Current commercial flow cytometers employ analog circuits to produce the feature values of the pulse waveforms that result from particle analysis. The use of analog pulse processing limits the features that can be measured to pulse integral, pulse height, and pulse width, and a large amount of potentially relevant information about the shape of the pulse waveform is lost. Direct digitizing of the waveform provides a means for the extraction of additional features, for example, pulse skewness and kurtosis, as well as the Fourier properties of the pulse. Here we describe a digital pulse waveform processing system that is compatible both with a commercial flow cytometer, and with a readily available computational platform. The performance of the digital and analog systems were compared through analysis of synthetic waveforms, and the waveforms produced by standard fluorescence microspheres and biological particles. The digital waveform processing system was found to be accurate and flexible, and the value of several of its unique attributes was demonstrated using biological cells. A protocol was designed in which digital pulse processing provided a means for the quantitative monitoring of the optical alignment of the flow cytometer. It was shown that digital pulse processing could be used to discriminate between particle classes which produce feature values indistinguishable through analog pulse processing, and to discriminate accurately single cells from doublets and larger aggregates. Q 1995 wi'iley-Liss, I~C .

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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 first 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.