FIELD OF THE INVENTION

The invention relates generally to monitoring of expectant mothers. More particularly, the invention relates to analysis of sensed acoustic data to produce computed representations of uterine activity, such as uterine contractions.

BACKGROUND

A uterine contraction is a temporary process during which the muscles of the uterus are shortened and the space between muscle cells decreases. These structural changes in the muscle cause an increase in uterine cavity pressure to allow pushing the fetus downward in a lower position towards delivery. During a uterine contraction, the structure of myometrial cells (i.e., cells of the uterus) changes and the uterine wall becomes thicker.FIG. 1Ashows an illustration of a relaxed uterus, in which the muscular wall of the uterus is relaxed.FIG. 1Bshows an illustration of a contracted uterus, in which the muscular wall of the uterus contracts and pushes the fetus against the cervix.

Uterine contractions are monitored to evaluate the progress of labor. Typically, the progress of labor is monitored through the use of two sensors: a tocodynamometer, which is a strain gauge-based sensor positioned on the abdomen of an expectant mother, and an ultrasound transducer, which is also positioned on the abdomen. The signals of the tocodynamometer are used to provide a tocograph (“TOCO”), which is analyzed to identify uterine contractions, while the signals of the ultrasound transducer are used to detect fetal heart rate, maternal heart rate, and fetal movement. However, these sensors can be uncomfortable to wear, and can produce unreliable data when worn by obese expectant mothers.

SUMMARY

In some embodiments, the present invention provides a specifically programmed computer system, including at least the following components: a non-transient memory, electronically storing computer-executable program code; and at least one computer processor that, when executing the program code, becomes a specifically programmed computing processor that is configured to perform at least the following operations: receiving a plurality of bio-potential signals collected at a plurality of locations on the abdomen of a pregnant mother; detecting R-wave peaks in the bio-potential signals; extracting maternal electrocardiogram (“ECG”) signals from the bio-potential signals; determining R-wave amplitudes in the maternal ECG signals; creating an R-wave amplitude signal for each of the maternal ECG signals; calculating an average of all the R-wave amplitude signals; and normalizing the average to produce an electrical uterine monitoring (“EUM”) signal. In some embodiments, the operations also include identifying at least one uterine contraction based on a corresponding at least one peak in the EUM signal.

In some embodiments, the present invention provides a method including receiving a plurality of bio-potential signals collected at a plurality of locations on the abdomen of a pregnant mother; detecting R-wave peaks in the bio-potential signals; extracting maternal ECG signals from the bio-potential signals; determining R-wave amplitudes in the maternal ECG signals; creating an R-wave amplitude signal for each of the maternal ECG signals; calculating an average of all the R-wave amplitude signals; and normalizing the average to produce an EUM signal. In some embodiments, the method also includes identifying at least one uterine contraction based on a corresponding at least one peak in the EUM signal.

In an embodiment, a computer-implemented method receiving, by at least one computer processor, a plurality of raw bio-potential inputs, wherein each of the raw bio-potential inputs being received from a corresponding one of a plurality of electrodes, wherein each of the plurality of electrodes is positioned so as to measure a respective one of the raw bio-potential inputs of a pregnant human subject; generating, by the at least one computer processor, a plurality of signal channels from the plurality of raw-bio-potential inputs, wherein the plurality of signal channels includes at least three signal channels; pre-processing, by the at least one computer processor, respective signal channel data of each of the signal channels to produce a plurality of pre-processed signal channels, wherein each of the pre-processed signal channels includes respective pre-processed signal channel data; extracting, by the at least one computer processor, a respective plurality of R-wave peaks from the pre-processed signal channel data of each of the pre-processed signal channels to produce a plurality of R-wave peak data sets, wherein each of the R-wave peak data sets includes a respective plurality of R-wave peaks; removing, by the at least one computer processor, from the plurality of R-wave peak data sets, at least one of: (a) at least one signal artifact or (b) at least one outlier data point, wherein the at least one signal artifact is one of an electromyography artifact or a baseline artifact; replacing, by the at least one computer processor, the at least one signal artifact, the at least one outlier data point, or both, with at least one statistical value determined based on a corresponding one of the R-wave peak data sets from which the at least one signal artifact, the at least one outlier data point, or both was removed; generating, by the at least one computer processor, a respective R-wave signal data set for a respective R-wave signal channel at a predetermined sampling rate based on each respective R-wave peak data set to produce a plurality of R-wave signal channels; selecting, by the at least one computer processor, at least one first selected R-wave signal channel and at least one second selected R-wave signal channel from the plurality of R-wave channels based on at least one correlation between (a) the respective R-wave signal data set of at least one first particular R-wave signal channel and (b) the respective R-wave signal data set of at least one second particular R-wave signal channel; generating, by the at least one computer processor, electrical uterine monitoring data representative of an electrical uterine monitoring signal based on at least the respective R-wave signal data set of the first selected R-wave signal channel and the respective R-wave signal data set of the second selected R-wave signal channel.

