Sleep medicine: from the laboratory bench to a

potential diagnostic toolTijana Bojić, MD, PhD1, Aleksandra Vuckovic, PhD2

and Aleksandar Kalauzi, PhD3

1Vinča Institute for Nuclear Sciences, University of Belgrade, Belgrade, Serbia, tijanabojic@vinca.rs2School of Engineering, University of Glasgow, Glasgow, UK, Aleksandra.Vuckovic@glasgow.ac.uk3Institute for Multidisciplinary Research, University of Belgrade, Belgrade, Serbia, kalauzi@imsi.rs

IntroductionState of drowsyness: transitional state between wakefulness and sleep

• National Sleep Foundation’s 2005 Sleep in America poll, 60% of adult drivers – about 168 million people – drowsy driving• One-third, (37% or 103 million people), have actually fallen asleep at the wheel!• 100,000 police-reported crashes are the direct result of driver drowsyness each year (1,550 deaths, 71,000 injuries, and $12.5 billion in monetary losses). Tip of the iceberg!

a. There is no test to determine sleepiness as there is for intoxication, i.e. a “Breathalyzer”.

b. State reporting practices are inconsistentc. Self-reporting is unreliabled. According to data from Australia, England, Finland, and other

European nations, all of whom have more consistent crash reporting procedures than the U.S., drowsy driving represents 10 to 30 percent of all crashes.

Introduction

• Physiology: decrease of sensory signal detection, increase of motor reaction time

• Psychology: decrease of attention level, dream-like mentation• Hori stages 1-9 (Hori T et al., 1990) : focus on the phases 1-4 (dominant

alpha EEG activity)

• Bojić T, Vuckovic A, Kalauzi A. Modeling EEG fractal dimension changes in wake and drowsy states in humans-a preliminary study. J Theor Biol 2010;262:214-222.

• Kalauzi A, Vuckovic A, Bojić T. EEG alpha phase shifts during transition from wakefulness to drowsyness. Int J Psychophysiol 2012, in press, doi:10.1016/j.ijpsycho.2012.04.012

Introduction

Alpha amplitude

• Relation of alpha amplitude with

fractal dimension (FD)

• Topographical distribution of FD in wake and drowsy

• Topographical distribution of FD differences

Alpha phase• Alpha carrier frequency: a

mathematical concept developed to overcome the FFT limitations for calculating the leading oscillatory component of the signal

• Phase shift: difference in phases between two electrodes

• Phase potential: one electrode; an average value of phase shift for all channel pairs, where the electrode is positioned as first in the pair.

• Phase locking: two electrodes; 1/SD of phase shift: SD↓locking↑

Uc ( t ) = Ac sin ( 𝜔t + )𝜑

Results: Alpha amplitude - fractal dimension

0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.51

1.1

1.2

1.3

1.4

1.5

1.6

ralpha

FD

a

r alpha=ΣAmp alpha/ΣAmp total

R2 mod, EEG=0.682

Relation between fractal dimension (FD) values and normalized alpha amplitudes of 280 analysed EEG signals (+ wake;Δ drowsy) and 30 simulations (solid line)

b

a

1.1 1.15 1.2 1.25 1.3 1.35

-0.1

0

0.1

st

dev

(F

D)

1.1 1.15 1.2 1.25 1.3 1.350

5

10

15

mean (FD)

P D

E

b

1.1 1.15 1.2 1.25 1.3 1.35

-0.1

0

0.1

s

t d

ev

(F

D)

1.1 1.15 1.2 1.25 1.3 1.350

5

10

15

mean (FD)

P D

E

a

Probability density estimate (PDE, left) and topographical distribution of mean FD values in the wake (a) and drowsy state (b).

