A breathalyzer for the assessment of chronic kidney disease patients’ breathprint: Breath flow dynamic simulation on the measurement chamber and experimental investigation

Abstract

Chemical sensor technologies with appropriate chamber design influence the kinetic adsorption of exhaled breath. Moreover, emphasize on feature extraction from breathprint may improve the accuracy of e-nose system. In this work, optimization of hardware and software have been elucidated for the improvement in the accuracy of classification between healthy and Chronic Kidney Disease (CKD) breathprint using a commercial sensor (MQ 135). Dimensioning the chamber to accommodate complete alveolar breath along with sensor positioning in the effective flow path of breath sample have been modeled using Computational Fluid Dynamics (CFD) which solved Navier-stokes equation for 3D chambers. The simulation results showed the cylindrical chamber with tangential sensor position to the flow path have effective adsorption of breath sample. The average of saturation voltage of breathprint acquired from 51 hemodialysis patients using the optimized hardware was 13.1% higher than the average saturation voltage of 47 healthy subjects. Fast Fourier Transform (FFT) coefficients extracted from breathprints exhibited high statistical significance between groups (p < 0.05). Susequently, the subset of features ranked by gain ratio algorithm showed highest accuracy of 85.7% for Support Vector Machine (SVM) classifier. These results indicated the importance of hardware and software optimization of e-nose systems for the potential applications in real time disease diagnosis.