In an embodiment, a computer-implemented method also includes sharpening, by the at least one computer processor, the electrical uterine monitoring data to produce a sharpened electrical uterine monitoring signal. In an embodiment, the sharpening step is omitted if the electrical uterine monitoring data is calculated based on a selected one of the electrical uterine monitoring signal channels that is a corrupted electrical uterine signal monitoring channel. In an embodiment, a computer-implemented method also includes post-processing the sharpened electrical monitoring signal data to produce a post-processed electrical uterine monitoring signal. In an embodiment, the sharpening step includes identifying a set of peaks in the electrical uterine monitoring signal data; determining a prominence of each of the peaks; removing, from the set of peaks, peaks having a prominence that is less than at least one threshold prominence value; calculating a mask based on remaining peaks of the set of peaks; smoothing the mask based on a moving average window to produce a smoothed mask; and adding the smoothed mask to the electrical uterine monitoring signal data to produce the sharpened electrical uterine monitoring signal data. In an embodiment, the at least one threshold prominence value includes at least one threshold prominence value selected from the group consisting of an absolute prominence value and a relative prominence value calculated based on a maximal prominence of the peaks in the set of peaks. In an embodiment, the mask includes zero values outside areas of the remaining peaks and nonzero values inside areas of the remaining peaks, wherein the nonzero values are calculated based on a Gaussian function

In an embodiment, the at least one filtering step of the pre-processing step includes applying at least one filter selected from the group consisting of a DC removal filter, a powerline filter, and a high pass filter.

In an embodiment, the extracting step includes receiving a set of maternal ECG peaks for the pregnant human subject; and identifying R-wave peaks in each of the pre-processed signal channels within a predetermined time window before and after each of the maternal ECG peaks in the set of maternal ECG peaks as the maximum absolute value in each of the pre-processed signal channels within the predetermined time window.

In an embodiment, the step of removing at least one of a signal artifact or an outlier data point includes removing at least one electromyography artifact by a process including identifying at least one corrupted peak in one of the plurality of R-wave peaks data sets based on the at least one corrupted peak having an inter-peaks root mean square value that is greater than a threshold; and replacing the corrupted peak with a median value, wherein the median value is either a local median or a global median.

In an embodiment, the step of removing at least one of a signal artifact or an outlier data point includes removing at least one baseline artifact by a process including: identifying a change point in R-wave peaks in one of the plurality of R-wave peaks data sets; subdividing the one of the plurality of R-wave peaks data sets into a first portion located prior to the change point and a second portion located subsequent to the change point; determining a first root-mean-square value for the first portion; determining a second root-mean-square value for the second portion; determining an equalization factor based on the first root-mean-square value and the second root-mean-square value; and modifying the first portion by multiplying R-wave peaks in the first portion by the equalization factor.

In an embodiment, the step of removing at least one of a signal artifact or an outlier point includes removing at least one outlier in accordance with a Grubbs test for outliers.

In an embodiment, the step of generating a respective R-wave data set based on each respective R-wave peak data set includes interpolating between the R-wave peaks of each respective R-wave peak data set, and wherein the interpolating between the R-wave peaks includes interpolating using an interpolation algorithm that is selected from the group consisting of a cubic spline interpolation algorithm and a shape-preserving piecewise cubic interpolation algorithm.

