SYSTEMS AND METHODS FOR REAL-TIME SIGNAL PROCESSING AND FITTING

• US 20160353996A1
• Filed: 06/06/2016
• Published: 12/08/2016
• Est. Priority Date: 06/05/2015
• Status: Active Grant

• Current Assignee: Arizona Board of Regents (University of Arizona)
• Original Assignee: Arizona Board of Regents (University of Arizona)
• Inventors: Fink, Wolfgang
• Granted Patent: US 11,766,182 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 3/16 for measuring intraocular pressure, e.g. tonometers
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/259 using conductive adhesive means, e.g. gels
• CPC Class Codes: A61B 5/28 for electrocardiography [ECG]
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7235 Details of waveform analysis A61B5/349 takes precedence
• CPC Class Codes: A61B 8/00 Diagnosis using ultrasonic, sonic or infrasonic waves imaging of objects using sonar G01S15/00

Abstract

Various examples of methods and systems are provided for real-time signal processing. In one example, a method for processing data to select a pattern includes receiving data via a sensor, evaluating the data including waveforms over a time domain, averaging the waveforms to obtain a mean waveform, selecting a pattern based on the mean waveform, and generating a notification regarding the selected pattern. The pattern can include a start time, a hold time, and an end time. In another example, a system includes one or more sensors that detect the data and a mobile platform that evaluates the data, averages the waveforms to obtain the mean waveform and selects a pattern based on the mean waveform. A user interface can be used to communicate the notification regarding the selected pattern. The patterns can include breathing patterns, which can be used to reduce stress in a subject being monitored by the sensor.

Claims

1. A method for processing data to select a pattern, the method comprising:

receiving data via one or more sensors;

executing instructions stored in memory, where execution of the instructions by a processor;

evaluates the received data over a time domain, where the received data evaluated over the time domain comprises waveforms,

averages the waveforms to obtain a mean waveform, and

selects a pattern based on the mean waveform, where the selected pattern comprises a start time, a hold time, and an end time; and

generating a notification regarding the selected pattern.

2. The method of claim 1, wherein the data comprises at least one of respiratory data, intraocular pressure data, blood sugar level data, blood oxygenation data, temperature data, radar data, sonar data, or lidar data.

3. The method of claim 1, wherein the one or more sensors are part of a wearable device.

4. The method of claim 1, wherein the pattern is a breathing pattern of a subject.

5. The method of claim 1, wherein at least some of the data is evaluated over the time domain in real-time.

6. The method of claim 1, further comprising identifying one or more peaks or valleys in the waveforms, wherein a space between two consecutive valleys represents a cycle.

7. The method of claim 1, further comprising processing each waveform to remove effects of noise and defects.

8. The method of claim 7, wherein processing the waveform comprises:

identifying one or more significant coefficients in a frequency domain, and

removing any coefficients that are not identified as significant in the frequency domain, wherein removing the coefficients not identified as significant results in a smoothed waveform.

9. The method of claim 8, further comprising identifying one or more peaks and valleys in the smoothed waveforms, wherein a space between two consecutive valleys represents a cycle.

10. A system for processing data to select a pattern, the system comprising:

one or more sensors, where each sensor detects data;

a mobile platform comprising a processor that executes instructions stored in memory, where execution of the instructions by the processor;

evaluates at least a portion of the data over a time domain, where the portion of the data evaluated over the time domain comprises waveforms,

averages the waveforms to obtain a mean waveform, and

selects a pattern based on the mean waveform, where the pattern comprises a start time, a hold time, and an end time; and

a user interface that communicates a notification regarding the selected pattern.

11. The system of claim 10, wherein the one or more sensors are part of a wearable device.

12. The system of claim 10, wherein the one or more sensors provide at least the portion of the data in real-time.

13. The system of claim 10, wherein the one or more sensors include at least one of an impedance pneumograph sensor, intraocular pressure sensor, blood sugar sensor, blood oxygenation sensor, temperature sensor, radar, sonar, and lidar.

14. The system of claim 10, wherein the processor executes instructions to identify one or more peaks or valleys in the waveforms, wherein a space between two consecutive valleys represents a cycle.

15. The system of claim 14, wherein the one or more peaks or valleys are identified based upon Root Mean Square Error (RMSE) analysis of the waveforms.

16. The system of claim 10, wherein the processor executes instructions to process each waveform to remove effects of noise and defects.

17. The system of claim 16, wherein processing the waveform comprises:

identifying one or more significant coefficients in a frequency domain, and

removing any coefficients that are not identified as significant in the frequency domain, wherein removing the coefficients not identified as significant results in a smoothed waveform.

18. The system of claim 10, wherein the mobile platform is remote from the one or more sensors, and the one or more sensors provide at least the portion of the data to the mobile platform via a wireless communication link.

19. The system of claim 10, wherein the notification is provided graphically via a display screen of the mobile platform or audibly via a speaker of the mobile platform.

20. The system of claim 10, wherein the pattern is a breathing pattern of a subject monitored by the one or more sensors.

21. A method for processing data to identify a pattern, the method comprising:

receiving data via one or more sensors;

executing instructions stored in memory, where execution of the instructions by a processor;

evaluates the received data over a time domain, where the received data evaluated over the time domain comprises waveforms,

compares window-shifted segments of the received data to at least one defined motif, and

determines an onset of the at least one defined motif in the received data based upon the comparison; and

generating a characteristic of the pattern based at least in part upon the onset of the at least one defined motif.

22. The method of claim 21, wherein the at least one motif corresponds to a specified condition associated with the received data.

23. The method of claim 22, wherein the onset of the at least one motif is determined based upon a logarithm of a Root Mean Square Error (RMSE) between the at least one defined motif and the window-shifted segments of the received data.