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Sleep Scoring‐Aid Service
www AseegaOnline com
www.AseegaOnline.com
How To Use Aseega
How To Use Aseega
Physiological Signal Processing
© PHYSIP
Welcome to AseegaOnline.com
Introduction
Physip is a European research company which designs innovative algorithms using the Ph
i i E
h
hi h d i
i
ti
l ith
i th
latest digital signal processing technology for the sleep analysis and vigilance monitoring fields
Our flagship product, Aseega, is a fast, time‐saving automatic sleep scoring aid tool.
It results from 35 years of sleep analysis research. Aseega has been clinically validated (*) and is already being used in cutting‐edge sleep research (**)
Aseega is now accessible through our web‐based service AseegaOnline and is open to researchers carrying out studies primarily on healthy cohorts
Thank you for considering Physip for your sleep scoring needs
Thank you for considering Physip for your sleep scoring needs
(*) Sleep 2007;30:1587‐95
(**) Science 2009;324:516‐9
Physiological Signal Processing
1.1
© PHYSIP
About this document
Introduction
This « How to Use Aseega » guide complements the information provided on the AseegaOnline and Physip websites
Intended audience
This guide consists of practical information on how to optimize the performance of Aseega and interpret its results reliably
General information concerning the procedure for using the service and the service output can be found in the AseegaOnline Service Guide
can be found in the AseegaOnline Service Guide
Document feedback
Physip welcomes your suggestions for improving our documentation. If you have Physip
welcomes your suggestions for improving our documentation If you have
any comments, please send your feedback to: [email protected]
2009‐07‐06 version 2.1
Physiological Signal Processing
1.2
© PHYSIP
How To Use Aseega
Content
Recording Recording
needs
• General
• Sensor location
• Recording parameters
EEGWatch
tracking
• Color display
• Recording check
• Scoring monitor
Numerical Numerical
results
• Content
• Structure
• Formats Physiological Signal Processing
Appendix
• More about Aseega
• Analog‐to‐digital conversion
• Time‐frequency analysis 2.0
© PHYSIP
General
Recording needs e
EEG recording constraints are related to the intended use. Automatic analysis is EEG
di
t i t
l t d t th i t d d
A t
ti
l i i
usually more sensitive to signal quality than visual analysis
e
The Aseega algorithm delivers sleep scoring and other analysis results based on a single channel
e
For optimal Aseega performance, the EEG signal to be analyzed must meet the recommendations indicated on our website (EEG Recording Checklist) (
g
)
e
The 3 obligatory recording requirements are detailed in the following slides
Respecting recording conditions is of primary importance
Quality input ⇒ quality output Physiological Signal Processing
2.1
© PHYSIP
Sensor location
e
Recording needs Recommended C P
Recommended: CzPz
y
Aseega analyzes the difference in voltage between central and occipital/parietal electrodes
• Cz‐Pz is preferred because of less muscular activity
f
db
fl
l
• C4‐O2, C3‐O1, Cz‐Oz channel analysis also provides
good results
C3
Cz
C4
Pz
O1
Oz
O2
y
Bipolar input is preferred (e.g. CzPz or C4O2)
y
Referential input is possible if both channels are recorded with the same reference, so the difference can be calculated e.g. if C4 and O2 are f
h diff
b
l l d
if
d