In an embodiment, the step of selecting at least one first one of the R-wave signal channels and at least one second one of the R-wave signal channels includes selecting candidate R-wave signal channels from the R-wave signal channels based on a percentage of prior intervals in which each of the R-wave signal channels experienced contact issues; grouping the selected candidate R-wave signal channels into a plurality of couples, wherein each of the couples includes two of the selected candidate R-wave channels that are independent from one another; calculating a correlation value of each of the couples; and selecting, as the selected at least one first one of the R-wave signal channels and the selected at least one second one of the R-wave signal channels, the candidate R-wave signal channels of at least one of the couples based on the at least one of the couples having a correlation value that exceeds a threshold correlation value.

In an embodiment, the step of calculating the electrical uterine monitoring signal includes calculating a signal that is a predetermined percentile of the selected at least one first one of the R-wave signal channels and the selected at least one second one of the R-wave signal channels. In an embodiment, the predetermined percentile is an 80thpercentile.

In an embodiment, the statistical value is one of a local median, a global median, or a mean.

In some embodiments, computer-implemented method includes receiving, by at least one computer processor, a plurality of raw acoustic inputs, wherein each of the raw acoustic inputs being received from a corresponding one of a plurality of acoustic sensors, and wherein each of the plurality of acoustic sensors is positioned so as to measure a respective one of the raw acoustic inputs of a pregnant human subject; generating, by the at least one computer processor, a plurality of signal channels from the plurality of raw acoustic inputs, wherein the plurality of signal channels includes at least three signal channels; pre-processing, by the at least one computer processor, respective signal channel data of each of the signal channels to produce a plurality of pre-processed signal channels, wherein each of the pre-processed signal channels includes respective pre-processed signal channel data; extracting, by the at least one computer processor, a respective plurality of S1-S2 peaks from the pre-processed signal channel data of each of the pre-processed signal channels to produce a plurality of S1-S2 peak data sets, wherein each of the S1-S2 peak data sets includes a respective plurality of S1-S2 peaks; removing, by the at least one computer processor, from the plurality of S1-S2 peak data sets, at least one of: (a) at least one signal artifact or (b) at least one outlier data point, wherein the at least one signal artifact is one of a movement-related artifact or a baseline artifact; replacing, by the at least one computer processor, the at least one signal artifact, the at least one outlier data point, or both, with at least one statistical value determined based on a corresponding one of the S1-S2 peak data sets from which the at least one signal artifact, the at least one outlier data point, or both was removed, to produce a plurality of interpolated S1-S2 peak data sets; generating, by the at least one computer processor, a respective S1-S2 signal data set for a respective S1-S2 signal channel at a predetermined sampling rate based on each respective interpolated S1-S2 peak data set to produce a plurality of S1-S2 signal channels; selecting, by the at least one computer processor, at least one first selected S1-S2 signal channel and at least one second selected S1-S2 signal channel from the plurality of S1-S2 signal channels based on at least one correlation between (a) a respective S1-S2 signal data set of at least one first particular S1-S2 signal channel and (b) a respective S1-S2 signal data set of at least one second particular S1-S2 signal channel; and generating, by the at least one computer processor, acoustic uterine monitoring data representative of an acoustic uterine monitoring signal based on at least the respective S1-S2 signal data set of the first selected S1-S2 signal channel and the respective S1-S2 signal data set of the second selected S1-S2 signal channel.

In some embodiments, the computer-implemented method also includes sharpening, by the at least one computer processor, the acoustic uterine monitoring data to produce a sharpened acoustic uterine monitoring signal. In some embodiments, the computer-implemented method also includes determining, by the at least one computer processor, whether the acoustic uterine monitoring data is calculated based on based on a selected one of the acoustic uterine monitoring signal channels that is a corrupted acoustic uterine monitoring signal channel, wherein the step of sharpening, by the at least one computer processor, the acoustic uterine monitoring data is omitted if the acoustic uterine monitoring data is calculated based on the selected one of the acoustic uterine monitoring signal channels that is the corrupted acoustic uterine signal monitoring channel. In some embodiments, the computer-implemented method also includes post-processing the sharpened acoustic monitoring signal data to produce a post-processed acoustic uterine monitoring signal. In some embodiments, the step of sharpening, by the at least one computer processor, the acoustic uterine monitoring data includes: identifying a set of peaks in the acoustic uterine monitoring signal data; determining a prominence of each of the peaks; removing, from the set of peaks, peaks having a prominence that is less than at least one threshold prominence value; calculating a mask based on remaining peaks of the set of peaks; smoothing the mask based on a moving average window to produce a smoothed mask; and adding the smoothed mask to the acoustic uterine monitoring signal data to produce the sharpened acoustic uterine monitoring signal data. In some embodiments, the at least one threshold prominence value includes at least one threshold prominence value selected from a group consisting of an absolute prominence value and a relative prominence value calculated based on a maximal prominence of the peaks in the set of peaks. In some embodiments, the mask includes zero values outside areas of the remaining peaks and nonzero values inside areas of the remaining peaks, wherein the nonzero values are calculated based on a Gaussian function.

