Serologic Diagnosis of Taenia Solium Cysticercosis through Linear Unmixing Analysis of Biosignals from ACEK Capacitive Sensing Method

Cysticercosis is a parasitic infection caused by adult tapeworms, and it constantly plagues the livelihoods of people from subsistence farming communities in developing countries. Diagnosis of Cysticercosis typically requires both central nervous system imaging and serological testing. The most common methods in serological testing are Enzyme-linked Immunosorbent Assay (ELISA) and Enzyme Immuno-electrotransfer Blot (EITB). Both ELISA and EITB methods are excessively time-consuming and labor-intensive. Recent research indicates that a shorter assay time and/or higher sensitivity can be achieved by integrating alternate current electrokinetics (ACEK) with biosensing. However, the raw time-series data is very noisy and the size of the dataset is extremely small, which would bring two potential challenges. On one hand, traditional statistical methods cannot extract features robust enough for high sensitivity as well as high specificity. On the other hand, the small data size limits the usage of automatic feature extractors such as deep neural networks. In this paper, we propose a linear unmixing based approach by exploiting the possibility that the time-series biological signals can be represented as linear combinations of source signals. This paper makes distinctive contributions to the field of bio-signal by introducing the unmixing model from the image processing domain to the time-series domain. Experimental results on the classification of Cysticercosis using 123 samples demonstrate the robustness and superior performance of the linear unmixing method over other conventional classifiers in handling small datasets.