Results: Alpha amplitude - fractal dimension

Results: Alpha amplitude - fractal dimension

Probability density estimate (PDE) of EEG fractal dimension absolute differencesBetween wake and drowsy state. F3, F7 and O1 electrodes changed the most theirFD (indicated by bolded circles)

0 10 20 30 40 50 60

20

40

0 10 20 30 40 50 60

20

40

0 10 20 30 40 50 60

20

40

0 10 20 30 40 50 60

20

40

0 10 20 30 40 50 60

20

40

n(t

) (o

)

c(t

)

time (s)

fn(Hz)

9

10

11

12

9-12

Results: alpha carrier frequency

Temporal dependence of phase shifts of Fourier components (Δ𝜑(t), upper four pannels, compared to carrier frequency phase shift (Δ𝜑(t), lowest pannel. Greater accuracy of Δ𝜑(t) compared to all separate Fourier component phase shift is evident.

Definition: frequency of the main oscillatory component imbedded in the EEG signal

Results: alpha phase shift

0

0.5

1

1.5

x 10-3

2 c

um

(Pd)

F7

F8

T3

T4T5

T6

F3

F4

C3

C4

P3

P4O1

O2

A

0.001

B

Definition: the difference in phases between the two electrodes

Frontal channels undergo the greatest change in alpha carrier frequency phase shift during wake to drowsy transition. Cumulative sum of squared differences of probability density estimates(Δ2

cum(Pd) between profiles of alpha phase shift in the wake and drowsy state. Same data presented as topographical distribution: solid line-mean value, thin line-standard deviation.

+ +

+ +

+ -

+ +

+ -

- -

+ -

60o

A

+ +

+ +

+ -

+ +

+ -

- -

+ -

B

Results: alpha phase potentialDefinition: the mean value of phase shift of one electrode with respect to all others

electrode pairs

Alpha phase potentials calculated from ten tested individuals in the wake (A) anddrowsy (B) state. A: Channels within these two regions were synchronized in phase, but opposite in phase across the regions. The pronounced division recorded in wakewas modified in the drowsy state. Arrows: channels where the greatest phase potential change occurred.

Results: alpha phase locking

A B

In wakefulness (A) there are 2 regions regions with above average phase locking between channels functionaly unconnected: anterior, rich in intraregional connections that are above average phase locked and posterior one. In the drowsy state (B) anterior region is reorganized, intraregional connection loosened and phasely connected to the posterior region that is unchanged.

Definition: An inverse measure of standard deviation of alpha phase shifts

Conclusions• New way of analysis of EEG oscillations, treating them as a stabile carrier frequency signals

• We found that EEG channels can be differently coupled in in the two states, synchronized in wake and less synchronized in drowsyness

• Hori stage 4: global decrease of alpha and change of channel coupling• Alpha oscillators (Lopes da Silva 1973, Klimesh, 1999, Jann, 2009): “local”or “functional”alphas.• Our data: first uncoupling in frontal regions-reduction of the highest cognitive functions, dream-like mentation (Nielsen, 2005), increase of motor reaction time (Stuss et al., 2005).

• Alpha phase potentials: contraphase of alpha oscillations between anterior and posterior regions in wakefulness; emergence of new alpha clusters in anterior region in drowsyness; more evidence in favor of existence of diffuse alpha generators

• Alpha phase locking: our data correlate with the distribution of neuronal assembleys: two circuits in wake integrating into one in drowsy state.

Perspectives

• New method-new insights

into physiological and pathological sleepiness

• Application – looking for collaboration with clinicians:

clinical diagnostics in pathologies-insomnia, obstructive sleep apnea, narcolepsy

– somnolence detection system: diagnostics of somnolence in healthy: situations of compromised individual and collective safety.

Acknowledgments

• Ministry of Education and Science of the Republic of Serbia (project III 41028)

• Institute for Mental Health, Belgrade, where part of this work was done

• Dr V. Radivojević, Mr P. Šuković and Dr Ž. Martinović for obtaining and analysing EEG records

• We express our gratitude to all participants in the experiments

Grazie per la vostra attenzione

Belgrado, 26 Giugno 2012.