Abstract
This paper proposes an adaptive learning control and monitoring of oxygen for patients with breathing complexities and respiratory diseases. By recording the oxygen saturation levels in real-time, this system uses an adaptive learning controller (ALC) to vary the oxygen delivered to the patient and maintains it in an optimum range. In the presented approach, the PID controller gain is tuned with the learning technique to provide improved response time and a proactive approach to oxygen control for the patient. A case study is performed by monitoring the time varying health vitals across different age groups to gain a better understanding of the relationship between these parameters for COVID-19 patients. This information is then used to improve the standard of care provided to patients and reducing the time to recovery. Results show that ALC controls the oxygen saturation within the target range of 90% to 94% SpO2, 77% and 80.1% of the time in patients of age groups 40-50 years old and 50-60 years old, respectively. It also had faster time to recovery to target SpO2 range when the concentration dropped rapidly or when the patient becomes hypoxic as compared to the manual control of the oxygen saturation by the healthcare staff.
Highlights
The start of the year 2020 introduced the globe to an unprecedented time of biological turmoil, the likes of which has not been seen since the black plague
A comparison between automatic and manual control is used to demonstrate the efficacy of adaptive learning controller for oxygen concentration in COVID-19 patients
It is observed that the automatic mode via adaptive learning controller (ALC) control is the better option as it allows the patients, for all age groups, to recover in less amount of time
Summary
The start of the year 2020 introduced the globe to an unprecedented time of biological turmoil, the likes of which has not been seen since the black plague. Health care providers monitor SpO2 and control the supply of oxygen to critical care patients by manually adjusting the supply of oxygen from the cylinder or source [18][19] This is inefficient, but is risky, prone to error and in cases of a high number of patients can lead to overloading of the staff and healthcare system. An adaptive learning control system is utilized to monitor and control the vital signs, i.e., SpO2, pulse rate and temperature of COVID-19 patients requiring critical care In such scenarios where patients’ condition is rapidly changing in response to the medical treatment or ventilation supportive care, it is risky as well as time consuming for hospital staff to continuously monitor their progress. This approach could improve the recovery time of patients, thereby reducing the load on hospitals
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