Detection of faulty glucose measurements using texture analysis

Faults occurring in hand-held blood glucose measurements can be critical to patient self-monitoring, as they can lead to unnecessary changes of treatment. We propose a method to detect faulty glucose measurement frames in devices that use a camera to estimate the glucose concentration. We assert that texture, as opposed to intensity, is able to differentiate between correct and false glucose measurements, regardless of the given blood sample. The co-occurrence based textural features energy, maximum probability and correlation prove to be suitable for our detection application. We calculate kinetic feature curves and use a hypothesis testing approach to detect faulty measurements. Our method is able to detect a faulty measurement after less than one third of the time, which would usually be needed. The validation of our method is done using a real data set of blood glucose measurements obtained using different glucose concentrations and containing both correct and faulty measurements.