Quantitative EEG - An Introduction

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Introduction

  • EEG can be analyzed quantitatively and qualitatively.
  • Quantitative Electroencephalography (qEEG) is the digital analysis of the raw EEG data. The other terms used in the field are “digital EEG,” “paperless EEG,’ as well as “EEG brain mapping.”
  • Quantitative EEG is defined as the application of mathematical and analytical techniques to characterize the EEG signal.
  • QEEG uses computers to quantify the electrical potential of 1-300Hz.
  • QEEG compresses the raw EEG data into the graphical display using mathematical and analytical methods.
  • The raw EEG data is analyzed using a mathematical algorithm, such as a Fast Fourier Transformation (FFT) or Fourier Analysis.
  • Visualize trends over time that can be missed on page-by-page analysis
    • See the forest not just the trees
  • Multimodal Monitoring in NeuroICU
    • It also allows for faster review of EEG in the ICU for seizure detection
    • Create unified with other diagnostic modalities to predict the eminent neurological decline
  • Allow non-neurologist to be able to respond to changing clinical activity
 
👉🏻
American Academy of Neurology defines Quantitative EEG (QEEG) as the mathematical processing of the digital EEG to highlight the specific waveform components, to transform EEGs into a format or domain that elucidates relevant information.

Utility of Quantitative EEG (QEEG)

  • Characterize the background activity
  • Detection, Representation, & Quantification
    • Abnormal activity for diagnostic purposes
    • Abnormal activity for treatment purposes
  • Visually summarize long-term trends in the EEG data.
  • Slowly progressive EEG changes can be spotted easily with help of QEEG
  • A quantified visual view can be used by non-neurologist to detect changes especially in NeuroICU settings
  • Detect delayed cerebral ischemia in Subarachnoid Hemorrhage.
  • Increase both the accuracy of diagnostic procedures as well as the safety and efficacy of treatments.

Non-frequency-based vs Frequency-based QEEG Tools

Non-frequency-based (Time-frequency analysis)

  • EEG is a shifting/varying signal whose power spectrum varies over time.
  • This variation is described using time-frequency tools.
  • These tools include:
    • Compressed spectral array (CSA)
      • a rarely used technique in which the power spectrum at each time point is displayed as a line graph.
    • Density spectral array (DSA)
      • encodes the amplitudes of the power spectrum in different colors and then displays them against time.
    • Rhythmicity Spectrogram;
      • a QEEG tool that highlights those elements of the DSA that have the highest amplitude and rhythmicity during a given time period.
      • It aids in highlighting the rhythmic activity associated with seizures.

Frequency-based

  • EEG can be filtered by passing the signal through various digital or analog filters, thereby, removing contributions from undesired frequencies.
  • Filtering is considered to be a basic part of both EEG collection and processing. It is also a QEEG tool.
  • Filtering allows for the analysis of specific frequency bands.
  • Also allows the removal of noise or vibrations.
  • Fourier Spectrum:
    • It is the graph of the amplitude against frequency.
    • It describes the input of different frequencies to the EEG for a given time period.
    • It is not constant with time.
    • It analyses the Power, Power Ratio, and Spectral Edge Frequency (SEF).
    • Power is defined as the area under the Fourier Spectrum Amplitude curve.
    • Table 1: Common EEG algorithms and Their Applications

      1A. Time-Domain Tools

      QEEG Algorithms
      Background Assessment
      Seizure Detection
      Ischemic Detection
      Amplitude-integrated EEG
      ✔️
      ✔️
      Envelope trend
      ✔️
      Burst-suppression index
      ✔️

      1B. Frequency-Domain Tools

      QEEG Algorithms
      Background Assessment
      Seizure Detection
      Ischemia Detection
      Spectrogram
      ✔️
      ✔️
      ✔️
      Alpha-delta ratio
      ✔️
      ✔️
      Alpha variability
      ✔️
      Asymmetry indices
      ✔️
      ✔️
      ✔️
      Rhythmicity spectrogram
      ✔️

