Automated particle classification based on digital acquisition and analysis of flow cytometric pulse waveforms

In flow cytometry, the typical use of front-end analog processing limits the pulse waveform features that can be measured to pulse integral, height, and width. Direct digitizing of the waveforms provides a means for the extraction of additional features, for example, pulse skewness and kurtosis, and Fourier properties. In this work, we have fist demonstrated that the Fourier properties of the pulse can be employed usefully for discrimination between different types of cells that otherwise cannot be classified by using only time-domain features of the pulse. We then implemented and evaluated automatic procedures for cell classification based on neural networks. We established that neural networks could provide an efficient means of classification of cell types without the need for user interaction. The neural networks were also employed in an innovative manner for analysis of the digital flow cytometric data without feature extraction. The performance of the neural networks was compared with that of a more conventional means of classification, the K-means clustering algorithm. Neural networks can be realized in hardware, and this, in addition to their highly parallel architecture, makes them an important potential part of real-time analysis systems. These results are discussed in terms of the design of a real-time digital data acquisition system for flow cytometry.