the skin depend on various influences, which appear continuously but not in a predictable manner. For identifying and quantifying different physiological influences the authors used wavelet transformation, analyzing continuously and simultaneously measured data. METHODS: A total of 34 healthy volunteers were included in the study. At the dorsum of the left hand, skin perfusion was measured by laser Doppler fluxmetry (LDF) and skin temperature was measured. Simultaneously, the electrocardiogram and the respiration were recorded. The recorded time series were analyzed with wavelet transformation and scale correlation (S-correlation). RESULTS: Semilinear analysis with wavelet transformation allowed a visualization of temporal changes in LDF frequency and amplitude in a color-coded quasi three-dimensional diagram. The authors found that tissue perfusion over an observation period of 327.68 s is governed by 6 closely connected, overlying waves with different degrees of freedom. The major determinants are low frequencies in LDF, which correlates with changes in skin temperature, responsible for 68.5% of the influence. Surprisingly, though indispensable for blood flow, respiration and heartbeat contributed to less then 2.5% of the rhythmic changes. CONCLUSIONS: When wavelet transformation is used in analyzing LDF time series, the different rhythms of cutaneous blood flow are made visible and quantifiable and can be assigned to different physiological origins. The application of this novel analysis method allows identifying mechanisms regulating skin perfusion, which will greatly facilitate the diagnosis of a variety of important vascular diseases, such as chronic venous insufficiency, diabetes, or neurotrophic disorders.