US-20260123878-A1 - FUSION SIGNAL PROCESSING FOR MATERNAL UTERINE ACTIVITY DETECTION

US20260123878A1US 20260123878 A1US20260123878 A1US 20260123878A1US-20260123878-A1

Abstract

A computer-implemented method includes providing, by at least one computer processor, a plurality of signal channels, wherein the plurality of signal channels includes a plurality of electrical uterine monitoring signal channels and a plurality of acoustic uterine monitoring signal channels; determining, by the at least one computer processor, a plurality of channel weights, wherein each of the channel weights corresponds to a particular one of the signal channels; and generating, by the at least one computer processor, a combined uterine monitoring signal channel by calculating a weighted average of the signal channels based on the channel weight for each of the signal channels.

Inventors

Muhammad Mhajna

Assignees

NUVO INT'L GROUP INC.

Dates

Publication Date: 20260507
Application Date: 20250908

Claims (15)

1 . A computer-implemented method, comprising: providing, by at least one computer processor, a plurality of signal channels, wherein the plurality of signal channels includes a plurality of electrical uterine monitoring signal channels and a plurality of acoustic uterine monitoring signal channels; determining, by the at least one computer processor, a plurality of channel weights, wherein each of the channel weights corresponds to a particular one of the signal channels; and generating, by the at least one computer processor, a combined uterine monitoring signal channel by calculating a weighted average of the signal channels based on the channel weight for each of the signal channels.
2 . The computer-implemented method of claim 1 , wherein the plurality of channel weights are determined based on a machine learning algorithm.
3 . The computer-implemented method of claim 2 , wherein the machine learning algorithm includes a gradient descent optimization process.
4 . The computer-implemented method of claim 1 , wherein the plurality of channel weights are determined by a process comprising: defining, by the at least one computer processor, a plurality of channel sets, each of the plurality of channel sets including at least some of the plurality of signal channels; defining, by the at least one computer processor, a plurality of initial weight sets, wherein each of the plurality of initial weight sets corresponds to a particular one of the plurality of channel sets; optimizing, by the at least one computer processor, the plurality of initial weight sets to generate a plurality of optimized weight sets, wherein each of the plurality of optimized weight sets corresponds to a particular one of the plurality of channel sets; and selecting, by the at least one computer processor, a best one of the plurality of optimized weight sets as the plurality of channel weights.
5 . The computer-implemented method of claim 4 , wherein the step of optimizing the plurality of initial weight sets includes a gradient descent process.
6 . The computer-implemented method of claim 4 , wherein the step of selecting the best one of the plurality of optimized weight sets is performed by a process comprising: generating, by the at least one computer processor, a plurality of interim uterine activity traces, wherein each of the plurality of interim uterine activity traces corresponds to a particular one of the plurality of optimized weight sets; calculating, by the at least one computer processor, for each of plurality of optimized weight sets, (a) signal-to-noise ratio of the particular one of the interim uterine activity traces that corresponds to each of the plurality of optimized weight sets; (b) a cost function; (c) a contraction confidence measure; and (d) a difference index; calculating, by the at least one computer processor, for each particular one of the plurality of optimized weight sets, an optimized weight set mean that is a mean of (a) the signal-to-noise ratio of the particular one of the optimized weight sets, (b) the cost function of the particular one of the optimized weight sets, (c) the contraction confidence measure of the particular one of the optimized weight sets, and (d) the difference index of the particular one of the optimized weight sets; and selecting, by the at least one computer processor, a one of the plurality of optimized weight sets having a best optimized weight set mean as the best one of the plurality of optimized weight sets.
7 . The computer-implemented method of claim 4 , further comprising: generating, by the at least one computer processor, a first interim uterine activity trace and a second interim uterine activity trace corresponding to a particular one of the plurality of channel sets, wherein the first interim uterine activity trace corresponds to a first one of the plurality of optimized weight sets for the particular one of the plurality of channel sets, and wherein the second interim uterine activity trace corresponds to a second one of the plurality of optimized weight sets for the particular one of the plurality of channel sets; calculating, by the at least one computer processor, for the first one of the plurality of optimized weight sets, (a) signal-to-noise ratio of the first interim uterine activity trace; (b) a cost function; (c) a contraction confidence measure; and (d) a difference index; calculating, by the at least one computer processor, for the second one of the plurality of optimized weight sets, (a) signal-to-noise ratio of the second interim uterine activity trace; (b) a cost function; (c) a contraction confidence measure; and (d) a difference index; calculating, by the at least one computer processor, for the first one of the plurality of optimized weight sets, a first mean that is a mean of (a) the signal-to-noise ratio of the first interim uterine activity trace; (b) the cost function of the first one of the plurality of optimized weight sets; (c) a contraction confidence measure of the first one of the plurality of optimized weight sets; and (d) a difference index of the first one of the plurality of optimized weight sets; calculating, by the at least one computer processor, for the second one of the plurality of optimized weight sets, a second mean that is a mean of (a) the signal-to-noise ratio of the second interim uterine activity trace; (b) the cost function of the second one of the plurality of optimized weight sets; (c) a contraction confidence measure of the second one of the plurality of optimized weight sets; and (d) a difference index of second first one of the plurality of optimized weight sets; selecting, by the at least one computer processor, the first one of the plurality of weight sets as a best weight set for the particular one of the plurality of channel sets, based on a determination that the first mean is better than the second mean; and selecting, by the at least one computer processor, the second one of the plurality of weight sets as a best weight set for the particular one of the plurality of channel sets, based on a determination that the second mean is better than the first mean.
8 . The computer-implemented method of claim 7 , further comprising: enhancing, by the at least computer processor, the plurality of channel sets prior to the step of selecting, by the at least one computer processor, the best one of the plurality of optimized weight sets as the plurality of channel weights.
9 . The computer-implemented method of claim 4 , wherein the step of defining the plurality of channel sets comprises defining a contraction-based channel set, and wherein the contraction-based channel set is determined by a process comprising: identifying, by the at least one computer processor, a set of contractions in each of the plurality of signal channels; extracting, by the at least one computer processor, contraction features for each of the plurality of signal channels based on the set of contractions identified for each of the plurality of signal channels; clustering, by the at least one computer processor, the plurality of signal channels into a plurality of clusters; and selecting, by the at least one computer processor, a best one of the plurality of clusters as the contraction-based channel set.
10 . The computer-implemented method of claim 9 , wherein the step of defining the plurality of channel sets further comprises: improving, by the at least one computer processor, the best one of the plurality of clusters.
11 . The computer-implemented method of claim 9 , wherein the step of defining the plurality of channel sets further comprises: adding, by the at least one computer processor, to the best one of the plurality of clusters, a portion of one of the signal channels that is not included in the best one of the plurality of clusters.
12 . The computer-implemented method of claim 9 , wherein the step of identifying the set of contractions in each of the plurality of signal channels is performed by a process that includes, for each one of the plurality of signal channels: generating, by the at least one computer processor, an enhanced version of the one of the plurality of signal channels; detecting, by the at least one computer processor, a candidate set of contractions in the enhanced one of the plurality of signal channels, wherein the candidate set of contractions includes a plurality of candidate contractions; calculating, by the at least one computer processor, a plurality of confidence measures for each candidate contraction; and removing, by the at least one computer processor, at least one of the candidate contractions from the set of candidate contractions based on the confidence measures corresponding to the at least one eliminated one of the candidate contractions, thereby producing the set of contractions.
13 . The computer-implemented method of claim 4 , wherein the step of defining, by the at least one computer processor, the plurality of initial weight sets comprises generating, by the at least one computer processor, for each of the channel sets, a channel-voting weight set and a born-equal weight set.
14 . The computer-implemented method of claim 1 , wherein the step of providing the plurality of signal channels includes generating at least one of the plurality of electrical uterine monitoring signal channels, and wherein the at least one of the plurality of electrical uterine monitoring signal channels is generated by a process comprising: receiving, by the 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, to produce a plurality of interpolated R-wave peak data sets; 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 interpolated 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 signal channels based on at least one correlation between (a) a respective R-wave signal data set of at least one first particular R-wave signal channel and (b) a respective R-wave signal data set of at least one second particular R-wave signal channel; and 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, thereby producing the at least one electrical uterine monitoring signal channel.
15 . The computer-implemented method of claim 1 , wherein the step of providing the plurality of signal channels includes generating at least one of the plurality of acoustic uterine monitoring signal channels, and wherein the at least one of the plurality of acoustic uterine monitoring signal channels is generated by a process comprising: receiving, by the 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, 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 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 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 comprises 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, thereby producing the at least one acoustic uterine monitoring signal channel.

Description

CROSS-REFERENCE TO RELATED APPLICATION This application is a Continuation of U.S. patent application Ser. No. 18/623,247, filed Apr. 1, 2024, and entitled “FUSION SIGNAL PROCESSING FOR MATERNAL UTERINE ACTIVITY DETECTION,” which is a Continuation of U.S. patent application Ser. No. 18/169,693, filed Feb. 15, 2023, and entitled “FUSION SIGNAL PROCESSING FOR MATERNAL UTERINE ACTIVITY DETECTION,” which is a Continuation of U.S. patent application Ser. No. 17/168,999, filed Feb. 5, 2021, and entitled “FUSION SIGNAL PROCESSING FOR MATERNAL UTERINE ACTIVITY DETECTION,” which is a Section 111(a) application relating to and claiming the benefit of commonly-owned, co-pending U.S. Provisional Patent Application No. 62/970,585, filed on Feb. 5, 2020 and entitled “FUSION SIGNAL PROCESSING FOR MATERNAL UTERINE ACTIVITY DETECTION,” the contents of which are incorporated herein by reference in their entirety. FIELD OF THE INVENTION The invention relates generally to monitoring of expectant mothers. More particularly, the invention relates to analysis of sensed bio-potential data and/or 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. 1A shows an illustration of a relaxed uterus, in which the muscular wall of the uterus is relaxed. FIG. 1B shows 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 i