Trends of Skin Blood Flow Response Signals for Early Pressure Ulcer Evaluation

Abstract

Pressure Ulcer (PU) or pressure sore is localized injury to the skin or underlying tissue or both that occur over a bony prominence when a patient is subjected to direct pressure for a lengthy period. Research have shown that skin blood flow response (SBFR) is a useful signal in PU evaluation due to its ability to determine ischemic stress response. In previous animal study, peak reactive hyperaemia (RH), a feature of SBFR has been measured for three loading-unloading cycles to indicate tissue recovery. However, in that study, peak SBFR feature cannot discriminate between tissue injury and tissue recovery effectively for short recovery time. In this study, the potential use of other SBFR signal features including time domain features i.e time to peak and frequency domain features i.e peak of power spectrum and total of power spectrum are investigated. The results show that for the samples taken, peak RH and area under the curve are both effective at discriminating between short of insufficient recovery time while other investigated features are ineffective.

Wavelet transform based features of skin blood flow response signal for pressure ulcer evaluation

“…Wavelet Transform was applied to extract wavelet coefficient. Continuous wavelet transform coefficient ̌ ( , ) of a time domain signal ( ) is calculated as [28][29][30][31][32][33]:…”

“…However, the number of samples exhibiting the increasing trend for short recovery time is relatively small (57% or 4/7 samples) to be considered as effective. Other than peak RH, trends of time to peak and area under the curve of hyperaemic response have also been investigated [10,28] and are summarized in Table 2 and Table 3 respectively. Area under curve is considered as a proportion of the need for metabolic reimbursement following tissue ischemia.…”

“…The result indicates that these features are not effective due to both trends not fulfilling the requirement of 72% or 5/7 samples simultaneously. In addition to time-domain analysis, RH has also been investigated using frequency domain analysis [28]. Maximum Peak PSD and Total PSD within frequency range of 0-10 Hz using Fast Fourier Transform (FFT) were computed [6,28].…”

“…In addition to time-domain analysis, RH has also been investigated using frequency domain analysis [28]. Maximum Peak PSD and Total PSD within frequency range of 0-10 Hz using Fast Fourier Transform (FFT) were computed [6,28]. The results also indicated that these features are not suitable at detecting sufficient and insufficient recovery time due to lower percentages of the trends compared to requirement as observed in Table 4 and 5.…”