Method and apparatus for measuring non-nutritive suck pattern stability
Method and apparatus are provided for generating an index associated with non-nutritive suck pattern stability. A method includes measuring a plurality of non-nutritive suck (NNS) pressure samples, generating a plurality of correlation values using the NNS pressure samples, and generating a measure of non-nutritive suck pattern stability using the plurality of correlation values.
1. A method comprising:
collecting a plurality of digitized, non-nutritive suck (NNS) pressure samples using a non-nutritive nipple with a pressure transducer;
determining a plurality of standard deviations of the plurality of burst data sets, wherein each standard deviation is based on a burst data point value associated with a particular sample of each of a plurality of normalized burst data sets; and
summing the plurality of standard deviations to provide a spatiotemporal index (STI) of non-nutritive suck pattern stability, wherein the STI is configured to provide a composite indication of variability of amplitude, and duration of a plurality of portions of the NNS pressure samples.
2. The method of claim 1 , including identifying in the plurality of non-nutritive suck samples a plurality of burst data sets wherein each burst data set includes a plurality of sequential pressure peaks.
3. The method of claim 2 , wherein determining a plurality of standard deviations includes normalizing each of the identified plurality of burst data sets to provide the plurality of normalized burst data sets.
4. The method of claim 3 , wherein normalizing includes normalizing amplitude and duration of each of the identified plurality of burst data sets.
5. The method of claim 4 , wherein normalizing the amplitude comprises:
determining mean amplitude of the burst data set;
determining one or more amplitude correlation values from a predetermined number of consecutive burst data points in the burst data set; and
dividing each particular burst data point by one of the one or more amplitude correlation values determined using the particular burst data point.
6. The method of claim 4 , wherein the amplitude correlation values are a standard deviation.
7. The method of claim 4 , wherein normalizing duration includes normalizing duration of each identified burst data set over a programmable number of data points.
8. The method of claim 7 , wherein normalizing duration includes using linear interpolation.
9. The method of claim 7 , wherein normalizing duration includes using rational interpolation.
10. The method of claim 7 , wherein normalizing duration includes using spline interpolation.
11. The method of claim 1 , wherein summing the plurality of standard deviations includes cumulatively summing the plurality of standard deviations.
12. The method of claim 1 , wherein collecting a plurality of digitized, non-nutritive suck (NNS) pressure samples includes sampling an output signal of the pressure transducer.
13. The method of claim 1 , further comprising applying a plurality of pneumatic stimulation pulses to the non-nutritive nipple according to a first pulse profile.
14. The method of claim 13 , wherein applying includes grouping pulses in time proximity as separate burst data waveforms.
15. The method of claim 13 , further comprising modifying the application of the plurality of pneumatic stimulation pulses based on the spatiotemporal index of non-nutritive suck pattern stability.
16. A method comprising:
collecting a plurality of digitized, non-nutritive suck (NNS) pressure samples using a non-nutritive nipple with a pressure transducer;
identifying in the plurality of non-nutritive suck samples a plurality of burst data sets wherein each burst data set includes an equal number of sequential pressure peaks;
normalizing the amplitude of each burst data set, wherein normalizing the amplitude includes:
determining mean amplitude of the burst data set;
determining one or more amplitude correlation values from a predetermined number of consecutive burst data points in the burst data set using the mean amplitude; and
dividing each particular burst data point by one of the one or more amplitude correlation values determined using the particular burst data point;
normalizing a duration of each burst data set over a predetermined number of data points;
determining a plurality of standard deviations of the plurality of burst data sets, wherein each standard deviation is based on a burst data point value associated with a particular sample of each of the plurality of normalized burst data sets; and
summing the plurality of standard deviations to provide a spatiotemporal index (STI) non-nutritive suck pattern stability, wherein the STI is configured to provide a composite indication of variability of amplitude, and duration of a second plurality of burst data sets of the plurality of burst data sets.
17. The method of claim 16 , including applying a plurality of pneumatic stimulation pulses to the non-nutritive nipple according to a first pulse profile.
18. A non-transitory computer-readable memory that stores instructions for execution by a processor to generate a stability value indicative of both amplitude and temporal non-nutritive suck pattern stability by performing operations comprising:
collecting a plurality of digitized, non-nutritive suck (NNS) pressure samples using a non-nutritive nipple with a pressure transducer;
determining a plurality of standard deviations of the plurality of burst data sets, wherein each standard deviation is based on a burst data point value associated with a particular sample of each burst data set of a plurality of normalized burst data sets; and
summing the plurality of standard deviations to provide a spatiotemporal index (STI) of non-nutritive suck pattern stability.
19. The non-transitory computer-readable memory of claim 18 , wherein determining a plurality of standard deviations includes:
identifying in the plurality of NNS pressure samples a plurality of burst data sets wherein each burst data set includes an equal number of sequential pressure peaks; and
normalizing the amplitude of each burst data set, wherein normalizing the amplitude includes:
determining mean amplitude of the burst data set;
determining one or more amplitude correlation values from a predetermined number of consecutive burst data points in the burst data set using the mean amplitude; and
dividing each particular burst data point by one of the one or more amplitude correlation values determined using the particular burst data point;
normalizing a duration of each burst data set over a predetermined number of data points,
wherein the normalizing the amplitude of each burst data set and the normalizing the duration of each burst data provides
the plurality of normalized burst data sets.