This work is funded by DARPA SBIR grant W31P4Q-08-C-0123 and. NIH SBIR grants R44HL068463-05 and 2R44-DE016772-0. The work is released under ...
Automated Sleep Staging in Real Time using a Single EEG Channel (Fp1-Fp2) Djordje Popovic1, 2), Robin Johnson1), Phillip Westbrook1), Chris Berka1), Gene Davis1)
Abstract#1174
Poster# 201
(1) Advanced Brain Monitoring, Inc., Carlsbad, CA
(2) University of Southern California
Introduction
Conclusion 1:
Conclusion 2:
Conclusion 3:
Cognitive impairments caused by chronic sleep deprivation can be ameliorated by frequent brief naps, but the effect depends on accumulated sleep debt and timing, duration and sleep architecture of the naps1). Naps would likely be more effective if their sleep architecture, timing and duration could be tailored with respect to the accumulated sleep debt and previous naps. Real-time assessment of sleep architecture using a minimal number of EEG channels and dry electrodes is a prerequisite for such an approach. With an intention of developing a device for optimizing naps in operational environments, we developed algorithms for real-time sleep staging using a single (Fp1-Fp2) EEG channel.
Automated assessment of sleep architecture on a secondby-second basis in real time using the Fp1-Fp2 EEG seems feasible.
Further refinement of the algorithm is needed in order to achieve accurate separation among wakefulness, early drowsiness and REM sleep.
Combining the frontopolar EEG with other sensors such as accelerometer may yield more accurate distinction between Wake, drowsiness and REM stages.
Results BASI
1
0.9
I(i)= 1 if FFx(i) < threshold 0 if FFx(i) > threshold
0.8 0.7
Wake Stage 1 Stage 2 SWS REM
0.4 0.3
SWS
2000 0
0
0
Architecture and prototypes of the Nap Cap Interface
Controller
EEG acquisition
Sleep staging
Sensory stimulation
Sleep modulation procedures
Duration of epoch can be adjusted depending on the desired goal (1 second for tracking short-time variations in vigilance, 10 – 30 seconds for classical sleep staging
1
1.5
2 BASI
2.5
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3.5
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0
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2000
0
0
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4
6
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4000
0
Wake 0
2
2000
Figure 1. Empirically derived probability that 1s long EEG segment is taken from a given sleep stage as a function of BASI
REM
1000 0
FFx(i) - fractal frequency of epoch i, defined as the frequency below which X% of the total spectral power is contained.
0.6
IF70
0.5 0.4 0.3
NREM Stage I
1000
2000
0.5
4
NREM Stage II
5000
0.1
0
2
10000
0.2
Ai(f) – EEG spectral power at frequency f in epoch i
0.8
Probability
4000
0.5
0
SWS Stage II Stage I REM Wake Movement
0.9
0.7
0.6
Number of epochs
Probability of sleep state
Modification of the alpha-slow-wave index (ASI) introduced in 19823) that takes into account the fact that there is little or no alpha activity in the Fp1-Fp2 EEG during wakefulness
SWSI
0
2
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8 10 f70 (Hz)
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0.2
FF70 enabled best discrimination between slow-wave sleep (NREM3) and other stages (optimal threshold = 8Hz).
0.1 0
0
0.2
0.4 0.6 0.8 Slow Wave Sleep Index (SWSI )
1
20
Figure 2. Empirically derived probability that 1s long EEG segment is taken from a given sleep stage as a function of SWSI
18
NAPCAP DEVICE
Stage Wake Moving average filter (10 seconds) applied to smooth out BASI series 5
Methods Subjects 13 healthy subjects (6M, 7F) took a 2-hour afternoon nap. Signals Portable wireless 9-channel EEG device (Advanced Brain Monitoring Inc.) acquired: • 3 channels of EEG (C3A2, C4A1 and Fp1Fp2) • 2 channels of EOG (referenced to mastoids) • chin EMG Visual sleep staging (GS) All records were scored in 30-second epochs according to the AASM criteria2) and each second within an epoch was assigned the same stage as the whole epoch. Automated sleep staging (AS) Each second of Fp1-Fp2 EEG was classified into Wake, Light Sleep (LS), Deep sleep (DS) and REM based on • beta-alpha-slow-wave index (BASI) • slow-wave-sleep index (SWSI) Outcomes Second-by-second comparisons were performed between GS and AS.
