Method and system for activity/rest identification

• US 9,198,619 B2
• Filed: 07/05/2011
• Issued: 12/01/2015
• Est. Priority Date: 07/05/2011
• Status: Active Grant

• Current Assignee: Universidad de Vigo
• Original Assignee: Universidad de Vigo
• Inventors: Riobo Aboy, Pedro Mateo, Fernandez Bernardez, Jose Ramon, Mojon Ojea, Artemio, Crespo Veiga, Cristina
• Primary Examiner(s): HINDENBURG, MAX F
• Application Number:
  US13/176,716
• Publication Number:
  US 20130012836A1
• Time in Patent Office: 1,610 Days
• Field of Search: 600/595, 600/300, 600/301, 600/587, 600/549
• US Class Current: 1/1
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles
• CPC Class Codes: A61B 5/6824 Arm or wrist
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence

Abstract

Disclosed embodiments include a method for automatic identification of activity and rest periods based on an actigraphy signal. According to an embodiment, the method comprises (a) applying one or more filters to the actigraphy signal to generate a filtered actigraphy signal, and (b) comparing the filtered actigraphy signal against an adaptive threshold to generate an output signal corresponding to the activity and rest periods. The method can be implemented as part of a medical apparatus or medical system for automatic activity/rest identification from actigraphy signals.

Claims

1. A method for identification of activity and rest periods based on an actigraphy signal, said method comprising:

(a) applying one or more filters to said actigraphy signal to generate a filtered actigraphy signal using a processing hardware element; and

(b) comparing said filtered actigraphy signal against an adaptive threshold based on the expected number of sleep hours implemented using a percentile of said actigraphy signal to generate an output signal corresponding to said activity and rest periods.

2. The method of claim 1, wherein said one or more filters include at least one nonlinear filter.

3. The method of claim 2, wherein said nonlinear filter is a rank-order filter.

4. The method of claim 3, wherein said adaptive threshold is an user specified function of the expected number of sleep hours of a subject.

5. The method of claim 1, further comprising implementing a method for data validation.

6. The method of claim 5, wherein said method for data validation is based on an empirical probability model, said empirical probability model containing information about a maximum number of valid consecutive zeroes considered during activity and rest periods for a given actigrapher.

7. The method of claim 6, further comprising applying an adaptive rank-order processing and decision logic stage.

8. The method of claim 7, further comprising applying one or more morphological filters to eliminate invalid activity and rest transitions during a preprocessing stage.

9. The method of claim 8, further comprising applying one or more morphological filters to eliminate invalid activity and rest transitions during a processing and decision stage.

10. A medical system for identification of activity and rest periods based on an actigraphy signal, said medical system comprising a processor configured to perform the processing steps of:

(a) applying one or more filters to said actigraphy signal to generate a filtered actigraphy signal; and

(b) comparing said filtered actigraphy signal against an adaptive threshold based on the expected number of sleep hours implemented using a percentile of said actigraphy signal to generate an output signal corresponding to said activity and rest periods.

11. The medical system of claim 10, wherein said one or more filters include a rank-order filter and said adaptive threshold is an user specified percentile-based function of the expected number of sleep hours of a subject.

12. The medical system of claim 11, wherein said processor is further configured to perform the processing steps of a) implementing a method for data validation based on an empirical probability model containing information about the maximum number of valid consecutive zeroes during activity and rest periods for a given actigrapher and a patient population, b) applying an adaptive rank-order filter, and c) applying one or more morphological filters to eliminate invalid activity and rest transitions.

13. An apparatus for identification of activity and rest periods, comprising:

(a) a sensor configured for acquiring actigraphy signals; and

(b) a processor configured for pocking said actigraphy signals by

     1) applying one or more filters to said actigraphy signal to generate a filtered actigraphy signal, and

     2) comparing said filtered actigraphy signal against an adaptive threshold based on the expected number of sleep hours implemented using a percentile of said actigraphy signal corresponding to (h_s/24)×

100% where h_s is a user-specified parameter corresponding to an average number of sleep hours per night to generate and report an output signal corresponding to said activity and rest periods, wherein said one or more filters comprise a median rank-order filter with user-specified window lengths up to 12 hours and a closing-opening morphological filter configured to eliminate invalid sleep-awake and awake-sleep transitions, and padding said actigraphy signal prior to said applying one or more filters at its beginning and end using the maximum value of said actigraphy signal in order to bias its beginning and end towards an awake state.

14. The apparatus for identification of activity and rest periods of claim 13, wherein said processor is further configured to perform the processing steps of a) implementing a method for data validation based on an empirical probability model containing information about the maximum number of valid consecutive zeroes during activity and rest periods for a given actigrapher and a patient population, b) applying an adaptive rank-order filter, and c) applying a second morphological filter to eliminate invalid activity and rest transitions.