Aetna considers the use of quantitative EEG (brain mapping), also known as BEAM (Brain Electrical Activity Mapping), medically necessary only as an adjunct to traditional EEG for any of the following:
For ambulatory recording of EEG to facilitate subsequent expert visual EEG interpretation; or
For continuous EEG monitoring by frequency-trending to detect early, acute intracranial complications in the operating room or intensive care unit (ICU); or
For evaluation of certain members with symptoms of cerebrovascular disease whose neuroimaging and routine EEG studies are not conclusive; or
For evaluation of dementia and encephalopathy when the diagnosis remains unresolved after initial clinical evaluation; or
For screening for possible epileptic seizures in high-risk ICU members; or
For screening for possible epileptic spikes or seizures in long-term EEG monitoring; or
For topographic voltage and dipole analysis in pre-surgical evaluations for intractable epilepsy.
In accordance with the American Academy of Neurology/American Clinical Neurophysiology Society's assessment and available evidence, Aetna considers the use of quantitative EEG experimental and investigational for all other indications, including any of the following diagnoses because there is inadequate scientific evidence to prove its clinical usefulness for these indications:
Asperger syndrome and other autism spectrum disorders
Quantitative EEG (qEEG) is a method of analyzing the electrical activity of the brain to derive quantitative patterns that may correspond to diagnostic information and/or cognitive deficits.
Quantitative EEG, a technique for topographic display and analysis of brain electrophysiological data, has been proposed for use in the diagnosis of various psychiatric disorders. Clinical studies have demonstrated distinctive forms of brain electrical activity in psychiatric conditions including attention deficit disorder, schizophrenia, major depression, and obsessive-compulsive disorder. However, the clinical significance of these distinctive patterns of brain wave activity is unknown. Thus the role of quantitative EEG in diagnosis, evaluation of disease progression, and treatment of these conditions has yet to be elucidated. A report from the American Academy of Neurology and the American Clinical Neurophysiology Society concluded that quantitative EEG remains investigational for clinical use in post-concussion syndrome, mild-to-moderate head injury, learning disability, attention disorders, schizophrenia, depression, alcoholism, and drug abuse.
Clinical studies have demonstrated distinctive forms of brain electrical activity in neurologic and psychiatric conditions including learning disabilities, autism, traumatic brain injury, coma, schizophrenia, major depression, and obsessive-compulsive disorder. However, the clinical significance of these distinctive patterns of brain wave activity is unknown. Thus the role of quantitative EEG in diagnosis, evaluation of disease progression, and treatment of these conditions has yet to be elucidated.
Quantitative EEG has been proposed for use in a broad array of potential applications. This evidence has focused on the diagnostic accuracy of QEEG. There is, however, a paucity of evidence regarding its clinical utility.
There are no current guidelines from leading medical professional organizations recommending the use of quantitative EEG as a screening test for neurological and psychiatric conditions. In addition, there are no peer-reviewed published prospective studies of the use of quantitative EEG screening for these conditions showing that management is altered such that clinical outcomes are improved.
There are no published clinical studies demonstrating that use of quantitiative EEG reduces the number of imaging studies or other follow-up tests. In addition, there are no current guidelines from leading medical professional organizations recommending the use of quantitive EEG either as a prerequisite to, or as a replacement for, imaging studies.
While there is some evidence that electroencephalograph activity differs between normal control subjects and subjects suffering from tinnitus, additional evidence is needed to evaluate the value of including quantitative EEG in a battery of electrophysiological tests for the clinical identification of a predominantly central type of tinnitus. In addition, there is little evidence to support the use of quantitative EEG to determine the need for change of medications in the treatment of tinnitus.
Some investigators have proposed use of quantitative EEG in psychiatric cases to facilitate selection of medications. However, there is a lack of reliable evidence from prospective studies demonstrating that clinical outcomes are improved by basing selection of psychotropic medications on quantitative EEG results compared to empiric selection. The FDA approved prescribing information for psychotropic medications includes no recommendation for use of quantitative EEG in selection or dosing, and there are no current guidelines from leading medical professional organizations recommending such use of quantitative EEG.
