Impedance-based classification of biological cells or other particles via machine learning methods

Abstract

The disclosure includes a platform for classifying particles (e.g., biological cells) based on impedance measurements of individual particles using a biodevice. A machine learning model can be used to classify cells with high accuracy (e.g., greater than 95%). This can be accomplished without the need for sample preparation or cellular tagging. The biodevice can immobilize individual cells (e.g., mechanically immobilize with a micro-pore). An external AC waveform can be applied, for example, in the 1 Hz to 1 MHz range. The machine learning model may be generated to provide classifications based on, for example, measurements of impedance magnitude and phase angle at discrete frequencies.

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