An Experimental Apparatus for E-Nose Breath Analysis in Respiratory Failure Patients

Abstract

Background: Non-invasive, bedside diagnostic tools are extremely important for tailo ring the management of respiratory failure patients. The use of electronic noses (ENs) for exhaled breath analysis has the potential to provide useful information for phenotyping different respiratory disorders and improving diagnosis, but their application in respiratory failure patients remains a challenge. We developed a novel measurement apparatus for analysing exhaled breath in such patients. Methods: The breath sampling apparatus uses hospital medical air and oxygen pipeline systems to control the fraction of inspired oxygen and prevent contamination of exhaled gas from ambient Volatile Organic Compounds (VOCs) It is designed to minimise the dead space and respiratory load imposed on patients. Breath odour fingerprints were assessed using a commercial EN with custom MOX sensors. We carried out a feasibility study on 33 SARS-CoV-2 patients (25 with respiratory failure and 8 asymptomatic) and 22 controls to gather data on tolerability and for a preliminary assessment of sensitivity and specificity. The most significant features for the discrimination between breath-odour fingerprints from respiratory failure patients and controls were identified using the Boruta algorithm and then implemented in the development of a support vector machine (SVM) classification model. Results: The novel sampling system was well-tolerated by all patients. The SVM differentiated between respiratory failure patients and controls with an accuracy of 0.81 (area under the ROC curve) and a sensitivity and specificity of 0.920 and 0.682, respectively. The selected features were significantly different in SARS-CoV-2 patients with respiratory failure versus controls and asymptomatic SARS-CoV-2 patients (p < 0.001 and 0.046, respectively). Conclusions: the developed system is suitable for the collection of exhaled breath samples from respiratory failure patients. Our preliminary results suggest that breath-odour fingerprints may be sensitive markers of lung disease severity and aetiology.

Development of an e-Nose System for the Early Diagnosis of Sepsis in Mechanically Ventilated Patients: A Preliminary Study

“…The sampling system was designed specifically to collect exhaled breath in a Nalophan TM bag during mechanical ventilation by automatically detecting the beginning and the end of each expiration. Once the bag is filled, it is kept at constant humidity and temperature for approximately 24 h for stabilization [3]. EB is sampled at the beginning of the experiment (baseline) and after the start of LPS infusion (at 5, 30, 60, 90, 120, 150 and 180 min after infusion), or in the corresponding timestamp after placebo sodium chloride infusion in the control group.…”

Potential for Early Noninvasive COVID-19 Detection Using Electronic-Nose Technologies and Disease-Specific VOC Metabolic Biomarkers

“…Bax et al [ 157 ] developed breath aroma fingerprints from exhaled breath samples, assessed using a commercial EOS-AROMA e-nose with a custom 4-MOS sensor array in combination with a T-piece valve and 5L NalophanTM bags as a system to help the management of respiratory failure patients. They carried out a feasibility study on 33 SARS-CoV-2 patients, consisting of 25 with respiratory failure, 8 asymptomatic, and 22 controls to gather data on tolerability to the breath analysis apparatus and for a preliminary assessment of sensitivity and specificity.…”

Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose

“…Notably, pulmonary biomarkers aid in understanding respiratory system-related processes and changes [ 25 ]. Clinical research on breath biomarkers has predominantly focused on diseases like lung cancers and asthma [ 26 , 27 ], cystic fibrosis [ 28 ], tuberculosis diagnosis [ 29 ], myocardial infarction [ 30 ], colorectal cancer [ 31 ], head and neck cancer [ 32 ], analysis in respiratory failure patients [ 33 ], gastric cancer detection [ 34 ], and importantly, metabolic disorders like diabetes [ 16 , 35 , 36 , 37 , 38 ].…”