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 net-works (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 dif-ferences 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 ac-curacy) 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.