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Publication Number
US-9186083-B2
Publication Date
2015-11-17
Expiration Date
Abstract
A method and system for sleep stage determination is disclosed comprising: acquiring EEG data from two or more EEG electrodes of an electrode arrangement, wherein the EEG data is divided into one or more epochs; detecting graphoelements from the EEG data for each epoch using a graphoelement detector; calculating a differential entropy from the EEG data for each epoch in an entropy module; and assigning a sleep stage to each epoch based on the calculated differential entropy and the detected graphoelements in a classifier module. This new method and system of sleep staging is potentially suited for, but not limited to, unattended sleep diagnostic scenarios.
Core Innovation
The invention relates to a method and sleep staging system for assigning sleep stages in an unattended sleep diagnostic scenario using a reduced electrode setup adapted to be attached on a frontal area of a patient's head. EEG data is acquired from two or more EEG electrodes including at least one reference EEG electrode and at least a second active EEG electrode, and the EEG data is processed into one or more epochs while being recorded at a processor.
The EEG data is analyzed in both time domain and frequency domain to extract transients and rhythmic activity for each epoch. For each epoch, an entropy module calculates a first entropy time-series that characterizes states of light sleep and a second entropy time-series that characterizes all sleep states including light sleep, and a differential entropy is calculated by subtracting the first entropy time-series from the second entropy time-series.
An epoch is assigned as light sleep when the absolute value of the differential entropy is minimum, or as deep sleep when the absolute value of the differential entropy is maximum. In parallel, at least one graphoelement is detected from the EEG data for each epoch, and when graphoelements are present the differential entropy is interpolated or correlated with the detected graphoelement and a particular sleep stage is assigned when the graphoelements and differential entropy are not contradictory.
When graphoelements are absent, the differential entropy is interpolated to determine whether the epoch corresponds to light sleep or deep sleep based on differential entropy extremes. The system and method also address REM staging by using additional logic for REM reclassification based on variable entropy while excluding increased EMG or increased SEM density.
Claims Coverage
The document provides three independent claims: a method claim for assigning sleep stages, and two system-style claims covering a sleep staging system with corresponding modules. Across these independent claims, the inventive features are centered on reduced frontal-area EEG acquisition, time- and frequency-domain analysis for transients and rhythmic activity, two entropy time-series with differential entropy extremes for light/deep assignment, and graphoelement detection with correlation/contradiction logic, including REM reclassification criteria.
Unattended reduced frontal-area EEG sleep stage acquisition
A method for assigning sleep stages using a reduced electrode setup in an unattended sleep diagnostic scenario, including acquiring EEG data from two or more EEG electrodes of an electrode arrangement adapted to be attached on a frontal area of a patient's head.
Time- and frequency-domain feature extraction for transients and rhythmic activity
Processing the EEG data into one or more epochs and analyzing the EEG data both in time domain and frequency domain to extract transients and rhythmic activity.
Differential entropy from light-sleep entropy minus all-sleep entropy extremes
Calculating a first entropy time-series characterizing states of light sleep and a second entropy time-series characterizing all sleep states including light sleep, calculating a differential entropy by subtracting the first entropy time-series from the second entropy time-series, and assigning an epoch as light sleep when an absolute value of the differential entropy is minimum or deep sleep when an absolute value of the differential entropy is maximum.
Graphoelement detection correlated with non-contradictory vs contradictory logic
Detecting whether at least one graphoelement is present for each epoch; interpolating the differential entropy when there are no detected graphoelements; and when graphoelements are detected, correlating the at least one graphoelement with the differential entropy, then assigning a particular sleep stage when the graphoelements and differential entropy are not contradictory or assigning a particular sleep stage and not any other sleep stage when they are contradictory.
Reduced electrode arrangement with reference and active EEG electrodes for system operation
A sleep staging system in an unattended sleep diagnostic scenario including an electrode arrangement with two or more EEG electrodes where at least one electrode is a reference EEG electrode and at least a second electrode is an active EEG electrode, adapted to be attached on a frontal area of a patient's head to measure brain electrical activity for acquiring EEG data.
Processor modules: epoching, spectral analysis, and entropy module for differential entropy
A processor configured with a data collection module to receive, record and store EEG data, a spectral analysis module to analyze EEG data in time domain and frequency domain to extract transients and rhythmic activity, and an entropy module configured to calculate a first entropy time-series and a second entropy time-series and calculate a differential entropy by subtracting the first entropy time-series from the second entropy time-series for each epoch.
Classifier using differential entropy and graphoelement presence for per-epoch sleep stage assignment
A graphoelement detector configured for detecting whether one or more graphoelements is present from the EEG data for each epoch and a classifier module configured to assign a sleep stage for each epoch based on the differential entropy and whether one or more graphoelements is present.
Across the independent claims, the core claim coverage combines reduced frontal-area EEG acquisition in an unattended scenario with epoching and time-/frequency-domain analysis, computes differential entropy from two entropy time-series (light-sleep states vs all sleep states), assigns light sleep and deep sleep using minimum/maximum absolute differential entropy, and integrates graphoelement detection with interpolation plus correlation/contradiction logic to govern sleep stage assignment; REM-related refinement is addressed in dependent claims.
Stated Advantages
Cost-effective expansion to home sleep diagnostics.
Results are comparable to human scoring using conventional R&K rules.
Documented Applications
Unattended sleep diagnostic scenarios including reduced-electrode home sleep diagnostics.
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