Assessing skin blood flow function in people with spinal cord injury using the time domain, time–frequency domain and deep learning approaches

Skin blood flow (SBF) has been assessed using the time domain and time–frequency domain methods. However, these methods require prior knowledge of selecting appropriate parameters for characterizing SBF responses. Deep learning has been successful on classification of medical images, and could be a promising tool for assessing SBF in various pathophysiological conditions. In this study, we proposed a deep learning-based framework for converting 1-dimensional time-series SBF into 2-dimensional time–frequency SBF for convolutional neural networks (CNNs). Thirty-seven participants were recruited into this study, including 21 people with spinal cord injury (SCI) and 16 healthy able-bodied controls. Laser Doppler flowmetry was used to measure sacral SBF. Continuous wavelet transform was used to obtain time–frequency representations of SBF. The whole frequency (WF, 0.0095–2 Hz), high frequency (HF, 0.138–2 Hz), and low frequency (LF, 0.0095–0.138 Hz) regions of the wavelet amplitudes were partitioned into the nonoverlapping patches. Four CNNs including AlexNet, Vgg-19, GoogLeNet, and ResNet-18 were employed to classify the patches. The results showed that the time-domain biphasic thermal index could not differentiate SBF in all groups. Time-frequency wavelet analysis showed differences in myogenic and cardiac controls between people with SCI who were active and sedentary (p < 0.01). CNNs results showed that all participants could be correctly classified based on the WF patches (100% of accuracy) compared to the HF (50–100%) and LF (66.7–100%) patches and five individual oscillation components (50–57.1%). Our study demonstrated that the classifiers could detect subtle changes in SBF function that cannot be revealed by the traditional methods.