Detecting cells rotations for increasing the robustness of cell sizing by impedance measurements, with or without machine learning.

The Coulter principle is a widespread technique for sizing red blood cells (RBCs) in hematological analyzers. It is based on the monitoring of the electrical perturbations generated by cells passing through a micro-orifice, in which a concentrated electrical field is imposed by two electrodes. However, artifacts associated with near-wall passages in the sensing region are known to skew the statistics for RBCs sizing. This study presents numerical results that emphasize the link between the cell flow-induced rotation in the detection area and the error in its measured volume. Based on these observations, two methods are developed to identify and reject pulses impaired by cell rotation. In the first strategy, the filtering is allowed by a metric computed directly from the waveform. In the second, a numerical database is employed to train a neural network capable of detecting if the cell has experienced a rotation, given its electrical pulse. Detecting and rejecting rotation-associated pulses are shown to provide results comparable to hydrodynamical focusing, which enforces cells to flow in the center of the orifice, the gold standard implementation of the Coulter principle.