Artificial Neural Networks for GMR-Based Magnetic Cytometry.

In this work, we propose an artificial neural network (ANN) for magnetic microcytometry pattern recognition and automated counting. The method is tested for detecting analytes in the 2-3-mu m range. The cytometer is composed of a disposable cartridge and an acquisition platform. The disposable cartridge contains microfluidic channels with 10 x 100 mu m2 cross section on top of a substrate with magnetoresistive (MR) sensors. The custom analog signal chain performs with an integrated noise of 2.99 mu Vrms in a 10-kHz bandwidth. To employ the ANN, we synthesize a training dataset based on the magnetic-dipole equation and several dataset expansion methods. The ANN is tested on an experiment with 2.8-mu m magnetic particles (MPs) and compared with an improved threshold-based method with reduced false positives. The ANN produces a maximum of 90% detection rate, improving on the 30%-50% detection rates of other single-sensor methods published in the literature.