In some embodiments, the at least one filtering step of the pre-processing step includes applying at least one filter selected from a group consisting of a DC removal filter, a powerline filter, and a high pass filter.

In some embodiments, the step of extracting, by the at least one computer processor, the respective plurality of S1-S2 peaks includes: receiving a set of maternal ECG peaks for the pregnant human subject; and identifying S1-S2 peaks in each of the pre-processed signal channels within a predetermined time window before and after each of the maternal ECG peaks in the set of maternal ECG peaks as a maximum absolute value in each of the pre-processed signal channels within the predetermined time window.

In some embodiments, the step of removing at least one of a signal artifact or an outlier data point includes removing at least one movement artifact by a process including: identifying at least one corrupted peak in one of the plurality of S1-S2 peak data sets based on the at least one corrupted peak having an inter-peaks root mean square value that is greater than a threshold; and replacing the corrupted peak with a median value, wherein the median value is either a local median or a global median.

In some embodiments, the step of removing at least one of a signal artifact or an outlier data point includes removing at least one baseline artifact by a process including: identifying a change point in S1-S2 peaks in one of the plurality of S1-S2 peak data sets; subdividing the one of the plurality of S1-S2 peak data sets into a first portion located prior to the change point and a second portion located subsequent to the change point; determining a first root-mean-square value for the first portion; determining a second root-mean-square value for the second portion; determining an equalization factor based on the first root-mean-square value and the second root-mean-square value; and modifying the first portion by multiplying S1-S2 peaks in the first portion by the equalization factor.

In some embodiments, the step of removing at least one of a signal artifact or an outlier point includes removing at least one outlier in accordance with a Grubbs test for outliers.

In some embodiments, the step of generating a respective S1-S2 data set based on each respective S1-S2 peak data set includes interpolating between the S1-S2 peaks of each respective S1-S2 peak data set, and the interpolating between the S1-S2 peaks includes interpolating using an interpolation algorithm that is selected from a group consisting of a cubic spline interpolation algorithm and a shape-preserving piecewise cubic interpolation algorithm.

In some embodiments, the step of selecting at least one first one of the S1-S2 signal channels and at least one second one of the S1-S2 signal channels from the plurality of S1-S2 signal channels includes: identifying contact issues in each of the plurality of S1-S2 signal channels; determining a percentage of prior intervals in which each of the plurality of S1-S2 signal channels experienced contact issues; selecting candidate S1-S2 signal channels from the plurality of S1-S2 signal channels based on the percentage of prior intervals in which each of the plurality of S1-S2 signal channels experienced contact issues; grouping the selected candidate S1-S2 signal channels into a plurality of couples, wherein each of the couples includes two of the selected candidate S1-S2 channels that are independent from one another; calculating a correlation value of each of the couples; identifying at least one of the couples having a correlation value that exceeds a threshold correlation value; selecting, as the selected at least one first one of the S1-S2 signal channels, the first candidate S1-S2 signal channel of each of the identified at least one of the couples; and selecting, as the selected at least one second one of the S1-S2 signal channels, the second candidate S1-S2 signal channel of each of the identified at least one of the couples.

In some embodiments, the step of calculating the acoustic uterine monitoring signal includes calculating the acoustic uterine monitoring signal that is, at each point in time, a predetermined percentile of all of the selected at least one first one of the S1-S2 signal channels and the selected at least one second one of the S1-S2 signal channels at that point in time. In some embodiments, the predetermined percentile is an 80th percentile.

In some embodiments, the statistical value is one of a local median, a global median, or a mean.

