The American Heart Association (AHA) recommends prompt electroencephalography (EEG) neuroprognostication for post-cardiac arrest patients in their 2020 guidelines on cardiopulmonary resuscitation (CPR) and emergency cardiovascular care (ECC). The prompt use of EEG in post-cardiac arrest patients is important because it allows for early identification of brain injury and can guide decisions about the continuation of life-sustaining treatment.

Hypoxic-ischemic brain injury is a leading cause of morbidity and mortality in survivors of hospital cardiac arrest.¹ Sadly, most of the post-resuscitation deaths are caused by the active withdrawal of life-sustaining treatment. The decision to actively remove life-sustaining treatment is made when a poor neurological outcome is expected. For this reason, it is critical to perform accurate neuroprognostication, as recommended by the American Heart Association (AHA) in their 2020 guidelines on cardiopulmonary resuscitation (CPR) and emergency cardiovascular care (ECC).²

Proper post-cardiac arrest neuroprognostication, or EEG after cardiac arrest, is essential to distinguish those who may achieve a meaningful neurological recovery from those who will inevitably have a poor neurological outcome.^2,3

Who Should Receive Neuroprognostication After Cardiac Arrest?

The AHA recommends multimodal neuroprognostication on all patients who remain comatose after cardiac arrest (Level 1 recommendation).² Multimodal neuroprognostication includes EEG, MRI, quantitative pupillometry, and serum neuron-specific enolase, among others. In addition to neuroprognostication, EEG testing is recommended to identify seizures and, if necessary, provide treatment.

Nonconvulsive seizures, for example, are common after cardiac arrest, and cannot be reliably detected without EEG.⁴ The American Academy of Neurology also provides data on its importance. Also, EEG after cardiac arrest should be used to rule out underlying ictal activity in cardiac arrest survivors with status myoclonus. The 2020 Emergency Cardiovascular Care Science with Treatment Recommendations (CoSTR) advises seizures to be treated when diagnosed in cardiac arrest patients with return of spontaneous circulation (ROSC).⁵

When Should Neuroprognostication After Cardiac Arrest Start?

Importantly, prognostic assessments should not be started too early. If they are administered too soon after the cardiac arrest and during initial post-resuscitation care, the results may appear worse than they actually are because of medications, or acute post-injury changes.⁶ Perhaps surprisingly, clinical prognostic testing such as pupillary light reflex should not be used for neuroprognostication until at least 5 days after ROSC (return of spontaneous circulation) in patients treated with targeted temperature management (TTM), in order for such testing to have prognostic significance.

Testing should not begin until the patient has been normothermic for at least 72 hours.^6-8 Imaging or EEG to detect status myoclonus may begin as early as 24 hours after ROSC; two studies including 347 patients, showed the presence of status myoclonus within 72 hours of ROSC predicted poor neurological outcome with specificity of 97% to 100%.^9,10 However, postanoxic status epilepticus may not manifest until 72 hours or more after ROSC and sedative drug dosages have been reduced, so waiting for neurological prognosis assessment is necessary.

How to Use EEG for Neuroprognostication After ROSC

The 2020 American Heart Association (AHA) Guidelines for Cardiopulmonary Resuscitation (CPR) and Emergency Cardiovascular Care (ECC) recommend the use of EEG after cardiac arrest in patients who remain in a coma after ROSC for the purposes of neuroprognostication.²

Findings that are consistent with poor outcomes include postanoxic status epilepticus and/or burst suppression 72 hours or more after ROSC.² Another potentially useful electrodiagnostic test is the somatosensory evoked potential (SSEP).

SSEP testing is conducted by stimulating the median nerve and looking for a resulting cortical N20 wave. N20 SSEP waves that are absent bilaterally correlate with poor prognosis.² Likewise, rhythmic periodic discharges on EEG are also consistent with poor prognosis.

Importantly, the AHA notes that the absence of EEG reactivity within 72 hours after cardiac arrest should not be used as the sole determinant of poor neurological outcomes. A lack of EEG reactivity during this time does not necessarily predict a poor neurological outcome.

Obtaining EEG after ROSC

Conventional EEG in the Intensive Care Unit is Critical Care and Cumbersome

While conventional EEG is an acceptable means to obtain EEG in the ICU, it is impractical. Post-cardiac arrest patients who need post-cardiac arrest care in the ICU will, as standard care, be intubated, and have central venous and possibly arterial lines, and intracardiac devices.