Crumbley and associates (2005) examined the use of quantitative EEG in predicting response to psychotropic medication. The clinical outcomes of 2 groups of patients were compared: (i) those with prescribed medication regimens that were concordant with the quantitative EEG predictors, and (ii) those whose medication regimens were discordant with the quantitative EEG predictors. Participants included 70 adolescent inpatients who were administered quantitative EEG upon admission. The results indicated no significant difference in clinical outcome between the two groups. The failure of this study to find significant differences in patient outcomes questions this particular use of the quantitative EEG (Crumbley et al, 2005).
John and Prichep (2006) noted that as quantitative EEG and pharmaco-EEG have evolved, a vast body of facts has been accumulated, describing changes in the EEG or event-related potentials observed in a variety of brain disorders or after administration of a variety of medications. With some notable exceptions, these studies have tended to be phenomenological rather than analytical. There has not been a systematic attempt to integrate these phenomena to provide better understanding of how the abnormal behaviors of a particular psychiatric patient might be related to the specific pattern of the deviant electrical activity, nor just how pharmacological reduction of that deviant activity may have resulted in more normal behavior.
There is insufficient evidence to support the use of quantitative EEG in the diagnosis and/or classification of attention-deficit hyperactivity disorder (ADHD) (Krull, 2009). Several studies have demonstrated differences in qEEG between groups of children with ADHD and normal children. However, these studies are limited by non-random assignment, lack of blinding, failure to consider comorbidities, and/or failure to control for pharmacologic therapy. In addition, the specificity of the findings for ADHD has not been demonstrated.
Snyder and Hall (2006) performed a meta-analysis on the use of quantitative EEG in evaluating patients with ADHD. The 9 eligible studies (n = 1,498) observed quantitative EEG traits of a theta power increase and a beta power decrease, summarized in the theta/beta ratio with a pooled effect size of 3.08 (95 % confidence interval: 2.90 to 3.26) for ADHD versus controls (normal children, adolescents, and adults). These investigators concluded that this meta-analysis supports that a theta/beta ratio increase is a commonly observed trait in patients with ADHD relative to normal controls. Moreover, they noted that since it is known that the theta/beta ratio trait may arise with other conditions, a prospective study covering differential diagnosis would be needed to determine generalizability to clinical applications. Furthermore, standardization of the quantitative EEG technique is also needed, specifically with control of mental state, drowsiness, and medication.
Although QEEG may prove to be helpful in the diagnosis and/or classification of ADHD in the future, at present, there is insufficient evidence to support its use in clinical populations.
Much of the literature submitted focuses on the use of QEEG in the early detection of dementia. Although several markers of early dementia have been reported in the literature, there is a lack of evidence that early detection of dementia alters clinical management such that outcomes are improved, especially given the lack of robust treatments available.
An assessment by the Swedish Office of Health Technology Assessment (SBU, 2008) found insufficient evidence to support the use of quantitative EEG in dementia. The SBU assessment stated: "[t]here is limited evidence that either visually rated EEG or qEEG helps the diagnostic workup differentiate AD (Alzheimer’s Disease) patients from controls or AD from other dementia disorders."
Klassen et al (2011) evaluated qEEG measures as predictive biomarkers for the development of dementia in Parkinson disease (PD). Preliminary work shows that qEEG measures correlate with current PD cognitive state. A reliable predictive qEEG biomarker for PD dementia (PD-D) incidence would be valuable for studying PD-D, including treatment trials aimed at preventing cognitive decline in PD. A cohort of subjects with PD in the authors' brain donation program utilizes annual pre-mortem longitudinal movement and cognitive evaluation. These subjects also undergo biennial EEG recording. EEG from subjects with PD without dementia with follow-up cognitive evaluation was analyzed for qEEG measures of background rhythm frequency and relative power in δ, α, and β bands. The relationship between the time to onset of dementia and qEEG and other possible predictors was assessed by using Cox regression. The hazard of developing dementia was 13 times higher for those with low background rhythm frequency (lower than the grand median of 8.5 Hz) than for those with high background rhythm frequency (p < 0.001). Hazard ratios (HRs) were also significant for greater than median bandpower (HR = 3.0; p = 0.004) compared to below, and for certain neuropsychological measures. The HRs for δ, α, and β bandpower as well as baseline demographic and clinical characteristics were not significant. The authors concluded that qEEG measures of background rhythm frequency and relative power in the band are potential predictive biomarkers for dementia incidence in PD. These QEEG biomarkers may be useful in complementing neuropsychological testing for studying PD-D incidence.
