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Issue 19, 2019
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Processing code-multiplexed Coulter signals via deep convolutional neural networks

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Abstract

Beyond their conventional use of counting and sizing particles, Coulter sensors can be used to spatially track suspended particles, with multiple sensors distributed over a microfluidic chip. Code-multiplexing of Coulter sensors allows such integration to be implemented with simple hardware but requires advanced signal processing to extract multi-dimensional information from the output waveform. In this work, we couple deep learning-based signal analysis with microfluidic code-multiplexed Coulter sensor networks. Specifically, we train convolutional neural networks to analyze Coulter waveforms not only to recognize certain sensor waveform patterns but also to resolve interferences among them. Our technology predicts the size, speed, and location of each detected particle. We show that the algorithm yields a >90% pattern recognition accuracy for distinguishing non-correlated waveform patterns at a processing speed that can potentially enable real-time microfluidic assays. Furthermore, once trained, the algorithm can readily be applied for processing electrical data from other microfluidic devices integrated with the same Coulter sensor network.

Graphical abstract: Processing code-multiplexed Coulter signals via deep convolutional neural networks

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Supplementary files

Article information


Submitted
20 Jun 2019
Accepted
21 Aug 2019
First published
23 Aug 2019

Lab Chip, 2019,19, 3292-3304
Article type
Paper
Author version available

Processing code-multiplexed Coulter signals via deep convolutional neural networks

N. Wang, R. Liu, N. Asmare, C. Chu and A. F. Sarioglu, Lab Chip, 2019, 19, 3292
DOI: 10.1039/C9LC00597H

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