referenced to A1, Aseega can compute C4O2 = C4A1 ‐ O2A1
Note: AASM recommends F4, C4, O2 with Fz‐Cz, Cz‐Oz as alternatives Physiological Signal Processing
2.2
© PHYSIP
Recording parameters
Recording needs EEG filtering
e
e
Hardware filtering: outside of [0‐50] Hz frequency range
y
AC
AC‐coupled recording recommended l d
di
d d
y
Powerline hardware rejection filter has to be activated
y
Low‐pass filter, above 50 Hz
y
No high‐pass filter
Software filtering: none
y
Do not apply any software filtering on files to be analyzed by Aseega
y
It is mandatory to provide Aseega with raw data
Physiological Signal Processing
2.3
© PHYSIP
Recording parameters
Recording needs Amplitude resolution
e
Recommended < 0.1µV/bit
y
It is recommended to maximize the use of the dynamic range of the analog‐
It
i
d dt
i i th
f th d
i
f th
l
to‐digital converter
y
Aseega performance can be significantly degraded if the EEG is coded on less than 10 bits (*) in slow wave sleep
than 10 bits (*) in slow wave sleep
y
For example, using a 16 bits ADC the recording gain should allow an input range of ± 3 mV, leading to a resolution of 0.092 µV/bits
y
See Appendix B for an at‐a‐glance reminder on analog‐to‐digital conversion
(*) this corresponds to numerical values belonging to a ± 512 range, which might be insufficient for Aseega
Physiological Signal Processing
2.4
© PHYSIP
How To Use Aseega
Content
Recording Recording
needs
• General
• Sensor location
• Recording parameters
EEGWatch
tracking
• Color display
• Recording check
• Scoring monitor
Numerical Numerical
results
• Content
• Structure
• Formats Physiological Signal Processing
Appendix
• More about Aseega
• Analog‐to‐digital conversion
• Time‐frequency analysis 3.0
© PHYSIP
EEGWatch
EEGWatch tracking
e
EEGWatch is an algorithm dedicated to sleep EEG analysis, providing an at a glance EEGW
t hi
l ith d di t d t l
EEG
l i
idi
t l
sleep overview. The resulting sleep macrostructure is easy to interpret
e
When used in parallel with another sleep analysis, possibly visual or automatic,
it enables prior visual identification of sleep episodes of interest for further investigation e
In order to optimize the performance of Aseega and interpret its results reliably,
is it useful to understand the information conveyed by such a sleep color display
Physiological Signal Processing
3.1
© PHYSIP
Color Display
EEGWatch tracking
Why use a Color Display?
e
e
e
Via a color display, our EEGWatch™ enables:
y
recording quality checks
y
sleep scoring monitoring
sleep scoring monitoring
Definition of color display y
3D color‐representation of time‐frequency analysis:
short‐time Fourier transform (STFT) spectrogram, EEGWatch, …
y
See Appendix C for some time‐frequency and spectral analysis basics
A color display provides:
y
an objective EEG analysis, neither data interpretation nor classification
y
an indispensable way of evaluating EEG signal quality
y
a rough first view of sleep architecture and of EEG frequency band anomalies
Physiological Signal Processing
3.2
© PHYSIP
Color Display
EEGWatch tracking
Which questions are addressed by the Color Display?
e
Does the EEG signal quality meet the recording specifications?
y
e
Do the recording parameters meet the specifications?
f
y
e
Recording filtering and sampling frequency adequate?
Does the sleep macrostructure reveal a structured or disturbed sleep pattern?
Does the sleep macrostructure reveal a structured or disturbed sleep pattern?