Manual stage BASI Automated detection
4
Algorithm: • if BASI>2 for >3sec raise Wake flag • Wake flag raised until BASI3sec
3
2
1
0
-1
30
Stage DS
Stages REM and LS
60
90
120
150
REM (initial detection) • if Wake flag is not raised AND • conditions for the detection of DS have not been met AND • there have been 5 or more seconds with BASI > 1 in the past 10 seconds AND • there have been 2 consecutive seconds with FF70 < 4Hz (=suspected rapid eye movements)
Threshold for optimal detection of Deep Sleep (=NREM 3) determined as a trade-off between sensitivity (% of NREM3 epochs correctly recognized DS) and positive predictive value (% of epochs recognized as DS that indeed were NREM3) SWSI cutoff
raise REM flag
Figure 3. Bar-plots of manual (upper half) and algorithmic (lower half) staging for all 13 subjects. Black – Wake, White – Sleep, Grey – NREM Stage 1 ( manual staging only). Wake correctly False positives staged (%) (NREM1) (%)
REM (continuation) • if REM flag is raised AND • there have been 5 or more seconds with BASI > 1 in the past 10 seconds
FF50
FF60
Sens. PPV
FF70
Sens. PPV
FF80
Sens. PPV
Sens. PPV
0.4
0.84
0.62
0.91
0.60
0.85
0.69
0.85
0.67
0.5
0.75
0.72
0.84
0.71
0.75
0.77
0.77
0.76
0.6
0.62
0.80
0.75
0.79
0.63
0.82
0.67
0.81
0.7
0.44
0.85
0.61
0.84
0.49
0.84
0.54
0.86
0.8
0.25
0.89
0.42
0.89
0.32
0.89
0.40
0.90
False SWS detections 10
3500
Manual stages SW Detector EEG central frequency
9 8 Hz
3000
7 6 5
2500
Stage IV Stage III
Epoch count
Subject with Autonomous closed-loop operation Nap Cap
keep scoring REM
False positives (other stages) - %
Stage II
2000
Stage I Wake
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1800 time (s)
2400
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10 Manual staging SW Detector EEG central frequency
9 8
1000
Hz
7 6
C3A2
86.3
41.7
6.6
VEOG
61.3
28.6
5.9
HEOG
63.2
28.3
5.2
Fp1Fp2
58.4
12.4
4.8
W
REM
NREM1 NREM2 NREM3
W
274
63
78
30
5
REM
40
237
36
36
1
LS
154
65
198
1258
70
DS
4
5
8
92
431
Citations
5
Light Sleep (NREM1 and 2) • if none of the criteria for Wake, REM or DS are met score LS
500
4 SWS
0
Stage II
Wake
Stage I
Stage II Manual stage
REM Movement time
Figure 4. Misclassifications across sleep stages scored by a human expert
Stage I Wake
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200
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1000 1200 time (s)
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Figure 5. Two examples of misclassification.
Classification accuracy Overall accuracy 77% (Wake:58%;LS:83%; DS:85%;REM:64%), similar to most reports in the literature Some typical causes of erroneous classifications: • sleep spindles (NREM2 or 3) raise BASI leading to misclassification as W, REM, or LS • motion artifacts (Wake or NREM1) causing a drop in FF70 misinterpreted as rapid eye movements or delta waves
1) Takahashi M. (2003). The role of prescribed napping in sleep medicine. Sleep Medicine Review 7(3): 227 – 235. 2) American Academy of Sleep Medicine. The AASM Manual for the scoring of sleep and associated events. AASM, Westchester, IL, 2007. 3) Jobert M., Schulz H., Jahnig P. et al (1994). A computerized method for detecting episodes of wakefulness during sleep based on the alpha-slow-wave index (ASI). Sleep 17(1): 37-46.
Acknowledgements
This work is funded by DARPA SBIR grant W31P4Q-08-C-0123 and NIH SBIR grants R44HL068463-05 and 2R44-DE016772-0. The work is released under DARPA distribution Statement “A” (Approved for Public Release, Distribution Unlimited) All authors are shareholders in Advanced Brain Monitoring, Inc.
