Student Name: Pham Lam Vuong

Supervisor : Dr. Likun Xia

Co-supervisor: Dr. Aarmir Saeed Malik

Field supervisor: Dr. Rusdi Bin Abd Rashid

Student Name: Pham Lam Vuong

Supervisor : Dr. Likun Xia

Co-supervisor: Dr. Aarmir Saeed Malik

Field supervisor: Dr. Rusdi Bin Abd Rashid

Outline

Introduction1

Proposed Methodology2

Preliminary Result53

INTRODUCTION

Harmful effects of Alcohol addiction

Alcohol addiction is characterized by an increased tolerance and physical dependence on alcohol that affect individual's ability to control alcohol consumption safely

Harmful effects of alcohol use:– 3.8% of all global deaths each year were

attributable to alcohol (about 2.5 million people) [1]

– Causal factor in 60 types of diseases and injuries – Component cause in 200 others (30% of road

accidents in Malaysia) [2]– Chronic, heavily drinking alters level of

neurotransmitters and kills many brain cells ( white cells and grey cells) that causing brain shrinkage

[1] WHO. (2011). Global status report on alcohol & health.

[2] Assunta M. The alcohol problem in Malaysia. The Globe Special Issue 4.Global Alcohol Policy Alliance, 2001–2002

• unintentional injuries: road traffic accidents, falls, drowning, poisoning …

• intentional injuries: violence and self-inflicted injuries

• cancers of the colorectal, female breast, larynx, liver, esophagus, oral cavity and pharynx

Alcohol Biomarker

Physiological indicators of alcohol exposure or ingestion and may reflect the presence of an alcohol use disorder

Increase treatment efficiency by– Combining with other screening tools to identify individuals with alcohol-related

problems or who are at risk – Evaluating if new intervention has desirable effect by giving outcome measures in

earlier stage– Identifying subset of abstainers at highest risk for relapse

Electroencephalogram (EEG)

A non-invasive technique that detects electrical impulses in the brain due to neuronal activity using electrodes placed on the patient’s scalp

Frequency-dependent, spontaneous and continuous neural activity

Appear in restful state.

Could be decomposed into bands with different frequencies reflecting different types of brain activities, most commonly:

Spectral powerDominant frequencySignal complexityCoherence …

Spectral powerDominant frequencySignal complexityCoherence …

Method using resting EEG Ref Result Limitation

Spectra power

- Detoxified patients (191 male alcoholics) vs. controls

Churchman [26]

Significant correlation between decreased power in slow bands and cortical atrophy. Increased power in beta band correlated mainly with the use of benzodiazepines, sensory perceptual alterations clinical seizures and family history

Lack of associations between EEG activities and the severity of abuse (cortical atrophy) or family/personal drinking history

- 22 Detoxified patients vs. 58 controls

Saletu [27]

Decrease delta & slow alpha power, increase beta power in alcoholics compared with controls. After 6 months of treatment, abstainers showed an increase slow activity & fast beta, a decrease in fast alpha & slow beta

Lack of association between the abstinent progress and EEG activities

- 307 Alcohol-dependent vs. 307 controls

Rangaswamy [28]

Increased Beta 1 (12.5-16Hz) and Beta 2 (16.5-20) absolute power at all loci, but most prominent in the central and parietal, increased Beta 3 (20.5-28) power in frontal.Male alcoholics had significantly higher beta power than female in all three bands

The relationship between elevation of beta power in alcoholics & the development of alcoholism needs more research

- 307 Alcohol-dependent vs. 307 controls

Rangaswamy [29]

Increased absolute theta (3-7Hz) power at all scalp locations, prominent at the central & parietal in male, and at the parietal in female in alcohol-dependent when compared with the respective matched controls

The relationship between theta power increase & the development of alcoholism and, state-related condition need investigated more

- 108 Hispanic & 269 non-Hispanic young adults with family history of alcohol dependence

Ehler [30]

Low voltage alpha (LVA) variant was not associated with drinking status, family history of alcoholism, or a personal history of anxiety disorders, Ethnic variation may exist in the prevalence of LVA EEG variants.In comparison with men, women were found to have higher slow alpha (7.5–9 Hz) and beta power (12–20 Hz, 20–50 Hz)

More than half participants were woman. Samples were assessed nonrandomly

- Alcohol dependence vs. non-alcohol dependence

Ehler [31]

Alcohol dependence but not a family history of alcohol dependent was associated with lower spectral power in the alpha frequency range in the right & left occipital areas in men but not in women

Low voltage alpha is recorded in both alcoholics and NACs without significant different. No association between alpha voltage & alcoholics

- Relapsers vs. abstainers (107 patients)

Bauer [25]

Sensitivity, specificity, and positive and negative predictive value were 61%, 85%, 75% and 74%, respectively using logistic regression

Low sensitivity.

