A Fast FPGA Hardware Accelerator for Remote Heart Rate Detection Based on RGB Vision.

A fast hardware accelerator is created by this work via field programmable gate array (FPGA) to estimate heart rate (HR) through the video recorded by a RGB camera based on the technology of remote photoplethysmography (rPPG). The method of rPPG acquires physiological signals of a human body by analyzing the subtle color changes on the surface of the human skin. The hardware implementation of rPPG to estimate HR is proposed herein to aim for a much faster calculation speed than software for a number of applications, like heart failure pre-warning of an in-action athlete and drowsiness detection of a driver. In this accelerator, ICA (Independent Component Analysis) is used to recover the blood volume pulse from the raw signals of remote PPG, and then obtain the heart rate value. The architecture of the hardware circuit is described in Verilog HDL and verified by Quartus II, and also implemented in an Altera DE10-Standard FPGA board, which consists of image capture, heart rate algorithm and image display. A TRDB-D5M camera is utilized for image capture. Two experiments were conducted with image collecting duration of 16 seconds and 8 seconds respectively, and the commercial device Omron HEM-6111 was used as the golden value. The proposed system achieves an accuracy in (ME ± 1.96SD) of -0.76 ± 5.09 and -0.70 ± 8.71 bpm in the short periods of 16-second and 8-second versions, respectively, which outperforms all the reported prior works in combined computation time and accuracy.

Ratio of positive lymph nodes: The prognostic value in stage IV thyroid cancer.

To assess the prognostic value of lymph node ratio (LNR) in patients with stage IV thyroid cancer based on the Surveillance, Epidemiology, and End Results (SEER) database. A total of 4,940 eligible patients were included for the analysis. Kaplan-Meier survival analysis and Cox proportional hazard regression were used to reveal the effect of LNR on overall survival (OS) and disease specific survival (DSS). The optimal cut-off value of LNR for predicting OS and DSS was determined by the time-dependent Receiver Operating Characteristic analysis. By the univariate Cox proportional hazard regression, LNR was significantly associated with OS and DSS in patients with medullary thyroid cancer (MTC), papillary thyroid cancer and anaplastic thyroid cancer (all P < 0.05). With the optimal cut-off value, Kaplan-Meier analysis showed that MTC patients with LNR≥76.5% were significantly associated with poorer OS (log-rank test: P < 0.0001), and LNR≥40.7% were significantly associated with poorer DSS (log-rank test: P < 0.0001). LNR was an independent prognostic factor of poorer survival in MTC patients after adjusting for other variables by multivariable Cox analysis (OS: hazard ratio [HR] = 2.560, 95% confidence interval [CI] 1.690-3.879, P < 0.0001; DSS: HR=2.781, 95% CI 1.582-4.888, P = 0.0004). Our results demonstrated that LNR could predict clinical outcomes in patients with stage IV MTC, and 76.5% was the optimal cut-off value of LNR to predict OS. LNR, as a function of the nodes positive and the nodes examined, could provide suggestions on the postoperative prognosis of patients with stage IV MTC.