Decoding of Code-Multiplexed Coulter Sensor Signals via Deep Learning
- Resource Type
- Conference
- Authors
- Wang, Ningquan; Liu, Ruxiu; Asmare, Norh; Anandakumar, Dakshitha B.; Fatih Sarioglu, A.
- Source
- 2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII) Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII), 2019 20th International Conference on. :202-205 Jun, 2019
- Subject
- Bioengineering
Components, Circuits, Devices and Systems
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Microfluidics
Electrodes
Training
Signal processing algorithms
Deep learning
Decoding
Convolution
Lab-on-a-chip
bioanalysis
machine learning
deep learning
convolutional neural network
Coulter sensing
- Language
- ISSN
- 2167-0021
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.