      Figure 1: Compressed Spectral Array (CSA)

      notion image

      Figure 2: Density Spectral Array (DSA)

      notion image

Artifacts

  • The EEG is frequently contaminated with artifacts i.e signatures, not of neural origin.
  • Interpretation of QEEG can be confounded by various sources of artifacts.
  • Common sources of artifacts are
    • Eye blinking
    • Eye movements
    • Movement of head or body
    • Shivering; can result in tonic or phasic muscle contractions
    • Line noise
      • Mass machinery in the ICU produce 55-60 Hz artifacts
      • Oscillating beds and percussions
      • Pulse artifacts

De-artifacting EEG

  • Manual de-artifacting results in suboptimal inter and interrater reliability.
  • Techniques broadly categorized as
    • Artifact Correction
      • It is removing artifacts without removing underlying EEG signal
      • An example of fully automatic artifact correction is ADJUST, available for MATLAB programming
    • Artifact Rejection
      • It is removing EEG segments that contain artifacts.
      • An example of a fully automatic artifact rejection method is S.A.R.A.

Clinical Applications of QEEG

Seizure Detection

  • Specific trends in frequency, rhythmicity, asymmetry, and amplitude are useful in seizure detection.

Frequency Based

  • Color density spectral assay (CDSA) creates a three-dimensional graphical display based on frequency-based EEG data.
  • The power or amplitude is shown on the x-axis and frequency on the y-axis over time.
  • Seizures consist of an increase in frequency and amplitude.
  • Seizure activity will present as an increase in power, i.e. area under the curve at frequencies seen during the evolution of seizure.

Rhythmicity Spectrum

  • It forms a three-dimensional display with time on the x-axis and frequency on the y-axis.
  • Frequency is in logarithmic scale to accentuate lower frequencies.
  • They display components with a higher degree of Rhythmicity.
  • May be able to detect subtle seizures.

Asymmetry Index

  • It compares the power of a given frequency between the right and left hemispheres.
  • Absolute asymmetry index
    • Asymmetry is expressed as absolute values or percentages.
    • Higher the number, the greater the asymmetry.
  • Relative asymmetry index
    • Expressed as relative values.
    • Positive and Negative values differentiate between the right and left hemispheres, respectively.
  • Asymmetry Spectrogram
    • The color indicates the hemisphere that has a higher power at a given frequency.
    • Darker intensity colors indicate a greater level of asymmetry.
    • Asymmetry trends are well suited for the detection of focal seizures.

Amplitude-based Trends

  • Envelope Trend (ET)
    • It is based on amplitude only.
    • Raw EEG is divided into 10- to 20- seconds epochs.
    • For each epoch, a median amplitude is calculated and plotted over time.
    • This allows to filter out short-duration artifacts.
  • Amplitude-integrated EEG (aEEG)
    • Also calculated using amplitudes only.
    • For each data point, raw EEG data is filtered and rectified i.e. all values made positive.
    • The minimum and maximum amplitudes in a predefined time frame are displayed on a semilogarithmic scale.
    • Seizures appear as an increase in minimum amplitude.
    • aEEG is also used for cerebral functioning monitoring (CFM) for the detection of seizures and background activity in neonates.

Ischemia Detection

  • Ischemia monitoring is designed to accentuate raw EEG changes during ischemia;
    • Loss of fast activity
    • Increase in slow activity
  • These changes can be quantified by using Fourier transformation to derive the following:
    • Power ratios; the ratio of theta-alpha (6-14 Hz) to total power (1-20 Hz)
    • Percentages
    • Total power values of slow versus fast
  • The changes are displayed as:
    • Color spectral assay (CDSA)
    • Line graphs
    • Histograms

Encephalopathies and Delirium

  • The QEEG is used in the evaluation of encephalopathies.
  • The encephalopathies can be caused by a diverse range of causes including:
    • Creutzfeldt-Jakob disease; QEEG findings ranging from nonspecific findings such as slowing and frontal rhythmic delta activity to disease-specific periodic sharp wave complexes.
    • Hypoxic-ischemic Encephalopathy; the EEG activity in the range of 0-1Hz may carry important clinical information.
    • Uremic Encephalopathy
    • Hepatic Encephalopathy; changes in the spectral pattern during the development of hepatic coma are specific.
    • Methamphetamine abstinence; the QEEG shows abnormalities similar to that of generalized cerebral encephalopathy.
    • Baclofen Overdose; QEEG in intrathecal baclofen overdose shows quasiperiodic epileptiform discharges.
    • Coma; the EEG allows insight into the thalamocortical functions when it is clinically inaccessible.
  • Delirium often goes undiagnosed. Quantitative EEG can help differentiate delirious and non-delirious patients.
  • Variables best used to distinguish delirium include:
    • Amount of Theta EEG activity.
    • Relative Power in the Delta frequency band.
    • Amount of activity in the slow-wave band.

