qEEG Aspects of Sleep Apnea

qEEG Aspects of Sleep Apnea

Jon Fonseca and Richard Soutar, Ph.D.

Keywords: qEEG, Sleep Apnea, Neurofeedback, Brainmapping

Sleep Apnea is a disorder that has been progressively gathering more attention over the last two decades as a problem that has been affecting more and more aging Americans. Kocak et al (2012) note that as early as 1997 the ASDA American Sleep Disorder Association (ASDA) defined obstructive sleep apnea syndrome or OSA as, “A syndrome characterized by recurrent obstructions in upper respiratory tract (URT) during sleep and seen often with a decrease in oxygen saturation.”  There are three basic types of Sleep Apnea and Obstructive Apnea (OSA) has the greatest prevalence. It comprises a significant portion of the over 80 existing sleep disorders.  According to the ASDA, statistical studies place the contemporary prevalence in the range of 1-5 % of the population. Key waking symptoms are excessive daytime drowsiness and fatigue, restlessness, moodiness and forgetfulness according to the NIH.

 

Little has been done in the way of qEEG analysis of the waking EEG with respect to OSA but considerable research has been done on EEG changes during sleep that are associated with Obstructive Sleep Apnea and methods have been developed to use EEG to detect the various kinds of sleep Apnea (Kocak et al, 2012). Karakis et al (2013) have reported that there is increasing value in using EEG technology to assess OSA during sleep in the traditional context of assessment.  Grenèche et al (2008) on the other hand did a study to determine the value of assessing Apnea patients with waking qEEG measures.  These authors found that the key waking qEEG feature found in OSA was increased .5-7.8Hz EEG as well as increased 12.7-29.2Hz.  This would indicate that qEEG findings should indicate increased delta and theta as well as some increases in beta and high beta frequencies.

It is well understood among neurofeedback practitioners that daytime drowsiness can appear as elevated delta, theta and alpha, however the spectral pattern varies significantly between individuals depending on other disorders they may have that also alter qEEG patterns.  Gloor et al (1986) reported several decades ago that deficits in acetylcholine manifest as elevated 2.5Hz delta.  At the same time, the research also indicates that Apnea has a degenerative effect on the brain which affects white matter (Alchanatis et al, 2004; Castrovanao et al, 2014).  The causal mechanism is still hotly debated and is likely bidirectional in that the condition leads to other problems that continue to promote white matter damage (da Silva Bahia & Pereira, 2015; Huynh et al 2014).   The indication of white matter degradation on the EEG is quite clear and well established according to Kanyazev (2012) and Neidermeyer (2006). Both report that elevated delta is a key signature of inflammation and Kanyazev (2012) cites several studies showing clear links between elevated delta and inflammation of underlying white matter.  This being the case, it should be expected that significant elevation in delta as compared to other frequencies should be readily observable in OSA cases in comparison to the normative distribution. Why beta should become elevated is unclear other than perhaps as an initial compensatory response to increased slowing in the EEG and increasing drowsiness.

Given the findings of the present research as described above we would expect that the majority of these individuals with OSA to exhibit considerable slow wave activity in the form of elevated diffuse delta activity and perhaps elevated theta as well.  The clinician mapping the subjects in this study did not observe any elevations in the beta frequencies when inspecting the maps and saw only intermittently observed elevated theta in a few subjects.  He did however observe a consistent elevated diffuse delta pattern.  Consequently, we reviewed the maps with the expectation that delta would be a good indicator of OSA when present in conjunction with the report of the typical symptoms.

Subjects

Subjects consisted of 205 clients, 133 male and 72 female, from a major wellness center in southern California.  Ages ranged from 45-85.  Clients were divided into two groups based on whether they had a diagnosis of Sleep Apnea or not, based on standard tests from sleep clinics.

Methods

A Quantitative EEG test was performed on each client.  Data was collected from 12 locations on the head using an electro-cap with linked ears.  Data was collected using a two channel Neuro-Integrator using a sequential collection technique.  One minute of data was collected from each homologous location pair.  Data was inspected for artifact and then processed using the Clear Mind Neuromap System based on the New Mind Normative Matrix.  This matrix contains a normative sample of 578 individuals scoring in the normal range using neurocognitive testing.  The New Mind database is age regressed and has been cross validated with other published normative databases.  Resulting maps with global elevated delta were identified and recorded.  Data was compared using contingency tables and Chi Square analysis to determine if there was a significant difference between groups with Apnea having high delta and groups with Apnea having low or normal delta.

Results

Results indicate a significant difference between groups as shown below.  There were 173 clients with Apnea and diffuse high delta and seven clients with high delta without sleep Apnea.  Fifteen clients had a diagnosis of Apnea but no high delta.  Ten clients had no Apnea and no High delta.  Chi Square test statistic (37.64) was clearly significant at p=.05.

Discussion

Our expectation that delta would be globally elevated, based on the literature and casual observation, was clearly met.  Those who had Apnea but no elevated delta may have either been misdiagnosed or in the early stages of development.  Given that delta is a function of inflammatory process and is proportional to the magnitude or power of the delta present (Knyazev, 2012), early stages of the disorder may not involve inflammation significant enough to generate abnormal levels of delta. The low number of individuals in this population with no Apnea indicates that the volunteer sample was likely biased but also indicative of the incidence of the disorder in an elderly population with significant health problems. It is clear that elderly individuals with a high level of diffuse delta are likely to have sleep Apnea but they may have arrived at that condition as a consequence of an inflammatory process related to other problems as well.  It is important to keep in mind that diffuse neuroinflammation can be a consequence of many other issues including MCI, Dementia, TBI, NED, and natural demyelination processes relating to aging.  Psychiatric interview and symptomology are other critical features important to the diagnostic process to make the distinction.  These findings may contribute to the OSA diagnostic process but also provide an easy and inexpensive method to evaluate different interventions employed with patients to reduce OSA.  Recently we have found that utilization of appliances to reduce OSA show marked changes in delta amplitudes and daytime drowsiness in our clinical setting.  Future studies should also look more closely at variations in theta and especially beta frequencies to determine their possible presence or contribution.

 

References

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Castronovo, V., Scifo, P., Castellano, A., Aloia, M., Iadanza, A., Marelli, S., Capp, S., Strambi, L., Falini, A. (2014) White matter integrity in obstructive sleep apnea before and after treatment, Sleep,37(9),1465-75. doi: 10.5665/sleep.3994

Greneche, J., Kriegar, J., Erhardt, C., Bonnefond, A., Muzet, A., Tassi, P.(2008). EEG spectral power and sleepiness during 24 h of sustained wakefulness in patients with obstructive sleep apnea syndrome. Clin Neurophysiol, Feb;119(2):418-28.

Huynh, N., Prilipko, O., Guilleminault, C. (2014). Volumetric brain morphometry changes in patients with obstructive sleep apnea syndrome: effects of CPAP treatment and literature review. Frontiers in Neurology, 5 (58), 1 -9

Kocak, O, Bayrak, B, Erdamar, A, Ozparlak, L, Telatar, Z, Erogul, Osman (2012).  Automated detection and classification of sleep apnea types using electrocardiogram (ECG) and electroencephalogram (EEG) features.  In Richard Mills, (Ed.), Advances in Electrocardiograms- Clinical Applications (pp. 211-23). Rijeka, Croatia: Intech. ISBN: 978-953-307-902-8. www.intechopen.com.

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