System and method for early detection of mild traumatic brain injury

• US 9,192,333 B1
• Filed: 05/12/2011
• Issued: 11/24/2015
• 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: Abedi, Ali, Hayes, Marie
• Primary Examiner(s): BETSCH, REGIS J
• Application Number:
  US13/106,451
• Time in Patent Office: 1,657 Days
• Field of Search: 702/19
• US Class Current: 1/1
• 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 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;

(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; and

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 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;

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

(b4) calculate mean and maximum durations for spontaneous micro-structural movement bouts,

(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.

2. The method of claim 1, 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.

3. The method of claim 2 wherein the data from a period identified as sleep fragmentation is used in a determination of brain injury.

4. The method according to claim 1 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.

5. The method of claim 1 wherein the determination of brain injury is made based on an analysis of bout frequency, bout duration, and interbout interval of microstructural sleep movements.

6. The method according to claim 1 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.

7. The method of claim 2, 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.