Marzano and colleagues (2008) stated that in the last 2 decades quantitative EEG analysis has been used to examine the neurophysiological characteristics of insomnia. These studies provided evidence in support of the hypothesis that primary insomnia is associated with hyper-arousal of central nervous system and altered sleep homeostasis. However, these researchers have here underlined that these results have intrinsic methodological problems, mainly related to constraints of standard assessment in clinical research. They have proposed that future studies should be performed on larger samples of drug-free patients, using within-subjects designs and longitudinally recording patients adapted to sleep laboratory. All these methodological improvements will allow to partial out the contribution of individual differences, pharmacological influences and first-night effects on EEG frequencies. Moreover, they have discussed the potential relevance of recent findings from basic research concerning local changes during physiological sleep, which could be extended to the study of insomnia.
Hargrove and colleagues (2010) stated that there is increasing acceptance that pain in fibromyalgia (FM) is a result of dysfunctional sensory processing in the spinal cord and brain, and a number of recent imaging studies have demonstrated abnormal central mechanisms. These researchers compared quantitative electroencephalogram (qEEG) measures in 85 FM patients with age- and gender-matched controls in a normative database. A statistically significant sample (minimum 60 seconds from each subject) of artifact-free EEG data exhibiting a minimum split-half reliability ratio of 0.95 and test-retest reliability ratio of 0.90 was used as the threshold for acceptable data inclusion. Electroencephalograms of FM subject were compared to EEGs of age- and gender-matched healthy subjects in the Lifespan Normative Database and analyzed using NeuroGuide 2.0 software. Analyses were based on spectral absolute power, relative power and coherence. Clinical evaluations included the Fibromyalgia Impact Questionnaire (FIQ), Beck Depression Inventory and Fischer dolorimetry for pain pressure thresholds. Based on Z-statistic findings, the EEGs from FM subjects differed from matched controls in the normative database in 3 features: (i) reduced EEG spectral absolute power in the frontal International 10-20 EEG measurement sites, particularly in the low- to mid-frequency EEG spectral segments; (ii) elevated spectral relative power of high frequency components in frontal/central EEG measurement sites; and (iii) widespread hypo-coherence, particularly in low- to mid-frequency EEG spectral segments, in the frontal EEG measurement sites. A consistent and significant negative correlation was found between pain severity and the magnitude of the EEG abnormalities. No relationship between EEG findings and medicine use was found. The authors concluded that qEEG analysis reveals significant differences between FM patients compared to age- and gender-matched healthy controls in a normative database, and has the potential to be a clinically useful tool for assessing brain function in FM patients.
Hathi et al (2010) assessed an EEG-based index, the Cerebral Health Index in babies (CHI/b), for identification of neonates with high Sarnat scores and abnormal EEG as markers of hypoxic ischemic encephalopathy (HIE) after perinatal asphyxia. This was a retrospective study using 30-min EEG data collected from 20 term neonates with HIE and 20 neurologically normal neonates. The HIE diagnosis was made on clinical grounds based on history and examination findings. The maximum-modified clinical Sarnat score was used to grade HIE severity within 72 hrs of life. All neonates underwent 2-channel bedside EEG monitoring. A trained electroencephalographer blinded to clinical data visually classified each EEG as normal, mild or severely abnormal. The CHI/b was trained using data from Channel 1 and tested on Channel 2. The CHI/b distinguished among HIE and controls (p < 0.02) and among the 3 visually interpreted EEG categories (p < 0.0002). It showed a sensitivity of 82.4 % and specificity of 100 % in detecting high grades of neonatal encephalopathy (Sarnat 2 and 3), with an area under the receiver operator characteristic (ROC) curve of 0.912. CHI/b also identified differences between normal versus mildly abnormal (p < 0.005), mild versus severely abnormal (p < 0.01) and normal versus severe (p < 0.002) EEG groups. An ROC curve analysis showed that the optimal ability of CHI/b to discriminate poor outcome was 89.7 % (sensitivity: 87.5 %; specificity: 82.4 %). The authors concluded that the CHI/b identified neonates with high Sarnat scores and abnormal EEG. These results support its potential as an objective indicator of neurological injury in infants with HIE.
