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Decoding of Code-Multiplexed Coulter Sensor Signals via Deep Learning

机译:通过深度学习对码复用库尔特传感器信号进行解码

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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.
机译:代码复用的库尔特传感器可以轻松集成到微流体设备中,并提供有关悬浮粒子的时空操作的信息,以进行定量样品评估。在本文中,我们介绍了一种基于深度学习的解码算法,用于处理微流体设备上码多路复用库尔特传感器网络的输出波形。我们基于深度学习的算法既简化了编码库尔特传感器的设计,又提高了信号处理速度。作为原理的证明,我们设计并制造了带有10个代码多路复用Coulter传感器的微流体平台,并使用人类卵巢癌细胞悬液作为测试样品来表征系统。我们基于深度学习的算法以800个粒子/秒的样本处理速度实现了87%的解码精度。

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