A real-time cardiac output monitoring system based on photoplethysmography

Abstract

Abstract Cardiac output is a critical parameter for assessing the cardiovascular system, and thermodilution is commonly used as CO monitoring in clinical settings. Thermodilution cannot be applied in continuous monitoring due to invasion. Photoplethysmography signal originates from the cardiovascular system. We proposed a CO continuous monitoring system based on photoplethysmography and deep learning method. PPG waveform parameters are extracted during the pre-processing stage, and fed into the deep learning model to calculate cardiac output. The system employs an analog front-end chip and an HPS_FPGA architecture to capture PPG signals. The pre-processing and the deep learning model-driven algorithm are designed in Python on the custom Linux for HPS. It realizes the real-time processing of the PPG signals and the continuous monitoring of cardiac output. Validated by an FPGA, the system reported average cardiac output monitoring results of approximately 5.09 L/min at rest and about 7.39 L/min after exercise.