Lopes et al (2010) examined and compared the brain cortical activity, as indexed by qEEG power, coherence and asymmetry measures, in panic disorder patients during an induced panic attack with a 35 % CO(2) challenge test and also in a resting condition. A total of 15 subjects with panic disorder were randomly assigned to both 35 % CO(2) mixture and atmospheric compressed air, in a double-blind study design, with EEG being recorded for a 20-min period. During induced panic attacks, a reduced right-sided frontal orbital asymmetry in the beta band, a decreased occipital frontal intra-hemispheric coherence in the delta band at both right and left sides, a left-sided occipital delta inter-hemispheric asymmetry and an increased relative power in the beta wave at T4 were observed. These data showed a disturbed frontal cortical processing, pointing to an imbalance of the frontal and occipital sites, common to both hemispheres, and an increased right posterior activity related to the high arousing panic attack condition. These findings corroborated the neuroanatomical hypothesis of panic disorder.
Velasques et al (2013) examined the relationship between cortical gamma coherence within patients with bipolar disorder and a control group during a pro-saccadic attention task. These investigators hypothesized that gamma coherence oscillations act as a main neural mechanism underlying information processing which changes in bipolar patients. A total of 32 subjects (12 healthy controls and 20 bipolar patients) were enrolled in this study. Participants performed a pro-saccadic attention task while their brain activity pattern was recorded using qEEG (20 channels). These researchers observed that the maniac group presented lower saccade latency when compared to depression and control groups. The main finding was a greater gamma coherence for control group in the right hemisphere of both frontal and motor cortices caused by the execution of a pro-saccadic attention task. The authors concluded that these findings suggested a disrupted connection of the brain's entire functioning of maniac patients and represented a deregulation in cortical inhibitory mechanism. Thus, these results reinforce the hypothesis that greater gamma coherence in the right and left frontal cortices for the maniac group produces a "noise" during information processing and highlights that gamma coherence might be a biomarker for cognitive dysfunction during the manic state. The authors stated that these findings need to be confirmed in larger samples and in bipolar patients before start the pharmacological treatment.
CPT Codes / HCPCS Codes / ICD-9 Codes
CPT codes covered if selection criteria are met:
Other CPT codes related to the CPB:
95812 - 95830
HCPCS code covered if selection criteria are met:
Topographic brain mapping
ICD-9 codes covered if selection criteria are met (not all-inclusive):
046.0 - 046.9
Slow virus infection of central nervous system
290.0 - 290.9
Senile and presenile organic psychotic conditions
Dementia in conditions classified elsewhere without behavioral disturbance
Dementia in conditions classified elsewhere with behavioral disturbance
Other persistent mental disorders due to conditions classified elsewhere
323.71 - 323.72
Toxic encephalitis, myelitis, and encephalomyelitis
345.00 - 345.91
Epilepsy and recurrent seizures
Anoxic brain damage
348.30 - 348.39
Encephalopathy, not elsewhere classified
433.00 - 438.9
Occlusion and stenosis of precerebral arteries, occlusion of cerebral arteries, transient cerebral ischemia, acute, but ill-defined cerebrovascular disease, other and ill-defined cerebrovascular disease, and late effects of cerebrovascular disease
Post traumatic seizures
Toxic effect of inorganic lead compounds
997.00 - 997.09
Nervous system complications
ICD-9 codes not covered for indications listed in the CPB:
291.0 - 291.9
Alcoholic induced mental disorders
292.0 - 292.9
Drug induced mental disorders
295.00 - 295.95
296.00 - 296.99
Episodic mood disorders [bipolar disorder]
Depressive type psychosis
299.00 - 299.91
Pervasive developmental disorders
Panic disorder without agoraphobia
303.00 - 303.93
Alcohol dependence syndrome
304.00 - 305.93
Drug dependence and nondependent abuse of drugs
Transient disorder of initiating or maintaining sleep
Persistent disorder of initiating or maintaining sleep
Other specific disorders of sleep of nonorganic origin
Depressive disorder, not elsewhere classified
314.00 - 314.9
Hyperkinetic syndrome of childhood
315.00 - 315.9
Specific delays in development
327.00 - 327.09
Organic disorder of initiating or maintaining sleep [organic insomnia]
332.0 - 332.1
388.30 - 388.32
Myalgia and myositis, unspecified [fibromyalgia]
768.70 - 768.73
Hypoxic-ischemic encephalopathy (HIE)
Insomnia with sleep apnea, unspecified
850.00 - 854.19
Intracranial injury, excluding those with skull fracture
Head injury, unspecified
Behavioral insomnia of childhood
The above policy is based on the following references:
American Academy of Neurology. Assessment: EEG brain mapping. Report of the American Academy of Neurology, Therapeutics and Technology Assessment Subcommittee. Neurology. 1989;39(8):1100-1101.
