OSA Patient Monitoring Based on the Beidou System

Author:

Liangming Cai,Xiaoqiong Cai,Min Du,Binxin Miao,Minfen Lin,Zhicheng Zeng,Shumin Li,Yuxin Ruan,Qiaolin Hu,Shuqin Yang

Abstract

This paper presents an OSA patient interactive monitoring system based on the Beidou system. This system allows OSA patients to get timely rescue when they become sleepy outside. Because the Beidou position marker has an interactive function, it can reduce the anxiety of the patient while waiting for the rescue. At the same time, if a friend helps the OSA patients to call the doctor, the friend can also report the patient's condition in time. This system uses the popular IoT framework. At the bottom is the data acquisition layer, which uses wearable sensors to collect vital signs from patients, with a focus on ECG and SpO2 signals. The middle layer is the network layer that transmits the collected physiological signals to the Beidou indicator using the Bluetooth Low Energy (BLE) protocol. The top layer is the application layer, and the application layer uses the mature rescue interactive platform of Beidou. The Beidou system was developed by China itself, the main coverage of the satellite is in Asia, and is equipped with a high-density ground-based augmentation system. Therefore, the Beidou model improves the positioning accuracy and is equipped with a special communication satellite, which increases the short message interaction function. Therefore, patients can report disease progression in time while waiting for a rescue. After our simulation test, the effectiveness of the OSA patient rescue monitoring system based on the Beidou system and the positioning accuracy of OSA patients have been greatly improved. Especially when OSA patients work outdoors, the cell phone base station signal coverage is relatively weak. The satellite signal is well-covered, plus the SMS function of the Beidou indicator. Therefore, the system can be used to provide timely patient progress and provide data support for the medical rescue team to provide a more accurate rescue plan. After a comparative trial, the rescue rate of OSA patients using the detection device of this system was increased by 15 percentage points compared with the rescue rate using only GPS satellite phones.

Publisher

Frontiers Media SA

Subject

Public Health, Environmental and Occupational Health

Reference36 articles.

1. Application of machine learning to predict obstructive sleep apnea syndrome severity;Mencar;Health Informatics J,2019

2. Learning from higher-layer feature visualizations;Nikolaidis;Preprints arXiv.,2019

3. RespNet: a deep learning model for extraction of respiration from photoplethysmogram;Ravichandran;Annu Int Conf IEEE Eng Med Biol Soc,2019

4. Automated sleep apnea detection in raw respiratory signals using long short-term memory neural networks;Van Steenkiste;IEEE J Biomed Health Inform,2019

5. System for obstructive sleep apnea events detection using convolutional neural network;Cen,2018

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