A Time-Frequency Deep Learning Classification Model for Metal Oxide Coated Particles

This study uses time-frequency transformed data and deep learning (DL) models to identify the groups of metal oxide nano-coated micro-particles using an impedance cytometer. The nano-coated bioparticles generate distinct electrical signals in a multifrequency electric field and can be used in biosensing applications. The current machine learning-enabled sensing modalities are unable to accurately differentiate different bioparticles as the feature selection and feature engineering techniques are ineffective in selecting useful and informative features. Here, we use Wigner-Vile Distribution to transform the time series data into the time-frequency domain and employ three deep learning models to evaluate the ability of time-frequency transformed data to accurately represent the most important features. A classification accuracy of 75% for (10nm and 30nm) coated particles was achieved on the simplest DL model. This combination of time-frequency representation and the DL model will be sufficient to differentiate bioparticles by acting as an alternative to other ML-based techniques.