Learning Disorders

  • The correlation between intelligence and EEG can be tested by:
    • EEG Power
      • The higher the power, the higher is the IQ.
      • A high value of slow power is associated with low IQ.
    • EEG Network Properties
      • Coherence; is an amplitude independent measure. A low level of coherence is positively correlated with intelligence.
      • Phase delays; shorter the phase delay, the higher the IQ.
      • Non-linear dynamical models.
  • Used cautiously, the QEEG can be used to differentiate the learning disorders and ADHD in children.

Attentional Disorder

  • Patients with ADHD have an increased theta/beta ratio and an increase in theta waves.
  • However, as these findings can be found in other disorders clinical evaluation is needed.

Mood Disorders

Depression

  • Trait markers for depression are:
    • Frontal alpha asymmetry
    • Changes in frontal QEEG concordance
    • Asymmetry in frontotemporal slow-wave activity
    • Decreased inter-hemispheric coherence in delta and theta frequency bands
    • Increased delta and theta bipolar absolute powers of the right hemisphere
    • A higher percentage of theta in posterior brain areas
    • Changes in beta activity
  • These changes can detect depression with a 72–93% sensitivity and 75–88% specificity.

Dementia

  • There is a displacement of the background frequency activity into delta and theta ranges.
  • Decrease or drop out of the alpha central frequency to 6.0-8.0 Hz.
  • These changes usually occur in moderate to severe diseases.
💡
An alpha-like rhythm can be a diagnostic marker.

Parkinson Disease

  • QEEG use early in Parkinson's Disease can act as a predictor to development of cognitive impairment, depression, and REM Behaviour Disorders (RBD).
  • QEEG oscillatory changes can be present early in the course of depression and RBD.
  • EEG slowing is observed in RBD and can be an early indicator for cognitive impairment, preceding clinically manifested dementia.
  • Spectral Analysis can act as a biomarker for future cognitive impairment.

Sleep Disorders

  • A vital role is played by QEEG in detecting sleep apnea and other disorders by sensing and recording the brain’s activity.
  • In patients with Obstructive Sleep Apnea (OSA), a reduction in the centrotemporal delta power; i.e. a reduction of delta waves in the central as well as temporal electrodes, is seen.
  • This occurs due to the non-restorative and fragmented sleep experienced by these patients.
  • These changes in delta power are speculated to be contributors to daytime symptoms of non-alertness and non-attentiveness in such patients.
 

Intraoperative Applications of QEEG

Carotid Endarterectomy (CEA) Procedure

  • EEG changes during the cross-clamping of the internal carotid artery are well documented and published.
  • There is well established direct correlation between a decrease in the cerebral blood flow of 18 ml/gm/min and EEG changes.
  • CSA and DSA present better-quantified data for EEG analysis.

Transcarotid Artery Revascularization (TCAR) Procedure

  • TCAR is a minimally invasive ground-breaking surgical procedure used to treat plaque in the carotid artery. This technique involves relining the plaque and covering it so that it can remodel instead of removing the plaque. If the plaque is left untreated could cause a stroke. QEEG utilization can supplement the raw EEG for continuous EEG analysis to identify any ischemic changes.

Cardiac Bypass Surgery (CABG)

  • Changes in cerebral perfusion during heart surgeries induced hypo-perfusion are routinely monitored by EEG.
  • QEEG can analyze the complex disruptions in the brain’s functions during hypo-perfusion, anesthesia, and hypothermia.

Interventional Neuro Radiological (INR) Procedure

  • Interventional neuroradiology(INR) is a rapidly expanding area with the procedures used to treat patients with neurovascular diseases via endovascular access.
  • The common INR procedures include treatment for brain aneurysms, embolizations of neoplasms, embolizations of arteriovenous malformations of the brain and the spinal cord, carotid artery stenting, etc.
  • QEEG in addition to raw EEG can identify the brain ischemia in real-time.

