Deep learning assisted cell electrical signal analysis in impedance cytometry.

In this study, we developed BioFluxNet, a 1D CNN-based algorithm for automated analysis of raw electrical signals in impedance cytometry to directly classify cell types and quantify cell counts. The network comprises three functional blocks: a feature extraction module with five alternating convolutional, normalization, activation, and pooling layers; a classification block with a single linear layer; and a counting block incorporating two linear layers and an activation layer. Raw signal streams of particles and cells, collected from an impedance cytometry featuring a serpentine microchannel and four pairs of face-to-face electrodes, are stored as training and testing sets, and then used to train and test BioFluxNet. Results demonstrate that the well-trained network achieves robust classification of raw signal streams from diverse particles and cells (including blood and tumor cells), while simultaneously enabling accurate counting of particles or cells form corresponding signal streams. Compared to conventional signal processing methods, BioFluxNet eliminates many time-consuming signal processing steps, reduces manual intervention, and minimizes subjectivity of operators. The proposed deep learning framework offers a rapid, automated solution for electrical signal analysis in impedance cytometry, showcasing broad applicability in cell characterization and related biomedical fields.