LSU College of Science
LSU  | Mathematics

Summer 2016

Infant Suck Detection Interface

Authors: Amy Adair, Abiti Adili, Zachary Bradshaw, Joshua Brock, Brandon Dellucky, Heewon Hah, Margarite LaBorde, Hugo Leiva, Miles Robicheaux, Sima Sobhiyeh, Zhaoxia (Mary) Wang, Jerome Weston

We collaborated with Abby Duhé, a researcher at Pennington Biomedical Research Center, to improve a data analysis program used to detect sucks in signals obtained from infant bottle feedings. The focus was to develop a user-friendly and efficient graphical user interface (GUI) using the Matrix Laboratory (MatLab) programming software. We began by improving the layout of the original GUI Abby used to analyze the signal. Once we designed a more appealing interface, we studied and improved upon the previous signal processing and suck detection methods.

Due to limitations, the previous program was inaccurate and ambiguous at best. To address this, we implemented proper orthogonal decomposition (POD), which is a method of data analysis that creates a database of prototypes resembling typical sucks. Therefore, this new GUI—called the Infant Suck Detection Interface—can now detect distinct variations in initial data. This may be beneficial when researching the effects of age and other factors on infantile feeding. In addition to improving the accuracy of suck detection, we also introduced filtering and preprocessing procedures. Through Fourier analysis and convolutions, the program now reduces noise efficiently, helping to identify sucks. These improvements, along with the new suck detection process, drastically reduced the run time for the program, allowing researchers to obtain results almost instantly.

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