International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022
p-ISSN: 2395-0072
www.irjet.net
Pressure Prediction System in Lung Circuit using Deep Learning and Machine Learning Dr. Vinod Wadne, Onkar Wanve, Anjali Singh, Yash Hanabar, Siddhesh Wable UG Student, Department of Computer Engineering, JSPM’s Imperial College of Engineering & Research, Pune, India --------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - A massive number of patients infected with SARS-
response to the changes in the patient's breathing frequency. Mechanical ventilators are clinically controlled and operated by doctors and nurses who are trained to handle them.
CoV2 and Delta variant of COVID-19 have generated acute respiratory distress syndrome (ARDS) which needs intensive care, which includes mechanical ventilation. But due to the huge no of patients, the workload and stress on healthcare infrastructure and related personnel have grown exponentially. This has resulted in huge demand for innovation in the field of automated healthcare which can help reduce the stress on the current healthcare infrastructure. This work gives a solution for the issue of Pressure prediction in mechanical ventilation. The algorithm suggested by the researchers tries to predict the pressure in the respiratory circuit for various lung conditions. Prediction of pressure in the lungs is a type of sequence prediction problem. LSTM (Long Short-Term Memory) is the most efficient solution to the sequence prediction problem. Due to its ability to selectively remember patterns over the long term, LSTM has an edge over normal RNN. RNN is good for short-term patterns but for sequence prediction problems LSTM is preferred.
However, PID controllers have certain limitations. When it comes to a process that is integrated and has a large time delay, performance is poor. Small changes or deviations are not reflected easily. The purpose of the given system is to eliminate these limitations. Using the concepts of deep learning, these limitations can be almost eliminated and the system can work with greater efficiency. Given technology is budget-friendly for hospitals. The amount of manpower required to ventilate a single patient will be reduced, which can be an enormous benefit, especially in pandemic situations. According to the resource Virus Centre of Johns Hopkins Medicine, 2.2% out of total worldwide COVID-19 affected people are died due to the acute respiratory syndrome analyzed since November 2019. Ground Glass Opacity (GGO) has been observed that the COVID-19 variants especially delta one cause pneumonia in both the lungs. A large number of people infected with Delta and other variants have acute respiratory distress syndrome (ARDS) and they need highlevel medical facilities like invasive mechanical ventilation. The effect of COVID-19 variants on the immune system, ground-glass opacity, and the different neoplastic changes in the lungs due to SARS-CoV2 and other variants attacks is the key focused area.
Key Words: Machine Learning, RNN, LSTM, Pytorch, COVID19 Pandemic
1. INTRODUCTION Many diseases, such as pneumonia, heart failure, COVID-19, etc., lead to lung failure for many different reasons. A person who cannot breathe on his own or has difficulty breathing needs to be given some external support to help with breathing. This help is provided to the patient with the help of a mechanical ventilator, and once the patient is stable, they are weaned off the ventilator. A mechanical ventilator is a machine that helps pump oxygen into a person’s body through a tube that goes in the mouth and down to the windpipe. According to the patient's condition and needs, the doctor programs the ventilator to push air when the patient needs help.
2. LITERATURE SURVEY In this paper [1] author tries to suggest that it is convenient to use HMMs algorithm to predict the pressure of ventilators in sedated patients and to results that the given threshold value of asynchrony events has such a probability. Unlike other studies based on very limited observation periods in patients with specific conditions, authors analyzed the whole period of mechanical ventilation in a different wide range of the population of ICU patients with a different variety of critical illnesses.
Mechanical ventilators use a PID (proportional-integralderivative) control algorithm to automatically adjust the oxygen concentration in the patient according to the patient's requirements. These controllers use several physiological data points of the patient, such as the breathing frequency, oxygen level, etc., to help the patient get stable and provide an appropriate amount of oxygen. The input to the system includes the carbon dioxide level, oxygen level, and air resistance. These ventilators help adjust the breathing frequency in a clinically appropriate manner in
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In this paper [2], different settings of mechanical ventilation of ventilators are analysed depending on the particular patient's lungs condition, and the determination of these parameters depends on the observed patient's past medical history and the experience of the clinicians involved in their practice. In the research, they have used Graded Particle
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