y
e
Device on? Electrode contact issue? Electromagnetic interference? Sleep cycles, wake episodes
p
gq
y
p
The next slides show some examples of recording quality checks as well as sleep scoring monitoring using color displays
Whatever the analysis, whether visual or automatic, it is essential to maintain close observation of the raw recording data
Physiological Signal Processing
3.3
© PHYSIP
Recording Quality Check
e
EEGWatch tracking
Electromagnetic interference (parasite frequency around 38Hz)
y
Ideally the source of the parasite should be removed rather than just y
p
j
filtering the recording. Moreover, the filtering solution assumes that the parasite frequency is known and constant, which is rarely the case
Sample of source which causes EEG disturbance, at 38 Hz, all night long. This could be due to a CPAP machine. Physiological Signal Processing
3.4
© PHYSIP
Recording Quality Check
e
EEGWatch tracking
Electromagnetic interference (sweeping parasite frequency)
y
Here the parasitical frequency slides at the beginning of the night. A filtering p
q
y
g
g
g
g
solution would be too sophisticated and costly in terms of signal processing. This is another reason to pay attention to recording conditions and deal with the causes of parasites, rather than the effects
Sample of source which causes EEG disturbance, at a sweeping frequency
Physiological Signal Processing
3.5
© PHYSIP
Recording Quality Check
e
EEGWatch tracking
Electromagnetic interference (bad recording parameters)
y
Very noisy EEG recording, revealing several parasitical frequencies. Aliasing y
y
g,
g
p
q
g
(under‐sampling) suspected
y
Signal interruption after 01h00, for about 15 minutes
Sample of a very disturbed night recording (STFT spectrogram)
Physiological Signal Processing
3.6
© PHYSIP
Hypnogram tracking
e
EEGWatch tracking
EEGWatch enables a quick validation of sleep architecture
y
High correlation between the 2 analyses: the sleep macrostructures g
y
p
provided by the two analyses are similar ‐ no obvious contradiction:
N3 synchronized with slow wave sleep (23:30), REM with θ and β rhythms (2:00), wake with α bursts and very few δ and θ rhythms (22:30 and 5:00)
Slow wave sleep
REM
wake
Color display used for sleep architecture monitoring
Physiological Signal Processing
3.7
© PHYSIP
Hypnogram tracking
e
EEGWatch tracking
EEGWatch enables confirmation of the interpretation by Aseega of a disturbed night with a difficult sleep onset and several awakening episodes
y
The wake activity – α rhythm (first 2 hours, 4:30, 5:30) or EEG saturation due to subject movements (0:00, 4:00, 6:00) is obvious on the EEGWatch, as well as the contrasting rather good sleep continuity in the middle of the night
EEG saturation caused by subject movement
α activity
Color display used for disturbed sleep scoring confirmation
Physiological Signal Processing
α activity
3.8
© PHYSIP
Hypnogram tracking
e
EEGWatch tracking
EEGWatch enables the tracking of Aseega scoring
y
Here, Aseega apparently failed to detect the first 2 REM episodes, probably ,
g pp
y
p
,p
y
because they contain less high frequencies (β) than the other episodes
y
→ visual over‐reading required to invalidate autoscoring or, on the contrary, validate the presence of « missed » REM sleep episodes
?
Color display used as an Aseega scoring check. Possible presence of missed REM episodes
Physiological Signal Processing
3.9
© PHYSIP
Hypnogram tracking
e
EEGWatch tracking
EEGWatch enables confirmation of Aseega scoring and invalidates this visual hypnogram from an inexperienced scorer
y
The significant alpha activity is clearly visible on the EEGWatch, as well
as the absence of theta activity, thus validating autoscoring and invalidating the second visual scoring
Color display used as an arbitration between two sleep scorings
Physiological Signal Processing
3.10
© PHYSIP
How To Use Aseega
Content
Recording Recording
needs
• General
• Sensor location
• Recording parameters
EEGWatch
tracking
• Color display
• Recording check
• Scoring monitor
Numerical Numerical
results
• Content
• Structure
• Formats Physiological Signal Processing
