International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 04 | Apr 2022
p-ISSN: 2395-0072
www.irjet.net
IOT Based Anesthesia Parameters Monitoring with Doctor Decision Assistance using Machine Learning DR. RADHA R1, NAGA SAI RAM AELLA2 1Associate
Professor, School of computer science and engineering, Vellore Institute of Technology, Chennai, India student, School of computer science and engineering, Vellore Institute of Technology, Chennai, India ---------------------------------------------------------------------***--------------------------------------------------------------------2Undergraduate
Abstract - Anesthesia plays a very important role in the
possible to monitor every value every time for the doctor there are chances the parameter may get skipped.
surgery. In a long surgery anesthesia is given multiple times but not at a time to avoid the high dose which may affect the patient. If the dose is not given in time there is a chance where the patient will be conscious and the situation like these tends to panic. Even the anesthesia overdose may lead to deaths. There are some factors which should be noticed to the concerned anesthesia doctor before giving the anesthesia to the patient. The anesthesia doctor can get assistance with the help of advancements in Computer science by using IOT and ML. The raspberry pi used in this project will collect the data from the sensors and with the help of an in-built wi-fi module in the raspberry pi the data will be transmitted to the Thingspeak. Earlier this system was purely based on the Arduino for just collecting the data and automatic injector was used which is not safe for the patient as during surgery anesthesia is given at different parts of the body based on where the surgery is going to be done. So, It is not possible to use the automatic injectors for anesthesia. To overcome the dose fluctuations and to get assistance from the Machine learning algorithm to doctor which shows the risk prediction based on the parameters entered by the doctor. The Immediate message will be sent to the concerned staff if there is any fluctuation in the reading.
The data from the sensors and the data is transmitted to the thingspeak cloud and from the cloud the data from sensors is retrieved to the website and different visualizations are shown based on the par values of the parameters. Even, If the values are fluctuating below or above the threshold immediate message will be sent to the concerned doctor.
1.1 Objective The main objective of my project is to develop a IOT based anesthesia parameter monitoring machine and apply the algorithms for prediction and make it in Low cost and User Friendly. The device will also have safety features to ensure smooth operation at the patient’s bedside and assist the doctor. This procedure is easy, riskless and time saving for doctors, patients and staff.
1.2 Challenges The challenges faced while completing the project are discussed briefly here. First everything started from the decision of the domain. As, the project is in the health domain the main issue with these types of domains is that a lot of data is very restricted, closed and private. The case study to find the base paper took a lot of hours and even then, most of those are not open source and it struggled to get the required data from the limited sources.
Key Words: IOT, ML, wi-fi, Thingspeak, Raspberry pi, Anaesthesia assistant, Arduino, Logistic Regression.
1. INTRODUCTION While performing long duration surgeries the anaesthesia is given to the patient several times but it is not delivered at a time as it may lead to overdose. Overdose may even end up in patient deaths. As, It is given multiple times to the patient during surgery the doctor needs to visualize the parameters every time he needs to inject the anaesthesia.
Next, the main concern arises when we need to get the dataset for training the model. The anaesthesia parameter dataset in the internet is either limited or can state it is almost null available as open source. As Every hospital maintains the data of patients as private no where we can get the related data for the work. Anyway, after going through a lot of research papers there is an experiment performed by the University of Queensland on anaesthesia. They collected the data from 32 patients during the surgery. Finally, the dataset is taken from the experiment as the data is open sourced.
Not only overdose but low dose than required may make situations panic during the surgery. Here Computer Science fields like IOT and Machine learning logistic regression algorithm will help us to overcome the above with Indications when the parameter values go beyond or behind the par value and the machine learning algorithm predicts the risk based on the parameters. It is a hassle - free for the doctor as he gets assistance from the risk predictor. It is not
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Next, the selection of the sensors based on the parameters needed for the doctor to get assistance. The parameters that should be taken into consideration took a lot of time to
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