American Psychiatric Association. Quantitative electroencephalography: A report on the present state of computerized EEG techniques. American Psychiatric Association Task Force on Quantitative Electrophysiological Assessment. Am J Psychiatry. 1991;148(7):961-964.
Kahn EM. Imaging of brain electrophysiologic activity: Applications in psychiatry. Gen Hosp Psychiatry. 1992;14(2):99-106.
Stam CJ, Jelles B, Achtereekte HA, et al. Diagnostic usefulness of linear and nonlinear quantitative EEG analysis in Alzheimer's disease. Clin Electroencephalography. 1996;27(2):69-77.
Kuperman S, Johnson B, Arndt S, et al. Quantitative EEG differences in a nonclinical sample of children with ADHD and undifferentiated ADD. J Am Acad Child Adolesc Psychiatry. 1996;35(8):1009-1017.
Nuwer M. Assessment of digital EEG, quantitative EEG, and EEG brain mapping: Report of the American Academy of Neurology and the American Clinical Neurophysiology Society. Neurology. 1997;49(1):277-292.
Hoffman DA, Lubar JF, Thatcher RW, et al. Limitations of the American Academy of Neurology and American Clinical Neurophysiology Society paper on QEEG. J Neuropsychiatry Clin Neurosci. 1999;11(3):401-407.
Small JG, Milstein V, Malloy FW, et al. Clinical and quantitative EEG studies of mania. J Affect Disord. 1999;53(3):217-224.
Hughes JR, John ER. Conventional and quantitative electroencephalography in psychiatry. J Neuropsychiatry Clin Neurosci. 1999;11(2):190-208.
Claus JJ, Kwa VI, Teunisse S, et al. Slowing on quantitative spectral EEG is a marker for rate of subsequent cognitive and functional decline in early Alzheimer disease. Alzheimer Dis Assoc Disord. 1998;12(3):167-174.
Mai R, Facchetti D, Micheli A, et al. Quantitative electroencephalography in amyotrophic lateral sclerosis. Electroencephalogr Clin Neurophysiol. 1998;106(4):383-386.
Claus JJ, Ongerboer de Visser BW, Walstra GJ, et al. Quantitative spectral electroencephalography in predicting survival in patients with early Alzheimer disease. Arch Neurol. 1998;55(8):1105-1111.
Drake ME, Padamadan H, Newell SA. Interictal quantitative EEG in epilepsy. Seizure. 1998;7(1):39-42.
Ebersole JS. New applications of EEG/MEG in epilepsy evaluation. Epilepsy Res Suppl. 1996;11:227-237.
Jacobs MP, Fischbach GD, Davis MR, et al. Future directions for epilepsy research. Neurology. 2001;57(9):1536-1542.
Procaccio F, Polo A, Lanteri P, et al. Electrophysiologic monitoring in neurointensive care. Curr Opin Crit Care. 2001;7(2):74-80.
Wallace BE, Wagner AK, Wagner EP, et al. A history and review of quantitative electroencephalography in traumatic brain injury. J Head Trauma Rehabil. 2001;16(2):165-190.
Barry RJ, Clarke AR, Johnstone SJ. A review of electrophysiology in attention-deficit/hyperactivity disorder: I. Qualitative and quantitative electroencephalography. Clin Neurophysiol. 2003;114(2):171-183.