Synchronization likelihood

- 11 Heavy vs. 11 light drinkers

Bruin[32]

Heavy student drinkers have increases in theta & gamma synchronization that are indicative of changes in hippocampal–neocortical connectivity

Study is limited to male students and short duration of drinking

- Light, moderate vs. heavy drinkers (49 males & 47 females)

Bruin [33]

Relatively low alpha and slow-beta synchronization in the left hemisphere in male and female heavy drinkersLower fast-beta band synchronization in moderately & heavily drinking males

Lack of information about the duration of drinking & mechanism related to low synchronization

Hjorth

- Relapsers vs. abstainers (78 patients)

Winterer [24]

Correct classify 83-85% by applying Hjorth’s features to Artificial neuronal network classification

The rate of misclassification is still high (>25%) for abstainer.

Problem statement & hypothesis

Problem statement: There is no investigation about capability of EEG for alcohol addiction screening and treatment.

Hypothesis: EEG biomarker will help increase treatment efficiency by determining if medication have a desirable effect much earlier.

=> To examine the usefulness of EEG features as biomarker for alcohol addiction treatment.

=> To develop an EEG biomarker to assist doctors evaluating new medication treatment (compare new medication with old medication).

PROPOSED METHODOLOGY

Proposed methodology - participants

Alcoholics meet criteria of DSM IV and applied for detoxification

Excluded criteria:– Under 18– Drug addiction (other than alcohol addiction)– Acute medical and psychiatric problems– History allergic to diazepam– Have severity withdrawal symptoms– Refuse consent– Patients who had abstained for more than one day before starting the treatment

18 participants will be enrolled to two EEG stages: – Pre-treatment: EEG is measured before participants take benzodiazepine– Post-treatment: EEG is measured after participants take benzodiazepine 2 weeks

12 weeks12 weeks

ScreeningScreening

RecruitmentRecruitment DetoxificationDetoxification

DiagnosingDiagnosing OutcomeOutcome

AWSAWS RRRR

Finish detoxification

score

Before treatment

Experiment degisn

StartStart

EndEnd

Eyes-open (5 min)Eyes-open (5 min)

Eyes-close (5 min)Eyes-close (5 min)

PRELIMINARY RESULTS

PRELIMINARY EXPERIMENT

17 alcohol users (11 men and 6 women) age 31-83 (mean 55.41 ± 13.86 years) were included in the study and matched with non-alcohol subjects according to age and gender.

The participants were evaluated by physician using Alcohol Use Disorders Identification Test (AUDIT).

Participants were assign to alcohol users if:– Have alcohol-related problem in the past year– Have first three AUDIT ≥ 4

StartStart

EndEnd

Eyes-open (5 min)Eyes-open (5 min)

Eyes-close (5 min)Eyes-close (5 min)

