Analysis of biomedical time signals for characterization of cutaneous diabetic micro-angiopathy

Jens Kraitl; Hartmut Ewald

doi:10.1117/12.702468

14 February 2007 Analysis of biomedical time signals for characterization of cutaneous diabetic micro-angiopathy

Jens Kraitl, Hartmut Ewald

Proceedings Volume 6445, Optical Diagnostics and Sensing VII; 64450T (2007) https://doi.org/10.1117/12.702468
Event: SPIE BiOS, 2007, San Jose, California, United States

Abstract

Photo-plethysmography (PPG) is frequently used in research on microcirculation of blood. It is a non-invasive procedure and takes minimal time to be carried out. Usually PPG time series are analyzed by conventional linear methods, mainly Fourier analysis. These methods may not be optimal for the investigation of nonlinear effects of the hearth circulation system like vasomotion, autoregulation, thermoregulation, breathing, heartbeat and vessels. The wavelet analysis of the PPG time series is a specific, sensitive nonlinear method for the in vivo identification of hearth circulation patterns and human health status. This nonlinear analysis of PPG signals provides additional information which cannot be detected using conventional approaches. The wavelet analysis has been used to study healthy subjects and to characterize the health status of patients with a functional cutaneous microangiopathy which was associated with diabetic neuropathy. The non-invasive in vivo method is based on the radiation of monochromatic light through an area of skin on the finger. A Photometrical Measurement Device (PMD) has been developed. The PMD is suitable for non-invasive continuous online monitoring of one or more biologic constituent values and blood circulation patterns.

Citation Download Citation

Jens Kraitl and Hartmut Ewald "Analysis of biomedical time signals for characterization of cutaneous diabetic micro-angiopathy", Proc. SPIE 6445, Optical Diagnostics and Sensing VII, 64450T (14 February 2007); https://doi.org/10.1117/12.702468

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KEYWORDS

Wavelets

Heart

Continuous wavelet transforms

Control systems

Signal detection

Digital signal processing

Measurement devices

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