Appendix
• More about Aseega
• Analog‐to‐digital conversion
• Time‐frequency analysis 4.0
© PHYSIP
Content
e
Numerical results
Aseega numerical results include (For Research package):
y
g
general information on recording parameters and automatic analysis
gp
y
y
sleep scorings: from 2‐, 5‐ to 10‐state(*), and 10‐state unsmoothed scoring
y
sleep parameters computed using several sleep onset definitions
y
macroscopic EEG analysis parameters:
• normalized power in frequency bands
• artifact density
• Spectrogram and EEGWatch
y
microstructures: spindles and alpha bursts detection
• position, duration, power
p
,
,p
• instantaneous frequency (mean, variance) analysis
• temporal shape and frequential stability
• events quantity /30s epoch, cumulated duration /30s epoch
events quantity /30s epoch cumulated duration /30s epoch
(*) R&K conventional sleep stages, plus 4 intermediate states. Physiological Signal Processing
4.1
© PHYSIP
Structure
Numerical results
Aseega numerical results are organized as follows (For Research package)
Sleep scoring
Sleep parameters
Macro‐analysis
Micro‐analysis
several levels of analysis, from
2 (wake/sleep) to 10 states
computed for several
definitions of sleep onset
analysis at the 30s epoch
time scale alpha bursts and spindles
microstructure detection
raw and smoothed
10‐state scorings
SPT, TST, WASO, sleep efficiency, …
spectral analysis: power in subject‐auto‐adapted frequency bands
position, duration, power, temporal shape stability
2‐3‐4‐5‐6‐state scorings
time/percent of time spent in each stage
ti
time‐frequency analysis f
l i
(EEGWatch, FFT spectrogram)
event instantaneous ti t t
frequency analysis, frequential stability
stage latencies, number of sta e shifts (% per ho r)
stage shifts (%, per hour)
Artifact density
number, cumulated duration and density per epoch
General information
l f
recording parameters, start time, duration, … sleep cycles analysis, start/end, stage content
automatic analysis parameters, channel, signal quality, …
Physiological Signal Processing
4.2
© PHYSIP
Formats
e
e
Numerical results
Se eral n merical res lt formats are a ailable
Several numerical result formats are available:
y
Matlab©‐compatible software format (MAT‐file) y
Microsoft Excel© format (XLS‐file) (*)
y
ASCII format (TEXT‐file)
Example of specific result retrieval: M‐script aseega_stats.m
y
Matlab©‐compatible software script:
• loads all analyses (.MAT files) from a given batch
• drill down: fetches each recording’s sleep latency
• plots or returns the collected data
y
Downloadable from AseegaOnline.com
(*) in this format, the numerical values of some 3D graphs will not be provided
Physiological Signal Processing
4.3
© PHYSIP
How To Use Aseega
Content
Recording Recording
needs
• General
• Sensor location
• Recording parameters
EEGWatch
tracking
• Color display
• Recording check
• Scoring monitor
Numerical Numerical
results
• Content
• Structure
• Formats Physiological Signal Processing
Appendix
• More about Aseega
• Analog‐to‐digital conversion
• Time‐frequency analysis
Appendix
© PHYSIP
More About Aseega
Appendix A
e
Aseega is an automatic sleep analysis technology resulting from 35 years of research
e
B d
Based on EEG signal processing, Aseega technology involves 3 successive steps:
EEG i l
i A
t h l
i
l
3
i
t
Aseega
Sleep
p
EEG
1. Preprocessing
Artifact rejection
Frequency q
y
band tuning
Non‐uniform
filter banks
2. Analysis
3. Classification
Spectral feature extraction
Microstructure detection
Awakening
identification
Fuzzy classification
Contextual rule smoothing
REM
identification
Sleep
scoring
Physiological Signal Processing
A.1
© PHYSIP
Analog‐to‐Digital Conversion
Appendix B
e
Analog‐to‐digital conversion = time sampling + amplitude quantization
e
The recordin parameters determine (point of no ret rn) q alit and resol tions
The recording parameters determine (point of no return) quality and resolutions
y
sampling frequency ⇒ frequency range
y
recording gain (or input voltage) ⇒ amplitude resolution
time sampling
quantization
Ts
q
Analog signal
Sinusoidal signal with 2Hz
frequency, amplitude of 50 µV
