Einzeltreffer — DigiBib

Systems and methods are provided for quantitatively and objectively characterizing sleep architecture in normal individuals and persons with various health conditions. Embodiments of the invention facilitate characterizing temporal-pattern information of an individual's sleep, such as measured by electroencephalography (EEG), for identifying persons with abnormalities in the temporal-pattern information, sequences or durations of their stages of sleeping (“sleep architecture”), for facilitating selecting appropriate therapy or treatment, and for monitoring the effectiveness of such therapy or treatment. In one aspect, a set of time series are formed by electronically representing and storing information pertaining to brain activity, such as EEG hypnogram or sleep information, over a multi-night span. Information for the timeseries is analyzed, using one or more models, such as nonlinear, self-excited threshold autoregressive (SETAR) or neural network models, for determining a measure of chaotic properties of the timeseries. The largest Lyapunov exponent (LLE) is determined for the time series. Statistical departures of a particular patient's LLE values from one or more reference ranges are determined.

Titel:	Characterizing sleep architecture
Autor/in / Beteiligte Person:	Cerner Innovation, Inc.
Link:	View record in USPTO Patent Grants (Volltext)
Veröffentlichung:	2018
Medientyp:	Patent
Sonstiges:	Nachgewiesen in: USPTO Patent Grants Sprachen: English Patent Number: 10098,582 Publication Date: October 16, 2018 Appl. No: 14/279750 Application Filed: May 16, 2014 Assignees: Cerner Innovations, Inc. (Kansas City, KS, US) Claim: 1. A non-transitory computer-readable media having computer-executable instructions embodied thereon that, when executed by a processor, cause a method of characterizing sleep architecture to be performed, the method comprising: acquiring a first set of sleep-related information for a target user via one or more sensors located in proximity to the target user; receiving a second set of sleep-related information associated with a reference population and including a range for largest Lyapunov exponent (LLE) values associated with the reference population; based on the first set of sleep-related information, determining a set of timeseries; based on the set of timeseries, determining an LLE value for each timeseries in the set of timeseries, thereby generating a set of LLE values; performing a comparison of the set of LLE values to a threshold to determine whether the target user is statistically different than the reference population based on the range for the LLE values associated with the reference population; based on the comparison determining that the target user is not statistically different than the reference population, designating the target user as associated with the reference population; and causing notification to be provided via an interface to a caregiver that the target user is associated with the reference population. Claim: 2. The computer-readable media of claim 1 , wherein the threshold is determined based on the range for largest Lyapunov exponent (LLE) values associated with the reference population, and wherein the target user is determined as not statistically different where the set of LLE values is less than the threshold. Claim: 3. The computer-readable media of claim 1 , wherein the reference population comprises a population of individuals of similar age and gender as the target user. Claim: 4. The computer-readable media of claim 1 , wherein the reference population comprises a population of individuals having a condition affecting sleep. Claim: 5. A non-transitory computer-readable media having computer-executable instructions embodied thereon that, when executed by one or more computing devices, cause a method of characterizing sleep architecture to be performed, the method comprising: acquiring a first set of sleep-related information for a target user via one or more sensors located in proximity to the target user, wherein the one or more sensors sense one or more of electroencephalography signal activity or muscle activity; receiving a second set of sleep-related information associated with a reference population and including a range for largest Lyapunov exponent (LLE) values associated with the reference population; based on the first set of sleep-related information, determining a set of timeseries; based on the set of timeseries, determining an LLE value for each timeseries in the set of timeseries, thereby generating a set of LLE values; performing a threshold comparison to determine whether the set of LLE values is statistically different than the range for LLE values associated with the reference population; based on the comparison determining a statistical departure of the set of LLE values from the range, designating the target user as not associated with the reference population; and causing notification to be provided via an interface to a caregiver that the target user is not associated with the reference population. Claim: 6. The computer-readable media of claim 5 , wherein the reference population comprises a population of individuals of similar age and gender as the target user. Claim: 7. The computer-readable media of claim 5 , further comprising determining whether a condition of the target user is progressing or regressing by performing a trend analysis on the set of LLE values. Claim: 8. The computer-readable media of claim 5 , wherein determining an LLE value for each timeseries in the set of timeseries comprises using a Kantz algorithm. Claim: 9. The computer-readable media of claim 8 , further comprising adding random noise to values of each timeseries, prior to determining the LLE value for each timeseries. Claim: 10. The computer-readable media of claim 5 , further comprising: based on the determined set of timeseries, generating a set of non-linear timeseries; and determining the LLE value for each non-linear timeseries in the set of non-linear timeseries. Claim: 11. The computer-readable media of claim 10 , wherein the non-linear timeseries comprises one of a self-excited threshold autoregressive or neural network model. Claim: 12. A method of characterizing sleep architecture, the method comprising: with one or more computing devices: acquiring a first set of sleep-related information for a target user via one or more sensors located in proximity to the target user; receiving a second set of sleep-related information associated with a reference population and including a range for largest Lyapunov exponent (LLE) values associated with the reference population; based on the first set of sleep-related information, determining a set of timeseries; based on the set of timeseries, determining an LLE value for each timeseries in the set of timeseries, thereby generating a set of LLE values; performing a comparison of the set of LLE values to a threshold to determine whether the target user is statistically different than the reference population based on the range for the LLE values associated with the reference population; based on the comparison determining that the target user is not statistically different than the reference population, designating the target user as associated with the reference population; and causing notification to be provided via an interface to a caregiver that the target user is associated with the reference population. Claim: 13. The method of claim 12 , wherein the threshold is determined based on the range for largest Lyapunov exponent (LLE) values associated with the reference population, and wherein the target user is determined as not statistically different where the set of LLE values is less than the threshold. Claim: 14. The method of claim 12 , wherein the reference population comprises a population of individuals of similar age and gender as the target user. Claim: 15. The method of claim 12 , wherein the reference population comprises a population of individuals having a condition affecting sleep. Patent References Cited: 2002/0095099 July 2002 Quyen ; 2004/0122335 June 2004 Sackellares ; 2007/0149952 June 2007 Bland Other References: Roschke et al. Nonlinear analysis of sleep eeg in depression: calculation of the largest lyapunov exponent. Eur Arch Psychiatry Clin Neurosci (1995) 245:27-35. cited by examiner ; Roschke et al. Nonlinear analysis of sleep EEG data in schizophrenia: calculation of the principal Lyapunov exponent. Psychiatry Research 56 (1995) 257-269. cited by examiner ; Fell et al. Deterministic chaos and the first positive Lyapunov exponent: a nonlinear analysis of the human electroencephalogram during sleep. Biological Cybernetics 69, 139-146 (1993). cited by examiner ; Stam. Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field. Clinical Neurophysiology 116 (2005) 2266-2301. cited by examiner Primary Examiner: Berhanu, Etsub Attorney, Agent or Firm: Shook, Hardy and Bacon, L.L.P.

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Characterizing sleep architecture