E-NOSE for identification of expiratory gases in patients with chronic obstructive pulmonary disease through breath

Abstract

The document proposes the design, implementation, programming, and use of an Electronic Nose (E-NOSE) to identify patterns in exhaled respiratory gases in individuals with respiratory problems and healthy individuals, with the aim of quickly identifying the condition known as Chronic Obstructive Pulmonary Disease (COPD). The Electronic Nose (E-NOSE) consists of an array of low-cost MQ sensors, an Arduino microcontroller, Neural Network algorithms, and a gas concentration chamber. Data acquisition was performed at the "Dr. José María Rodríguez" General Hospital in Ecatepec with the consent of the hospital's Bioethics Committee, healthy patients, and patients diagnosed with COPD. We stored the data for subsequent processing, which allowed us to train, validate, and test a neural network to classify healthy patients from those with COPD. The model was trained with unprocessed data. The obtained results yielded a 92% confusion matrix, indicating the potential viability of the prototype. The prototype should substantially improve the gas concentration chamber using additive manufacturing and seek to enhance data quality through preprocessing.