Systems and methods for maternal uterine activity detection

Abstract

A method includes receiving bio-potential inputs; generating signal channels from the bio-potential inputs; pre-processing data in the signal channels; extracting R-wave peaks from the pre-processed data; removing artifacts and outliers from the R-wave peaks; generating R-wave signal channels based on the R-wave peaks in the pre-processed signal channels; selecting two or more of the R-wave signal channels; and combining the selected two or more R-wave signal channels to produce an electrical uterine monitoring signal.

What is claimed is: 
     
         1 . A computer-implemented method, comprising:
 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 comprises 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 comprises 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 comprises 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.   
     
     
         2 . The computer-implemented method of  claim 1 , further comprising:
 sharpening, by the at least one computer processor, the electrical uterine monitoring data to produce a sharpened electrical uterine monitoring signal.   
     
     
         3 . The computer-implemented method of  claim 2 , wherein 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. 
     
     
         4 . The computer-implemented method of  claim 2 , further comprising:
 post-processing the sharpened electrical monitoring signal data to produce a post-processed electrical uterine monitoring signal.   
     
     
         5 . The computer-implemented method of  claim 2 , wherein the sharpening step comprises:
 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.   
     
     
         6 . The computer-implemented method of  claim 5 , wherein 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. 
     
     
         7 . The computer-implemented method of  claim 5 , wherein 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. 
     
     
         8 . The computer-implemented method of  claim 1 , wherein 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. 
     
     
         9 . The computer-implemented method of  claim 1 , wherein the extracting step comprises:
 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.   
     
     
         10 . The computer-implemented method of  claim 1 , wherein the step of removing at least one of a signal artifact or an outlier data point comprises removing at least one electromyography artifact by a process comprising:
 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.   
     
     
         11 . The computer-implemented method of  claim 1 , wherein the step of removing at least one of a signal artifact or an outlier data point comprises removing at least one baseline artifact by a process comprising:
 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.   
     
     
         12 . The computer-implemented method of  claim 1 , wherein the step of removing at least one of a signal artifact or an outlier point comprises removing at least one outlier in accordance with a Grubbs test for outliers. 
     
     
         13 . The computer-implemented method of  claim 1 , wherein the step of generating a respective R-wave data set based on each respective R-wave peak data set comprises interpolating between the R-wave peaks of each respective R-wave peak data set, and wherein the interpolating between the R-wave peaks comprises 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. 
     
     
         14 . The computer-implemented method of  claim 1 , wherein 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 comprises:
 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.   
     
     
         15 . The computer-implemented method of  claim 1 , wherein the step of calculating the electrical uterine monitoring signal comprises 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. 
     
     
         16 . The computer-implemented method of  claim 15 , wherein the predetermined percentile is an 80th percentile. 
     
     
         17 . The computer-implemented method of  claim 1 , wherein the statistical value is one of a local median, a global median, or a mean.