Design of a CNN1D for Control and Monitoring of Blood Pressure

The ability to continuously monitor the parameters associated with the state of health of an individual results in a large volume of data, which presents both challenges and opportunities for data analysis. There are several studies of convolutional neural networks (CNN) used successfully in the implementation of tools for the monitoring and analysis of a large amount of medical data, accomplishing faster and more accurate data processing for the forecasting of health-related variables. This paper will address the design of a predictive model of ECG and PPG signals to estimate blood pressure (BP) with an one dimensional convolutional neural network (CNN1D) that optimizes performance to reduce the amount of processing on devices.