Weiler EW, Brill K, Tachiki KH, Wiegand R. Electroencephalography correlates in tinnitus. Int Tinnitus J. 2000;6(1):21-24.
Shulman A, Goldstein B. Quantitative electroencephalography: Preliminary report--tinnitus. Int Tinnitus J. 2002;8(2):77-86.
Chabot RJ, di Michele F, Prichep L. The role of quantitative electroencephalography in child and adolescent psychiatric disorders. Child Adolesc Psychiatr Clin N Am. 2005;14(1):21-53, v-vi.
Nuwer MR, Hovda DA, Schrader LM, Vespa PM. Routine and quantitative EEG in mild traumatic brain injury. Clin Neurophysiol. 2005;116(9):2001-2025.
Crumbley JA, DeFilippis NA, Dsurney J, Sacco A. The neurometric-quantitative electroencephalogram as a predictor for psychopharmacological treatment: An investigation of clinical utility. J Clin Exp Neuropsychol. 2005;27(6):769-778.
John ER, Prichep LS. The relevance of QEEG to the evaluation of behavioral disorders and pharmacological interventions. Clin EEG Neurosci. 2006;37(2):135-143.
Snyder SM, Hall JR. A meta-analysis of quantitative EEG power associated with attention-deficit hyperactivity disorder. J Clin Neurophysiol. 2006;23(5):440-455.
Bares M, Brunovsky M, Kopecek M, et al. Changes in QEEG prefrontal cordance as a predictor of response to antidepressants in patients with treatment resistant depressive disorder: A pilot study. J Psychiatr Res. 2007;41(3-4):319-325.
Purins A, Hiller J. Quantitative EEG for predicting patient response to antidepressants. Australia and New Zealand Horizon Scanning Network. Prioritising Summary. Volume 17. Canberra, ACT: Australian Government; August 2007.
Pichon Riviere A, Augustovski F, Cernadas C, et al. Cerebral mapping [summary]. IRR No. 100. Buenos Aires, Argentina: Institute for Clinical Effectiveness and Health Policy (IECS); 2007.
Marzano C, Ferrara M, Sforza E, De Gennaro L. Quantitative electroencephalogram (EEG) in insomnia: A new window on pathophysiological mechanisms. Curr Pharm Des. 2008;14(32):3446-3455.
Galderisi S, Mucci A, Volpe U, Boutros N. Evidence-based medicine and electrophysiology in schizophrenia. Clin EEG Neurosci. 2009;40(2):62-77.
Hargrove JB, Bennett RM, Simons DG, et al. Quantitative electroencephalographic abnormalities in fibromyalgia patients. Clin EEG Neurosci. 2010;41(3):132-139.
Hathi M, Sherman DL, Inder T, et al. Quantitative EEG in babies at risk for hypoxic ischemic encephalopathy after perinatal asphyxia. J Perinatol. 2010;30(2):122-126.
Lopes FL, Oliveira MM, Freire RC, et al. Carbon dioxide-induced panic attacks and quantitative electroencephalogram in panic disorder patients. World J Biol Psychiatry. 2010;11(2 Pt 2):357-363.
Klassen BT, Hentz JG, Shill HA, et al. Quantitative EEG as a predictive biomarker for Parkinson disease dementia. Neurology. 2011;77(2):118-124.
Tye C, McLoughlin G, Kuntsi J, Asherson P. Electrophysiological markers of genetic risk for attention deficit hyperactivity disorder. Expert Rev Mol Med. 2011;13:e9.
Velasques B, Bittencourt J, Diniz C, et al. Changes in saccadic eye movement (SEM) and quantitative EEG parameter in bipolar patients. J Affect Disord. 2013;145(3):378-385.
Copyright Aetna Inc. All rights reserved. Clinical Policy Bulletins are developed by Aetna to assist in administering plan benefits and constitute neither offers of coverage nor medical advice. This Clinical Policy Bulletin contains only a partial, general description of plan or program benefits and does not constitute a contract. Aetna does not provide health care services and, therefore, cannot guarantee any results or outcomes. Participating providers are independent contractors in private practice and are neither employees nor agents of Aetna or its affiliates. Treating providers are solely responsible for medical advice and treatment of members. This Clinical Policy Bulletin may be updated and therefore is subject to change.