Further Reading

  • LaRoche, S. M., & Haider, H. A. (2018). Handbook of ICU EEG monitoring. Springer Publishing Company.

Bibliography

  • Nuwer, M. (1997). Assessment of digital EEG, quantitative EEG, and EEG brain mapping: Report of the American Academy of Neurology and the American Clinical Neurophysiology Society. Neurology, 49(1), 277–292. doi:10.1212/wnl.49.1.277
  • Doyle, O. M., Greene, B. R., Murray, D. M., Marnane, L., Lightbody, G., & Boylan, G. B. (2007). The effect of frequency band on quantitative EEG measures in neonates with Hypoxic-ischaemic encephalopathy. 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. doi:10.1109/iembs.2007.4352391
  • Popken, R. J., Kropveld, D., Oosting, J., & Chamuleau, R. A. F. M. (1983). Quantitative Analysis of EEG Power Spectra in Experimental Hepatic Encephalopathy. Neuropsychobiology, 9(4), 235–243. doi:10.1159/000117971
  • Newton, T. F., Cook, I. A., Kalechstein, A. D., Duran, S., Monroy, F., Ling, W., & Leuchter, A. F. (2003). Quantitative EEG abnormalities in recently abstinent methamphetamine dependent individuals. Clinical Neurophysiology, 114(3), 410–415. doi:10.1016/s1388-2457(02)00409-1
  • Fakhoury, T., Abou-Khalil, B., & Blumenkopf, B. (1998). EEG changes in intrathecal baclofen overdose: a case report and review of the literature. Electroencephalography and Clinical Neurophysiology, 107(5), 339–342. doi:10.1016/s0013-4694(98)00085-6
  • CHABOT, R. (2005). The role of quantitative electroencephalography in child and adolescent psychiatric disorders. Child and Adolescent Psychiatric Clinics of North America, 14(1), 21–53. doi:10.1016/j.chc.2004.07.005
  • Coburn, K. L., Lauterbach, E. C., Boutros, N. N., Black, K. J., Arciniegas, D. B., & Coffey, C. E. (2006). The Value of Quantitative Electroencephalography in Clinical Psychiatry: A Report by the Committee on Research of the American Neuropsychiatric Association. The Journal of Neuropsychiatry and Clinical Neurosciences, 18(4), 460–500. doi:10.1176/jnp.2006.18.4.460
  • Bresnahan, S. M., & Barry, R. J. (2002). Specificity of quantitative EEG analysis in adults with attention deficit hyperactivity disorder. Psychiatry Research, 112(2), 133–144. doi:10.1016/s0165-1781(02)00190-7
  • WIESER, H., SCHINDLER, K., & ZUMSTEG, D. (2006). EEG in Creutzfeldt–Jakob disease. Clinical Neurophysiology, 117(5), 935–951. doi:10.1016/j.clinph.2005.12.0
  • Kanda, P. A. de M., Anghinah, R., Smidth, M. T., & Silva, J. M. (2009). The clinical use of quantitative EEG in cognitive disorders. Dementia & Neuropsychologia, 3(3), 195–203. doi:10.1590/s1980-57642009dn30300004
  • Keizer, A. W. (2019). Standardization and Personalized Medicine Using Quantitative EEG in Clinical Settings. Clinical EEG and Neuroscience52(2), 82–89. https://doi.org/10.1177/1550059419874945
  • Geraedts, V. J., Boon, L. I., Marinus, J., Gouw, A. A., van Hilten, J. J., Stam, C. J., Tannemaat, M. R., & Contarino, M. F. (2018). Clinical correlates of quantitative EEG in Parkinson disease. Neurology91(19), 871–883. https://doi.org/10.1212/wnl.0000000000006473
  • El-Mekkawy, L., El Salmawy, D., Basheer, M. A., Maher, E., & Nada, M. M. (2022). Screening of non-restorative sleep by quantitative EEG. The Egyptian Journal of Neurology, Psychiatry and Neurosurgery, 58(1). https://doi.org/10.1186/s41983-022-00446-0
 
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