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Code-multiplexed Coulter sensors can easily be integrated into microfluidic devices and provide information on spatiotemporal manipulations of suspended particles for quantitative sample assessment. In this paper, we introduced a deep learning-based decoding algorithm to process the output waveform from a network of code- multiplexed Coulter sensors on a microfluidic device. Our deep learning-based algorithm both simplifies the design of coded Coulter sensors and increases the signal processing speed. As a proof of principle, we designed and fabricated a microfluidic platform with 10 code-multiplexed Coulter sensors, and used a suspension of human ovarian cancer cells as a test sample to characterize the system. Our deep learning-based algorithm resulted in an 87% decoding accuracy at a sample processing speed of 800 particles/s.
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Processing code-multiplexed Coulter signals via deep convolutional neural networks
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.
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- Award ID(s):
- 1752170
- PAR ID:
- 10399295
- Date Published:
- Journal Name:
- Lab on a Chip
- Volume:
- 19
- Issue:
- 19
- ISSN:
- 1473-0197
- Page Range / eLocation ID:
- 3292 to 3304
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/c9lc00597h