Spectral power

Independent t-test for EEG power variables

Variables Alcoholics ControlsGroup comparision p-

valuesn = 17 n = 12

    Mean S.D   Mean S.D

EC

Fp1 theta (uV) 23.13 27.10   69.11 49.61 0.01007

Fz theta (uV) 29.98 32.83 81.94 65.33 0.02273

T3 theta (uV) 25.55 34.53 76.96 41.07 0.00193

P4 theta (uV) 24.83 34.36 78.22 54.36 0.01463

F8 theta (uV) 25.72 32.69 67.66 47.52 0.01640

O1 theta (uV) 32.30 39.35 86.38 53.63 0.00780

Fz high gamma (uV) 1.62 1.36   16.92 23.32 0.04427

EO

Fz alpha (%) 13.31 5.88   5.82 3.29 0.00035

O1 alpha (%) 15.06 7.28 5.09 1.51 0.00006

P4 alpha (%) 15.71 8.16 5.19 2.24 0.00012

Pz alpha (%) 16.60 7.10 7.29 1.90 0.00010

T6 alpha (%) 15.34 7.88 5.09 1.93 0.00010

Fp1 theta (uV) 18.67 23.84 67.74 51.65 0.01661

Fz theta (uV) 23.87 29.70 68.17 43.64 0.01345

T3 theta (uV) 20.32 32.84 68.06 35.73 0.00306

P4 theta (uV) 22.79 30.41 74.52 56.60 0.02080

F8 theta (uV) 19.91 28.75 60.95 43.54 0.01933

O1 theta (uV) 24.16 32.84   78.06 55.66 0.01577

Upper section shows variables of the eye-closed stage; lower section shows variables of the eye-open stage. Those values were picked up from electrodes which had significant different between alcoholics and controls (p < 0.05)

The electrodes which have significant different between alcoholics and controls are chosen.

Formula for independent t-test calculation:

Statistical Probability Map

Maps of EEG absolute power differences between alcoholics and normal controls in eyes-close (EO) state

Maps of EEG relative power differences between alcoholics and normal controls in eyes-close (EO) state

14/16 (87.5%) casesP<0.05

14/16 (87.5%) casesP<0.05

12/16 (75%) casesP<0.001

12/16 (75%) casesP<0.001

VariablePAU NAS

p-valueMean S.D Mean S.D

EC

Delta 9.04 1.42 10.55 1.76 0.00779

Theta 21.51 6.46 57.06 19.80 0.00000

Alpha 12.94 4.03 12.77 4.29 0.90369

Beta 8.11 1.52 8.66 1.56 0.29103

High Beta 1.52 0.39 1.48 0.46 0.76164

Gamma 2.10 0.85 3.08 1.05 0.00413

High Gamma 2.06 0.54 15.19 3.06 0.00000

EO

Delta 9.82 2.73 9.24 1.34 0.42610

Theta 17.19 4.73 53.33 17.12 0.00000

Alpha 6.19 1.13 7.01 1.46 0.06874

Beta 8.29 1.47 10.16 2.79 0.01795

High Beta 2.59 1.61 2.79 1.59 0.70575

Gamma 3.07 1.79 4.65 2.46 0.03488

High Gamma 2.48 1.03 11.87 2.91 0.00000

Hjorth features

• The Hjorth’s mobility feature is obtained from the first derivative of EEG fluctuation. It shows the rate of change of signal’s amplitude.

• The Hjorth’s complexity feature is obtained from the second derivative of EEG fluctuation. It shows the peaks and troughs of signal.

alcoholics control

mobility complexity mobility complexity

EC

frontal 3.2930 7.6049 5.6371 8.3020

central 3.6353 7.2607 5.9533 8.2474

parietal 3.4995 7.3842 5.5194 8.4599

temporal 3.9655 7.3759 5.8131 8.1374

occipital 3.4096 7.2394 5.1621 8.3782

EO

frontal 3.3328 7.9792 4.8878 8.4895

central 3.5157 7.8788 5.6657 8.3631

parietal 3.2936 8.0335 5.3084 8.5222

temporal 3.9557 7.7526 5.5270 8.1471

occipital 3.3066 7.8580 5.1191 8.4575

𝑀𝑜𝑏𝑖𝑙𝑖𝑡𝑦= ඥ𝑀2/𝑇𝑃 (1) 𝐶𝑜𝑚𝑝𝑙𝑒𝑥𝑖𝑡𝑦= ඥ(𝑀4/𝑀2)/(𝑀2/𝑇𝑃) (2) where: 𝑇𝑃= (𝑥12 + 𝑥22 + ⋯+ 𝑥𝑛2)/𝑛 (3) 𝑀2 = (𝑑12 + 𝑑22 + ⋯+ 𝑑𝑛2)/𝑛 (4)

𝑀4 = ሺ𝑑1−𝑑0ሻ2+ሺ𝑑2−𝑑1ሻ2+⋯+ሺ𝑑𝑛−𝑑𝑛−1ሻ2𝑛 (5)

