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 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.

Flow cytometric analysis using digital signal processing.

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.