Discrete‐time signal Obtained by time sampling
period Ts = 10ms (Fs = 100Hz) Quantized discrete‐time signal
= digital signal
obtained by quantization using
a step q = 8.3 µV
(insufficient resolution for Aseega )
Physiological Signal Processing
B.1
© PHYSIP
Color display principle ( 1/3 )
e
Time‐frequency analysis
Power spectrum of a stationary signal
y
( )
(a) EEG sample while the subject is awake
p
j
y
(b) spectral analysis: power distribution along the frequency components. Here, the subject is awake and the α is the main activated EEG rhythm (a) 5s extract from a given 30s EEG epoch)
(b) Power spectrum of the whole 30s EEG epoch
Physiological Signal Processing
C.1
© PHYSIP
Color display principle ( 2/3 )
e
Time‐frequency analysis
One spectrum per epoch, power spectra monitoring over time
y
Colors are related to the power and begin (low power) with dark blue, p
g (
p
)
,
ranging through shades of blue, green, yellow and red (high power)
y
EEG monitoring: the subject falls asleep after 10 epochs
(a) Spectrum sequence (3D side view)
(b) View « from above »
Physiological Signal Processing
C.2
© PHYSIP
Color display principle ( 3/3 )
e
Time‐frequency analysis
Sleep EEG monitoring using time‐frequency analysis
y
Graphical snapshot of sleep enabling a quick overview of sleep architecture
p
p
p
g q
p
y
EEGWatch is an enhanced short‐time Fourier transform (STFT) spectrogram, dedicated to sleep EEG analysis. As a result, it is easier to interpret than the conventional STFT
wake
REM
spindles
slow wave sleep
slow wave sleep
Physiological Signal Processing
C.3
© PHYSIP
Analysis of basic signals ( 1/4 )
e
Time‐frequency analysis
Sinusoid waveform with frequency of 2 Hz
y
( )
(a) temporal signal containing a single frequency
p
g
g
g
q
y
y
(b) Fourier analysis shows that the signal power is concentrated at the 2Hz frequency, for the whole signal duration
(a) Temporal signal (extract of 2s duration)
(b) STFT Spectrogram of the 1hour‐long signal (sampling frequency fs = 100Hz)
(sampling frequency fs = 100Hz)
Physiological Signal Processing
C.4
© PHYSIP
Analysis of basic signals ( 2/4 )
e
Time‐frequency analysis
Swept‐frequency signal (chirp)
y
( )
(a) sliding frequency from 5 to 40 Hz (quadratic swept‐frequency)
g q
y
(q
p
q
y)
y
(b) Fourier analysis enables the monitoring of the signal power location along the time, from 5 to 40 Hz
(a) Temporal signal (extract of 2s duration)
(b) STFT Spectrogram of the 1hour‐long signal (fs = 100Hz)
(fs = 100Hz)
Physiological Signal Processing
C.5
© PHYSIP
Analysis of basic signals ( 3/4 )
e
Time‐frequency analysis
Random signal of « white noise » type
y
((a) white noise: wide sense stationary random signal with flat power spectral )
y
g
p
p
density
y
(b) Fourier analysis shows that the signal power is spread equally on all frequencies, for the whole signal duration
(a) Temporal signal (extract of 2s duration)
(b) STFT Spectrogram of the 1hour‐long signal
(fs = 100Hz)
(fs = 100Hz)
Physiological Signal Processing
C.6
© PHYSIP
Analysis of basic signals ( 4/4 )
e
Time‐frequency analysis
« Colored noise » = filtered white noise
y
( )
(a) Pass‐band filtered ([1‐20] Hz) white noise signal: colored noise
([
] )
g
y
(b) Fourier analysis shows that this noise contains almost no more power (i.e. no more information) outside of the pass band, whatever the time
(a) Temporal signal (extract of 2s duration)
(b) STFT Spectrogram of the 1hour‐long signal (fs = 100Hz)
(fs = 100Hz)
Physiological Signal Processing
C.7
© PHYSIP
Thank you for your interest in AseegaOnline
For further information concerning our technology, research, clinical validations and recording recommendations, please visit our websites
and recording recommendations, please visit our websites
www.physip.fr
www.AseegaOnline.com
www.AseegaOnline.com Physip SA
y p
6, rue Gobert
75011 Paris
France
The information contained in this document is subject to change without notice. This document contains trademarks that belong to
PHYSIP S.A. and other companies. © 2009 PHYSIP S.A. All rights reserved. HUA09AOL/201, July 2009.
Physiological Signal Processing
© PHYSIP