Hjorth Features

Hjorth’s mobility and complexity

CPEI features

• The EEG waveform can be described as a sequence of ordinal patterns. The permutation entropy (PE) describes the relative occurrence of each of these patterns. A composite PE index (CPEI) was developed, which was the sum of two simple PEs

alcoholics control

mean median max min mean median max min

EC

frontal 0.8518 0.8511 0.8659 0.8347 0.8838 0.8840 0.8875 0.8797

central 0.8448 0.8450 0.8605 0.8258 0.8857 0.8857 0.8908 0.8807

parietal 0.8447 0.8457 0.8596 0.8255 0.8851 0.8854 0.8904 0.8794

temporal 0.8556 0.8558 0.8661 0.8429 0.8840 0.8842 0.8895 0.8781

occipital 0.8382 0.8400 0.8558 0.8106 0.8799 0.8799 0.8886 0.8712

EO

frontal 0.8568 0.8638 0.8745 0.8244 0.8813 0.8819 0.8866 0.8747

central 0.8544 0.8619 0.8738 0.8191 0.8856 0.8855 0.8910 0.8801

parietal 0.8577 0.8650 0.8752 0.8228 0.8839 0.8838 0.8897 0.8780

temporal 0.8613 0.8664 0.8765 0.8318 0.8816 0.8819 0.8864 0.8759

occipital 0.8596 0.8640 0.8756 0.8316 0.8820 0.8820 0.8893 0.8751

CPEI = −σ𝑝𝑖 × lnሺ𝑝𝑖ሻ,𝑡𝑖𝑒<0.5,𝜏=1+ σ𝑝𝑖 × lnሺ𝑝𝑖ሻ,𝑡𝑖𝑒<0.5,𝜏=2ln (49)

CPEI Feature

CPEI features in EC stage and EO stage

0.80000.81000.82000.83000.84000.85000.86000.87000.88000.89000.9000

fron

tal

cent

ral

pari

etal

tem

pora

l

occi

pita

l

fron

tal

cent

ral

pari

etal

tem

pora

l

occi

pita

l

EC EO

alcohol users max

alcohol users mean

alcohol users min

NAS max

NAS mean

NAS min

CLASSIFICATION RESULT

Algorithm Features usedAverage Correct

classificationWorst

Correct RateSensitivity Specificity F-score

Random Forest CPEI 84.00% 20.00% 81.00% 87.00% 0.85

Discriminant analysis Phase Delay 70.00% 40.00% 61.00% 84.00% 0.71

Decision Tree EEG power 79.00% 40.00% 77.00% 70.00% 0.78

Random forest (AUC=0.7291)Discriminant analysis (AUC=0.6542)Decision Tree (AUC=0.6312)

SUMMARY & RECOMMENDATION

There was a decrease in brain electrical activity and the slowing of the predominant frequency in chronic and heavy alcohol users, most significant in occipital and parietal regions.

CPEI features showed high power of classification between alcohol users and non-alcohol subjects with less dependence on timing and noise during recording. Above classification results can be improve by selecting more suitable features for classification.

The results still might be highly inaccurate due to our large sources of bias. The high variance can be reduced by increasing sample size with suitable range of biological characteristic.

APPENDIX

Asymmetry, coherence and phase delayeye-closed eye-open

Pairs alcoholics controls Pairs alcoholics control

Asymetry

Fp1-F3 theta 1.1659 4.2337 Fp1-F3 theta 0.4930 2.7176

F3-O1 theta -0.8173 -2.9414 F3-O1 theta -0.7506 -3.0701

Fp1-F3 alpha -0.5609 1.6291 Fp1-C3 alpha -0.3522 -0.7367

O1-O2 theta 0.9638 3.8528 O1-O2 theta 1.2039 3.8600

Coherence

Fp1-P3 theta 1.4007 4.2911 Fp1-C3 theta 0.4063 3.8591

Fp2-P4 theta 1.3397 3.9992 Fp2-P4 theta 1.9675 5.6538

Fp1-C3 beta -0.1459 -1.4241 Fp1-C3 alpha -0.8562 -2.9408

Fp2-C4 beta -0.6094 -2.2025 F4-C4 alpha -1.8376 -4.2112

T3-T4 theta 2.6847 6.0048 T3-T4 theta 1.7863 5.5125

P3-P4 alpha -1.7929 -3.8038

Phase delay

Fp1-C3 theta 1.1770 3.1375 Fp1-C3 theta 1.4562 3.0291

Fp2-C4 theta 1.3738 3.3764 Fp2-C4 theta 1.3364 3.7921

Fp1-Fp2 theta 2.9970 5.1148 Fp1-Fp2 theta 3.4897 5.8020

P3-P4 theta 2.4922 4.9240 P3-P4 theta 1.8277 4.6238

Asymetry: difference power between 2 regions Asym = [(A-B)/(A+B)]

