System and method for early detection of mild traumatic brain injury

• US 10,244,977 B2
• Filed: 11/23/2015
• Issued: 04/02/2019
• Est. Priority Date: 12/30/2010
• Status: Active Grant

• Current Assignee: University of Maine System Board of Trustees
• Original Assignee: University of Maine System Board of Trustees
• Inventors: Hayes, Marie J., Abedi, Ali
• Primary Examiner(s): Betsch, Regis J
• Application Number:
  US14/948,667
• Publication Number:
  US 20160183861A1
• Time in Patent Office: 1,226 Days
• CPC Class Codes: A61B 2562/0247 Pressure sensors
• CPC Class Codes: A61B 2562/028 Microscale sensors, e.g. electromechanical sensors [MEMS]
• CPC Class Codes: A61B 2562/164 the sensor is mounted in or on a conformable substrate or carrier
• CPC Class Codes: A61B 2562/168 Fluid filled sensor housings
• CPC Class Codes: A61B 5/002 Monitoring the patient using a local or closed circuit, e.g. in a room or building A61B5/0017 takes precedence
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• 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 5/1118 Determining activity level
• CPC Class Codes: A61B 5/4064 Evaluating the brain A61B5/031, A61B5/369, A61B5/14553 take precedence
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles
• CPC Class Codes: A61B 5/4815 Sleep quality
• CPC Class Codes: A61B 5/6892 Mats
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A wireless system and methods for non-invasive detection of brain injury that includes a detection device for reliable monitoring and analyzing of sleep movement in comparison with normal sleep movement architecture to identify dampened sleep movement patterns indicative of brain injury.

Claims

1. A device for detecting brain injury by measuring sleep movement parameters comprising:

a plurality of sensors, each operative at a sampling rate of at least 10 Hz to generate a plurality of temporally-associated data signals relating to sleep movements of a subject, said sensors arranged to provide both integrated and spatially segregated movement estimation for said subject, at least some of said sensors having a sensitivity capable of detecting load variances as small as 1 g;

a signal conditioning module including a conditioning circuit in communication with said plurality of sensors, said signal conditioning module operative to condition said data signals;

a processor in communication with said signal conditioning module, said processor operative to analyze said conditioned data, said processor having a module programmed for parsing the data into a plurality of bins of defined temporal duration, at least some of which bins are 60 seconds or less in duration, and for comparing the average values of each bin to differentiate among (a) bouts of quiescence, (b) bouts of macro-structural movements indicative of sleep or wake cycles, or sleep fragmentation; and

(c) bouts of micro-structural movements, the micro-structural movements being useful to detect brain injury; and

a non-inverting resistor-ladder amplifier as an interface between each of the plurality of sensors and the processor.

2. The device according to claim 1 wherein said plurality of sensors are arranged in a two dimensional array across a sleep surface, said array of sensors being in communication with said signal conditioning module.

3. The device according to claim 2 wherein said sensors are grouped in pairs of sensors at each location and further wherein each sensor pair includes two sensors having different sensitivity ranges to improve dynamic range.

4. The device according to claim 3 further including an analog to digital converter that is operative to convert analog signals obtained from said sensor array into a digital data stream for analysis by said processor.

5. The device according to claim 4 further including a wireless transmitter in communication with said analog to digital converter and a wireless receiver in communication with said processor, said wireless transmitter operative to transfer said digital data stream to said wireless transmitter for analysis by said processor.

6. The device according to claim 5 wherein said wireless receiver utilizes a method of Offset Quadrature Phase Shift Keying (OQPSK) Modulation.

7. The device according to claim 2 further including auxiliary sensors that measure at least one of heart rate and respiration, said auxiliary sensors being situated in close communication with said sleeping surface, and in communication with said signal conditioning module.

8. The device according to claim 1 wherein said processor is operative to identify within said microstructural movements a dampened pattern of sleep movement vigor characterized by at least one diminished parameter selected from a group consisting of movement frequency, vigor, bout organization, bout duration, amplitude, burst, and actigraphy-based measures of sleep fragmentation when compared to a value derived from sleep movement characteristics of a population without brain injury.

9. The device according to claim 8 wherein said processor is further operative to assign a conclusion of brain injury to a subject demonstrating said dampened pattern of sleep movement.

10. The device according to claim 1 further including a module programmed for a de-noising operation that utilizes a Fast Fourier Transformation, an Inverse Fast Fourier Transformation, and Probability Density estimation to remove noise from the sensor signals.

11. The device according to claim 1 wherein said processor has a module programmed for:

(i) parsing the data into sets of bins of defined temporal duration, at least some bin sets being 60 seconds or less in duration, and other bin sets being more than 60 seconds in duration;

(ii) analyzing the data from bins that are more than 60 seconds in duration for bouts of quiescence and bouts of macro-structural movements indicative of sleep or wake cycles; and

(iii) analyzing the data from bins that are 60 seconds or less for bouts of micro-structural movements indicative of brain injury.

12. The device according to claim 1, wherein at least one of the sensors comprises a piezoelectric based passive pressure sensor in a metal-carbon sheet covered with a vinyl.

13. A method of testing a subject for brain injury comprising the steps of:

(a) monitoring the sleep movements of a subject using movement sensors operable at a sampling rate of at least 10 Hz to generate a plurality of temporally-associated data signals relating to sleep movements, wherein an output of the movement sensors is converted in a resistance-to-voltage amplifier;

(b) parsing the data at least once into a plurality of bins of defined temporal duration of 60 seconds or less, and determining an average value for each bin to differentiate bouts of micro-structural movements from bouts of quiescence and bouts of macro-structural movements;

(c) comparing the values for bouts of micro-structural movements to a threshold value that is indicative of brain injury; and

(d) assigning a determination of brain injury according to the outcome of the comparison.

14. The method of claim 13, further comprising:

parsing the data an additional time into sets of bins of defined temporal duration of more than 60 seconds in duration; and

analyzing the data from bins that are more than 60 seconds in duration for bouts of quiescence and bouts of macro-structural movements indicative of sleep or wake cycles.

15. The method of claim 14 wherein the data from a period identified as sleep fragmentation is used in a determination of brain injury.

16. The method of claim 14, further comprising discarding the sleep movement data binned in bins of duration of more than 60 seconds when the data indicates bouts of quiescence or bouts of macro-structural movements.

17. The method according to claim 13 wherein step (a) also includes monitoring at least one of heart rate and respiration for the subject and further wherein step (b) also includes modifying the determined value as a function of at least one of heart rate and respiration.

18. The method according to claim 13 further comprising identifying a period of 1-3 hours during which the data signals include no movements indicative of frank awakenings, and wherein step (b) includes the following sub-steps applied to the data of said period:

(b1) calculate a grand mean value for the data generated in step (a);

(b2) compare each bin to the grand mean and assign a corresponding binary value to each bin;

(b3) set movement bout onset/offset criteria; and

(b4) calculate mean and maximum durations for spontaneous microstructural movement bouts.

19. The method according to claim 18 wherein sub-step (b2) includes assigning a zero binary value when the bin is less than the grand mean and a unity binary value when the bin is greater than the grand mean.

20. The method of claim 13 wherein the determination of brain injury is made based on an analysis of bout frequency, bout duration, and interbout interval of microstructural sleep movements.

21. The method according to claim 13 wherein the bins of defined temporal duration of 60 seconds or less include bins of 1 to 12 seconds to obtain estimates for fast-cycle micro-structural movements.

22. The method according to claim 13, wherein the movement sensors are capable of being sampled at 200,000 samples per second.