Intelligent Breath Analyzer System using Machine Learning Approach

Abstract

We propose DeepBreath, a deep learning model which automatically recognized people’s psychological stress level (mental overload) from their breathing patterns. Using a low cost Microphone, we track a person’s breathing patterns as temperature changes around his/her nostril.

First of all, instead of creating handcrafted features to capture aspects of the breathing patterns, we transform the uni-dimensional breathing signals into two dimensional respiration variability spectrometer (RVS) sequences. Finally, a data augmentation technique, inspired from solutions for over-fitting problems in deep learning, is applied to allow the CNN to learn with a small-scale dataset from short-term measurements Finally, the dataset of labeled thermal images will be open to the community. This review presents and discusses a new frontier for fast, risk-free and potentially inexpensive diagnostics of respiratory diseases by detecting volatile organic compounds (VOCs) present in exhaled breath.  Ideas regarding the improvement of Microphone and the further planning of work flow are also discussed The other part discusses diverse Microphone that have been developed and used for the detection of respiratory diseases In this project using microphone we predicted that the person has a Covid-19 positive or not .In this project ,we have make such type of  App ,in which first we take some sample of covid-19 that we stored the data in the App for analyze.  When any person started breathing the sensor of the microphone sensor the breathing pattern and matches with the given input data whichever already stored in the App using machine learning. And after comparis it shows us that the person is suffering from covid-19 or Not. In this our App is working. The occurrence of asynchronous breathing (AB) during mechanical ventilation (MV) can have detrimental effect towards a patients recovery. Hence it is essential to develop an algorithm to automate AB detection in real time . In this study a method for AB detection  using machine learning ,in particular Convectional Neural Network CNN is presented and its presented and its performance in identifying AB .