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Processing code-multiplexed Coulter signals via deep convolutional neural networks.

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  • 1School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. sarioglu@gatech.edu.

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This study uses deep learning to analyze Coulter sensor data from microfluidic chips, enabling precise particle tracking. The advanced signal processing accurately predicts particle size, speed, and location for real-time analysis.

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Area of Science:

  • Biotechnology
  • Microfluidics
  • Signal Processing

Background:

  • Coulter sensors traditionally count and size particles.
  • Microfluidic integration of multiple Coulter sensors allows spatial particle tracking.
  • Code-multiplexing simplifies hardware but requires advanced signal processing for multi-dimensional data extraction.

Purpose of the Study:

  • To integrate deep learning-based signal analysis with microfluidic code-multiplexed Coulter sensor networks.
  • To train convolutional neural networks for analyzing Coulter waveforms and resolving interferences.
  • To predict particle size, speed, and location using advanced signal processing.

Main Methods:

  • Coupling deep learning (convolutional neural networks) with microfluidic code-multiplexed Coulter sensor networks.
  • Training neural networks to recognize sensor waveform patterns and resolve interferences.
  • Utilizing advanced signal processing to extract multi-dimensional information from output waveforms.

Main Results:

  • Achieved >90% pattern recognition accuracy for distinguishing non-correlated waveform patterns.
  • Demonstrated potential for real-time microfluidic assays due to processing speed.
  • Showcased the algorithm's ability to predict particle size, speed, and location.

Conclusions:

  • Deep learning-based signal analysis enhances microfluidic Coulter sensor networks for particle tracking.
  • The developed algorithm offers high accuracy and potential for real-time applications.
  • The trained algorithm is adaptable for processing data from other microfluidic devices with similar sensor networks.