Coherence is a measure of the degree of association or coupling of frequency spectrabetween two different time series

Phase delays are a measure of the temporal lead or lag of spectra

Phase delay & coherence

𝐶𝑜ℎ𝑒𝑟𝑒𝑛𝑐𝑒ሺ𝑓ሻ= (σ (𝑎ሺ𝑥ሻ𝑢(𝑦) + 𝑏ሺ𝑥ሻ𝑣ሺ𝑦ሻ))2 + (σ (𝑎ሺ𝑥ሻ𝑣ሺ𝑦ሻ− 𝑏ሺ𝑥ሻ𝑢ሺ𝑦ሻ))2𝑁𝑁 σ (𝑎ሺ𝑥ሻ2 + 𝑏ሺ𝑥ሻ2)𝑁 σ (𝑢ሺ𝑦ሻ2 + 𝑣ሺ𝑦ሻ2)𝑁

𝐶𝑜𝑠𝑝𝑒𝑐𝑡𝑟𝑢𝑚ሺ𝑓ሻ= ൫𝑎ሺ𝑥ሻ𝑢ሺ𝑦ሻ+ 𝑏ሺ𝑥ሻ𝑣ሺ𝑦ሻ൯𝑁

𝑄𝑢𝑎𝑑𝑝𝑒𝑐𝑡𝑟𝑢𝑚ሺ𝑓ሻ= (𝑎ሺ𝑥ሻ𝑣ሺ𝑦ሻ− 𝑏ሺ𝑥ሻ𝑢(𝑦))𝑁

𝑃ℎ𝑎𝑠𝑒 𝑎𝑛𝑔𝑙𝑒 ሺ𝑓ሻ= 𝐴𝑟𝑐𝑡𝑎𝑛 (𝑄𝑢𝑎𝑑𝑠𝑝𝑒𝑐𝑡𝑟𝑢𝑚(𝑓))(𝐶𝑜𝑠𝑝𝑒𝑐𝑡𝑟𝑢𝑚(𝑓))

Classification result (random forest)

Random forest classification              

Features   Classification result

Power CPEI HjorthAsyme-

tryCoherenc

ePhase Delay

  Average result Worst case Sensitivity Specificity F-score

              Test Train Test Train Test Train Test Train Test Train

Y 0.80 1.00 0.40 1.00 0.79 1.00 0.80 1.00 0.82 1.00

Y 0.84 1.00 0.20 1.00 0.81 1.00 0.87 1.00 0.85 1.00

Y 0.71 1.00 0.20 1.00 0.65 1.00 0.76 1.00 0.71 1.00

Y 0.71 1.00 0.20 1.00 0.64 1.00 0.85 1.00 0.73 1.00

Y 0.78 1.00 0.40 1.00 0.74 1.00 0.85 1.00 0.81 1.00

Y 0.79 1.00 0.20 1.00 0.75 1.00 0.88 1.00 0.82 1.00

Y Y Y 0.82 1.00 0.40 1.00 0.77 1.00 0.87 1.00 0.83 1.00

Y Y 0.79 1.00 0.20 1.00 0.75 1.00 0.88 1.00 0.82 1.00

Y Y 0.77 1.00 0.20 1.00 0.72 1.00 0.81 1.00 0.78 1.00

Y Y 0.79 1.00 0.20 1.00 0.75 1.00 0.83 1.00 0.80 1.00

Y Y 0.80 1.00 0.40 1.00 0.77 1.00 0.89 1.00 0.83 1.00

Y Y 0.80 1.00 0.40 1.00 0.76 1.00 0.87 1.00 0.82 1.00

Y Y Y 0.78 1.00 0.20 1.00 0.74 1.00 0.85 1.00 0.80 1.00

Y Y Y Y 0.83 1.00 0.40 1.00 0.78 1.00 0.87 1.00 0.83 1.00

Y Y Y Y Y Y   0.77 1.00 0.20 1.00 0.73 1.00 0.84 1.00 0.79 1.00

Classification result (Discriminant analysis)

