Sensor-based systems and methods for monitoring maternal position and other parameters

• US 10,265,010 B2
• Filed: 04/25/2016
• Issued: 04/23/2019
• Est. Priority Date: 04/25/2015
• Status: Active Grant

• Current Assignee: Leaf Healthcare, Inc. (Smith & Nephew PLC)
• Original Assignee: Leaf Healthcare, Inc. (Smith & Nephew PLC)
• Inventors: Larson, Barrett J.
• Primary Examiner(s): Holmes, Rex R
• Application Number:
  US15/137,426
• Publication Number:
  US 20160310062A1
• Time in Patent Office: 1,093 Days
• CPC Class Codes: A61B 2503/02 Foetus
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 2560/0475 Special features of memory means, e.g. removable memory cards
• CPC Class Codes: A61B 5/0011 Foetal or obstetric data
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/02411 of foetuses
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/1114 Tracking parts of the body
• CPC Class Codes: A61B 5/1116 Determining posture transitions
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/1121 Determining geometric values, e.g. centre of rotation or angular range of movement
• CPC Class Codes: A61B 5/344 Foetal cardiography
• CPC Class Codes: A61B 5/4343 Pregnancy and labour monitoring, e.g. for labour onset detection
• CPC Class Codes: A61B 5/4362 Assessing foetal parameters
• CPC Class Codes: A61B 5/6823 Trunk, e.g., chest, back, abdomen, hip
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/746 : Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A61B 5/7465 : Arrangements for interactive communication between patient and care services, e.g. by using a telephone network telemetry of measured physiological signal A61B5/0002
• CPC Class Codes: G16H 80/00 : ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring

Abstract

Sensor-based systems, devices, and methods are disclosed for monitoring for an aortocaval compression condition in a pregnant woman. A sensor device may be secured directly or indirectly to the woman, which may collect various sensor data related to the woman and/or the fetus. The device may determine a physical orientation of the woman (e.g., upright and/or lateral tilt angles of the woman) and/or biometric parameter(s) of the woman and/or fetus based on the collected sensor data, identify an aortocaval compression condition based on the determined orientation of the woman and/or biometric parameter(s) of the woman and/or fetus, and generate a repositioning alert via an alert mechanism. The disclosed concepts may be implemented, for example, as a personal home-use device, or as a broader system in a hospital or other medical facility, e.g., for monitoring and displaying aortocaval compression related information for multiple patients and/or facilitating patient turn protocols.

Claims

1. A monitoring system for monitoring for an aortocaval compression condition, the monitoring system comprising:

one or more sensors configured to be secured directly or indirectly to the user and configured to generate sensor signals;

a processor;

computer instructions stored in non-transitory computer-readable medium and executable by the processor to;

receive the sensor signals generated by the one or more sensors;

determine a physical orientation of the user based on the received sensor signals;

determine heart rate variability (HRV) data based on the received sensor signals, the heart rate variability data indicating at least one of (a) a heart rate variability of the user or (b) a heart rate variability of the fetus;

identify an aortocaval compression condition based at least on the determined physical orientation of the user and the determined HRV data; and

automatically generate and transmit an alert signal in response to identifying the aortocaval compression condition; and

an alert mechanism configured to receive the alert signal and output an alert to the user.

2. The monitoring system of claim 1, wherein the monitoring system comprises a self-contained device including the one or more sensors, the processor, and the non-transitory computer-readable medium storing the computer instructions.

3. The monitoring system of claim 1, wherein the HRV data determined based on the received sensor signals indicates a heart rate variability of the fetus.

4. The monitoring system of claim 1, wherein the HRV data determined based on the received sensor signals indicates a heart rate variability of the user.

5. The monitoring system of claim 1, wherein the computer instructions executable by the processor to determine HRV data based on the received sensor signals comprise computer instructions executable by the processor to determine HRV data based on a power spectra across a series of R-R intervals using a fast Fourier transformation.

6. The monitoring system of claim 1, wherein the computer instructions executable by the processor to determine HRV data based on the received sensor signals comprise computer instructions executable by the processor to determine HRV data using fractal dimensions.

7. The monitoring system of claim 1, wherein the computer instructions executable by the processor to determine HRV data based on the received sensor signals comprise computer instructions executable by the processor to determine HRV data using discrete wavelet transformations.

8. The monitoring system of claim 1, wherein the computer instructions executable by the processor to determine HRV data based on the received sensor signals comprise computer instructions executable by the processor to determine HRV data from an EKG signal.

9. The monitoring system of claim 1, wherein the computer instructions executable by the processor to identify the aortocaval compression condition based at least on the determined physical orientation of the user and the determined HRV data comprise computer instructions executable by the processor to:

determine a change in the HRV data corresponding with an indication of aortocaval compression; and

identify the aortocaval compression condition based on the determined change in the HRV data.

10. The monitoring system of claim 1, wherein the computer instructions executable by the processor to identify the aortocaval compression condition based at least on the determined physical orientation of the user and the determined HRV data comprise computer instructions executable by the processor to:

dynamically adjust an HRV threshold value over time;

compare the HRV data with the dynamically adjusted HRV threshold value over time; and

identify the aortocaval compression condition based on the comparison.

11. The monitoring system of claim 8, wherein the computer instructions executable by the processor to identify the aortocaval compression condition based at least on the determined physical orientation of the user and the determined HRV data comprise computer instructions executable by the processor to:

determine an activity level of the user based on the received sensor signals;

dynamically adjust an HRV threshold value over time based on the determined activity level of the user;

compare the HRV data with the dynamically adjusted HRV threshold value over time; and

identify the aortocaval compression condition based on the comparison.