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首页> 外文会议>4th International Symposium on Communications, Control and Signal Processing (ISCCSP 2010) >Signal processing for biologically inspired sensors
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Signal processing for biologically inspired sensors

机译：生物启发传感器的信号处理

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摘要

Pores or channels with diameters in the range of nanometers up to micrometers can be used as Coulter counting apertures to detect particles and organic molecules such as proteins. Coulter counting is performed by applying a constant potential across a nano- or micropore while recording the drop in ionic current upon passage of a molecule. Looking at the shape and duration of these current pulses enables us to estimate the size as well as the concentration of these molecules. Discrimination between different analytes can be performed by extracting appropriate features from the Coulter signals (events) and using them for classification. The challenge in being able to identify particular analytes is that a drop in current can also be caused by a molecule bouncing off the pore wall rather than moving through the micropore. Such drops are called non-events and can be discriminated from the events using Support Vector Machines. In this paper, we consider the amplitude of the current drop and the duration of the current pulse as features to determine if an event occurred. The proposed approach uses the Dirichlet process mixture model to cluster the data in the feature domain as the type of the events in the signal record is unknown. Results obtained show that the Dirichlet process mixture model accurately finds the types of events and their count for each signal record.
机译：直径在纳米至微米范围内的孔或通道可用作库尔特计数孔，以检测颗粒和有机分子（例如蛋白质）。库尔特计数是通过在纳米孔或微孔上施加恒定电势，同时记录分子通过时离子电流的下降来进行的。查看这些电流脉冲的形状和持续时间可以使我们估算这些分子的大小和浓度。可以通过从库尔特信号（事件）中提取适当的特征并将其用于分类来进行不同分析物之间的区分。能够识别特定分析物的挑战是电流的下降也可能是由于分子从孔壁反弹而不是穿过微孔而引起的。此类丢弃称为非事件，可以使用支持向量机将其与事件区分开。在本文中，我们将电流降的幅度和电流脉冲的持续时间视为确定事件是否发生的特征。由于信号记录中事件的类型是未知的，因此所提出的方法使用Dirichlet过程混合模型对特征域中的数据进行聚类。获得的结果表明Dirichlet过程混合模型可以准确地找到事件的类型以及每个信号记录的计数。

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