A Fuel Cell Based Sensing Platform for Selective Detection of Acetone in Hyperglycemic Patients

Abstract

Elevated level of acetone in breath or sweat is an indication of type-I diabetes, which can turn into ‘ketoacidosis’- a serious hyperglycemic condition. Continuous monitoring is a challenge among the conventional sensing methods. Though real-time detection of acetone from different biofluids is promising, signal interference from other biomarkers remains an issue. A minor fluctuation of the signals in micro-Ampere range causes substantial overlapping in linear/polynomial calibration fittings. To address the above in non-invasive detection, principal component analysis (PCA) was demonstrated which generated specific pattern for the different concentration points of acetone in the subspace. It improves the overlapping of the signals in between of two or different concentration points of the data sets and eliminate dimensionality and redundancy of data variables. An algorithm following PCA was incorporated into the microcontroller (nRF51822) for the functionality of wearable device in a selective manner in the physiological range (0.5ppm to 4ppm) of acetone.