Early mastitis diagnosis through topological analysis of biosignals from low-voltage alternate current electrokinetics

Mastitis is the most economically important disease of dairy cows worldwide, and it constantly plagues the dairy industry. A reliable biosensing method is thus imperative to detect this disease at its early stage and accurately identify the pathogen concentration level in order to better control the disease and consequently improve the quality of milk. Recent research indicates that shorter assay time and/or higher sensitivity can be achieved by integrating alternate current electrokinetics (ACEK) with biosensing. However, most existing ACEK devices use voltage levels around 10V at the risk of electrochemical reactions because a lower voltage may not effectively trigger the ACEK effect. Currently, there are no related works that can efficiently tackle the dilemma between avoiding electrochemical reaction and accelerating assay process. This paper adopts low-voltage (40~135mV) ACEK, which is safe but yields ambiguous biosignals within a short assay time, presenting great challenge to high-fidelity identification of pathogen concentration levels. This paper makes two distinctive contributions to the field of biosignal analysis. First, moving away from the traditional signal analysis in the time or spectral domain, we exploit the possibility of representing the biosignal through topological analysis that would reveal the intrinsic topological structure of point clouds generated from the biosignal. Second, in order to tackle another common challenge of biosignal analysis, i.e., limited sample size, we propose a so-called Gaussian-based decision tree (GDT), which can efficiently classify the biosignals even when the sample size is extremely small. Experimental results on the classification of five pathogen concentration levels using only 10 samples taken under various voltage levels demonstrate the robustness of the topological features as well as the advantage of GDT over some other conventional classifiers in handling small dataset. Our method reduces the voltage of ACEK to a safe level and still yields high-fidelity results in a short time.