Discriminant analysis classification                        

Features Classification result

Power CPEI HjorthAsyme-

tryCoherenc

ePhase Delay

 Average

resultWorst case Sensitivity Specificity F-score

Test Train Test Train Test Train Test Train Test Train

Y 0.66 0.88 0.00 0.73 0.57 0.90 0.61 0.87 0.62 0.88

Y 0.64 0.98 0.00 0.92 0.57 0.98 0.70 0.99 0.64 0.98

Y 0.56 0.95 0.00 0.88 0.46 0.93 0.60 0.98 0.54 0.96

Y 0.64 0.86 0.20 0.81 0.56 0.86 0.73 0.86 0.64 0.86

Y 0.69 0.93 0.00 0.81 0.66 0.90 0.72 0.97 0.71 0.94

Y 0.70 0.84 0.40 0.81 0.61 0.79 0.84 1.00 0.71 0.88

Y Y Y 0.59 0.99 0.00 0.92 0.50 1.00 0.58 0.99 0.56 1.00

Y Y 0.58 0.96 0.00 0.85 0.48 0.97 0.61 0.96 0.55 0.97

Y Y 0.65 0.99 0.20 0.92 0.55 0.97 0.66 1.00 0.62 0.99

Y Y 0.65 1.00 0.20 1.00 0.55 1.00 0.78 1.00 0.65 1.00

Y Y 0.47 1.00 0.00 1.00 0.37 1.00 0.56 1.00 0.44 1.00

Y Y 0.52 0.96 0.00 0.88 0.41 0.94 0.52 0.99 0.47 0.97

Y Y Y 0.69 0.94 2.00 0.85 0.63 0.92 0.72 0.96 0.70 0.94

Y Y Y Y 0.53 0.96 0.00 0.88 0.46 0.95 0.57 0.97 0.53 0.96

Y Y Y Y Y Y   0.69 0.93 0.20 0.88 0.63 0.90 0.72 0.98 0.69 0.94

Classification result (Decision tree)

Decision tree classification                          

Features Classification result

Power CPEI HjorthAsyme-

tryCoherenc

ePhase Delay

 Average

resultWorst case Sensitivity Specificity F-score

Test Train Test Train Test Train Test Train Test Train

Y 0.79 0.92 0.40 0.85 0.77 0.91 0.70 0.94 0.78 0.92

Y 0.75 0.94 0.20 0.92 0.69 0.94 0.81 0.95 0.76 0.94

Y 0.65 0.92 0.00 0.81 0.58 0.90 0.66 0.95 0.64 0.92

Y 0.74 0.95 0.20 0.88 0.65 0.94 0.80 0.98 0.73 0.96

Y 0.74 0.94 0.40 0.88 0.70 1.00 0.78 0.90 0.76 0.95

Y 0.68 0.91 0.20 0.85 0.57 0.86 0.79 1.00 0.67 0.92

Y Y Y 0.70 0.98 0.20 0.88 0.61 1.00 0.74 0.97 0.68 0.98

Y Y 0.72 0.97 0.20 0.92 0.69 0.96 0.69 0.99 0.73 0.97

Y Y 0.72 0.92 0.00 0.92 0.68 0.95 0.74 1.00 0.73 0.97

Y Y 0.72 0.96 0.00 0.84 0.65 0.95 0.69 0.97 0.70 0.96

Y Y 0.73 0.95 0.20 0.88 0.68 1.00 0.65 0.91 0.71 0.96

Y Y 0.73 0.97 0.20 0.88 0.68 0.97 0.70 0.99 0.73 0.98

Y Y Y 0.71 0.97 0.20 0.92 0.68 0.96 0.67 0.99 0.72 0.97

Y Y Y Y 0.68 0.98 0.00 0.88 0.59 1.00 0.65 0.97 0.64 0.98

Y Y Y Y Y Y 0.72 0.98 0.20 0.92 0.67 1.00 0.71 0.96 0.72 0.98