Toward IoT-Based Medical Edge Devices: PPG-Based Blood Pressure Estimation Application
Denis Bernard, Constantino Msigwa, Jaeseok Yun
Abstract
The Internet of Things (IoT) now provide a considerable advantage in terms of intelligent devices. For example, sensor data of medical equipment could be used to extract meaningful insights to enable a range of tasks to be performed intelligently at the device location, such as blood pressure (BP) prediction several minutes ahead. In this article, we propose a computing device called Medical Edge, designed to convert conventional medical equipment into IoT-enabled devices. We developed an interworking proxy that allows Medical Edge to be interfaced with medical devices, such as patient monitors (e.g., GE CARESCAPE B650), to smoothly collect physiological data and upload them to an IoT server platform. We used a oneM2M standard-based server platform to provide access to these data in a standardized manner. To demonstrate a promising application of our proposed Medical Edges, we performed a study on BP estimation based on photoplethysmography (PPG) signal only. We propose a hybrid neural network architecture and apply it with a publicly available data set called multiparameter intelligent monitoring in intensive care II (MIMIC II). The model consists of five 1-D convolutional neural networks (CNNs), three Bi-directional long short-term memory networks, and four fully connected layers. The mean absolute error (MAE) and standard deviation (STD) of the proposed model were, respectively, 0.95 and 1.44 millimetres of mercury (mmHg) for diastolic BP (DBP), and 1.38 and 2.13 mmHg for systolic BP (SBP). The experimental results are in full compliance with the international standards of the Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS).