SYSTEMS AND METHODS FOR DETECTING A LABOR CONDITION
Systems and methods for monitoring the onset or occurrence of labor contractions and detecting or estimating labor in a pregnant female are provided.
1 . A system for identifying a labor state in a pregnant female, the system comprising:
a patch coupled to an abdominal region of the pregnant female;
a physiological sensor coupled to the patch or integrated in the patch;
a processor communicatively coupled to the physiological sensor; and
a computer-readable medium having non-transitory, processor-executable instructions stored thereon, wherein execution of the instructions causes the processor to perform a method comprising:
acquiring a physiological signal from the physiological sensor;
processing the physiological signal to identify and extract a parameter of interest from the physiological signal; and
analyzing the parameter of interest to determine whether the parameter is indicative of a labor state.
2 . The system of claim 1 , wherein the method performed by the processor further comprises developing a personalized parameter baseline.
3 . The system of claim 2 , wherein the parameter of interest is tracked over time to develop the personalized parameter baseline.
4 . The system of claim 2 , wherein a plurality of parameters of interest are identified and extracted from the physiological signal, and
wherein analyzing the parameter of interest to determine whether the parameter is indicative of a labor state comprises: comparing the parameter of interest to the personalized parameter baseline to identify a deviation from the personalized parameter baseline, and determining whether the deviation is indicative of the labor state.
5 . The system of claim 4 , wherein analyzing the parameter of interest to determine whether the parameter is indicative of a labor state comprises: identifying a pattern in the plurality of parameters, and determining whether the pattern is indicative of the labor state.
6 . The system of claim 4 , wherein the plurality of parameters comprise physiological and behavioral parameters.
7 . The system of claim 1 , wherein analyzing the parameter of interest to determine whether the parameter is indicative of a labor state comprises feeding the parameter into a machine learning model trained to detect labor.
8 . The system of claim 7 , wherein the machine learning model comprises one or more of a generalized linear model, a decision tree, a support vector machine, a k-nearest neighbor, a neural network, a deep neural network, a random forest, and a hierarchical model.
9 . The system of claim 1 , wherein analyzing the parameter of interest to determine whether the parameter is indicative of the labor state comprises comparing the parameter to community data stored in a database.
10 . The system of claim 9 , wherein the community data comprises one or more of: recorded trends, rules, correlations, and observations generated from tracking, aggregating, and analyzing parameters from a plurality of users.
11 . The system of claim 1 , wherein the physiological sensor comprises a measurement electrode and reference electrode.
12 . The system of claim 1 , wherein the physiological sensor comprises one or more physiological sensors configured to measure one or more of an electrohysterography signal, a biopotential signal, maternal uterine activity, maternal uterine muscle contractions, maternal heart electrical activity, maternal heart rate, fetal movement, fetal heart rate, maternal activity, maternal stress, and fetal stress.
13 . The system of claim 1 , wherein the parameter of interest comprises one or more of a maternal heart rate metric, a maternal heart rate variability metric, a fetal heart rate metric, a fetal heart rate variability metric, a range of an electrohysterography signal, a power of an electrohysterography signal in a specific frequency band, a frequency feature of an electrohysterography signal, a time-frequency feature of an electrohysterography signal, a frequency of contractions, a duration of contractions, and an amplitude of contractions.
14 . The system of claim 1 , wherein the patch comprises a portable sensor module coupled to the patch or integrated into the patch, wherein the sensor module comprises the physiological sensor, the processor, and the computer-readable medium and further comprises an electronic circuit and a wireless antenna, and wherein the sensor module is in wireless communication with a mobile computing device.
15 . The system of claim 1 , wherein the method performed by the processor further comprises generating an alert.
16 . The system of claim 1 , wherein the method performed by the processor further comprises determining a probability that the pregnant female is experiencing labor-inducing contractions.
17 . The system of claim 16 , wherein the method performed by the processor further comprises determining a degree of certainty around the determined probability.
18 . The system of claim 1 , wherein the method performed by the processor further comprises determining a probability that the pregnant female will enter the labor state within a given time period.
19 . The system of claim 1 , wherein the method performed by the processor further comprises determining an estimate of time until the pregnant female enters the labor state.
20 . A computer-implemented method for identifying a labor state in a pregnant female, the method comprising:
acquiring a physiological signal from a physiological sensor, wherein the physiological sensor is coupled to a patch or integrated into the patch, wherein the patch is configured to be coupled to an abdominal region of the pregnant female;
processing the physiological signal to identify and extract a parameter of interest from the physiological signal; and
analyzing the parameter of interest to determine whether the parameter is indicative of a labor state.