Abstract
Background and Objective: The emergence of the nonnutritive suck (NNS) pattern in preterm infants reflects the integrity of the brain and is used by clinicians in the neonatal intensive care unit (NICU) to assess feeding readiness and oromotor development. A critical need exists for an integrated software platform that provides NNS signal preprocessing, adaptive waveform discrimination, feature detection, and batch processing of big data sets across multiple NICU sites. Thus, the goal was to develop and describe a cross-platform graphical user interface (GUI) and terminal application known as NeoNNS for single and batch file time series and frequency-domain analyses of NNS compression pressure waveforms using analysis parameters derived from previous research on NNS dynamics. Methods. NeoNNS was implemented with Python and the Tkinter GUI package. The NNS signal-processing pipeline included a low-pass filter, asymmetric regression baseline correction, NNS peak detection, and NNS burst classification. Data visualizations and parametric analyses included time- and frequency-domain view, NNS spatiotemporal index view, and feature cluster analysis to model oral feeding readiness. Results. 568 suck assessment files sampled from 30 extremely preterm infants were processed in the batch mode (<50 minutes) to generate time- and frequency-domain analyses of infant NNS pressure waveform data. NNS cycle discrimination and NNS burst classification yield quantification of NNS waveform features as a function of postmenstrual age. Hierarchical cluster analysis (based on the Tsfresh python package and NeoNNS) revealed the capability to label NNS records for feeding readiness. Conclusions. NeoNNS provides a versatile software platform to rapidly quantify the dynamics of NNS development in time and frequency domains at cribside over repeated sessions for an individual baby or among large numbers of preterm infants at multiple hospital sites to support big data analytics. The hierarchical cluster feature analysis facilitates modeling of feeding readiness based on quantitative features of the NNS compression pressure waveform.
Highlights
Human neonates demonstrate two distinct types of sucking in a developmental progression: the first is nonnutritive sucking (NNS)—a repetitive bursting pattern characterized by mouthing and the tongue/jaw compressions on a pacifier or nipple in the absence of a liquid stimulus [1], and followed by nutritive sucking (NS)—when a nutrient is obtained from the bottle or breast. e NNS compression pressure pattern is an accessible motor behavior which can be digitized in real time and subsequently used by the medical care team to make inferences about brain development and prefeeding skills in preterm and term infants [2]
NNS waveforms can be studied in the frequency domain on the Power Spectrum view page (Labeled 4 in Figure 1), which shows the results of 4 computational spectral methods, including the fast Fourier transformation (FFT) [34], periodogram [35], Welch’s [36], and Yule–Walker methods [37], respectively
After the first N bursts of M successive cycles are chosen, individual N bursts are aligned at the same origin as shown in the upper panel of spatiotemporal index (STI) view. e middle panel shows an overlay of five NNS bursts (x- and y-axis normalized) assigned to a 10,000 data sample window. e bottom plot panel shows the standard deviation of the N normalized burst segments from the second panel and displays the resulting STI value
Summary
Human neonates demonstrate two distinct types of sucking in a developmental progression: the first is nonnutritive sucking (NNS)—a repetitive bursting pattern characterized by mouthing and the tongue/jaw compressions on a pacifier or nipple in the absence of a liquid stimulus [1], and followed by nutritive sucking (NS)—when a nutrient is obtained from the bottle or breast. e NNS compression pressure pattern is an accessible motor behavior which can be digitized in real time and subsequently used by the medical care team to make inferences about brain development and prefeeding skills in preterm and term infants [2]. In the neonatal intensive care unit (NICU), the temporal organization of the NNS burst structure provides clinicians with diagnostic information on the infant’s health status, including various forms of lung disease, infection, and neurological function during a critical period of brain development as the infant transitions from the tube to oral feeding. Objective: e emergence of the nonnutritive suck (NNS) pattern in preterm infants reflects the integrity of the brain and is used by clinicians in the neonatal intensive care unit (NICU) to assess feeding readiness and oromotor development. Data visualizations and parametric analyses included time- and frequency-domain view, NNS spatiotemporal index view, and feature cluster analysis to model oral feeding readiness. NeoNNS provides a versatile software platform to rapidly quantify the dynamics of NNS development in time and frequency domains at cribside over repeated sessions for an individual baby or among large numbers of preterm infants at multiple hospital sites to support big data analytics. e hierarchical cluster feature analysis facilitates modeling of feeding readiness based on quantitative features of the NNS compression pressure waveform
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