Conventional EEG after cardiac arrest is challenging. Attempting to obtain EEG signals through a dozen wires in a critical care setting without quality-limiting artifacts is challenging. Indeed, providing continuous conventional EEG in the ICU is a literal barrier to care for ICU or neurocritical care nurses and staff.

Zeto EEG – Wireless Full-Montage Monitoring For Use on Coma Patients After ROSC

According to the AHA, proper neuroprognostication could prevent withdrawal of life support as it will indicate the accurate neurologic prognosis of a patient who has a chance at successful neurological recovery. Thus, the AHA recommends performing multimodal neurologic prognostication including EEG in all patients who remain in a coma after ROSC following cardiac arrest.

Zeto has developed a full montage, 19-channel (10-20 system) wireless headset with dry electrodes for rapid EEG monitoring. The benefits of Zeto’s electrodes include no skin preparation, no cleanup, and no gel or paste residue. The electrodes are single-use and soft.

The Zeto EEG device collects high-quality EEG recordings and transmits them wirelessly to the cloud for remote viewing and interpretation. ZETO’s EEG platform also offers an FDA-cleared seizure detection and trending algorithm developed by Encevis.

The Zeto EEG headset can be placed after a short training, and the setup takes only 5 minutes – which is crucial for patients in a coma. Once placed, the Zeto headset provides continuous EEG monitoring for up to 5-6 hours.

Overall, prompt EEG for post-cardiac arrest patients is a vital aspect of proper neuroprognostication and plays an important role in determining prognosis in patients and guiding treatment decisions. It is important for healthcare providers, especially intensive care medical providers to be aware of the AHA guidelines and incorporate EEG into their standard care management of post-cardiac arrest patients.

When called upon to perform the difficult and highly consequential task of neuroprognostication, choose Zeto for EEG in the ICU.

References

1. Witten L, Gardner R, Holmberg MJ, et al. Reasons for death in patients successfully resuscitated from out-of-hospital and in-hospital cardiac arrest. Resuscitation. 2019;136:93-99. PMID:30710595 doi:10.1016/j.resuscitation.2019.01.031
2. Panchal AR, Bartos JA, Cabanas JG, et al. Part 3: Adult Basic and Advanced Life Support: 2020 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation. 2020;142(16_suppl_2):S366-S468. PMID:33081529 doi:10.1161/CIR.0000000000000916
3. Geocadin RG, Callaway CW, Fink EL, et al. Standards for Studies of Neurological Prognostication in Comatose Survivors of Cardiac Arrest: A Scientific Statement From the American Heart Association. Circulation. 2019;140(9):e517-e542. PMID:31291775 doi:10.1161/CIR.0000000000000702
4. Freund B, Kaplan PW. Myoclonus After Cardiac Arrest: Where Do We Go From Here? Epilepsy Curr. 2017 Sep-Oct;17(5):265-272. doi: 10.5698/1535-7597.17.5.265. PMID: 29225535; PMCID: PMC5716491.
5. Ryoo SM, Jeon SB, Sohn CH, et al. Predicting Outcome With Diffusion-Weighted Imaging in Cardiac Arrest Patients Receiving Hypothermia Therapy: Multicenter Retrospective Cohort Study. Crit Care Med. 2015;43(11):2370-2377. PMID:26284621 doi:10.1097/CCM.0000000000001263
6. Samaniego EA, Mlynash M, Caulfield AF, Eyngorn I, Wijman CA. Sedation confounds outcome prediction in cardiac arrest survivors treated with hypothermia. Neurocrit Care. 2011;15(1):113-119. PMID:20680517 doi:10.1007/s12028-010-9412-8
7. Berg KM, Soar J, Andersen LW, et al. Adult Advanced Life Support: International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations. Resuscitation. 2020. PMID:33098922 doi:10.1016/j.resuscitation.2020.09.012
8. Callaway CW, Donnino MW, Fink EL, et al. Part 8: Post-Cardiac Arrest Care: 2015 American Heart Association Guidelines Update for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation. 2015;132(18 Suppl 2):S465-482. PMID:26472996 doi:10.1161/CIR.0000000000000262
9. Ruknuddeen MI, Ramadoss R, Rajajee V, Grzeskowiak LE, Rajagopalan RE. Early clinical prediction of neurological outcome following out of hospital cardiac arrest managed with therapeutic hypothermia. Indian J Crit Care Med. 2015;19(6):304-310. PMID:26195855 doi:10.4103/0972-5229.158256
10. Zhou SE, Maciel CB, Ormseth CH, Beekman R, Gilmore EJ, Greer DM. Distinct predictive values of current neuroprognostic guidelines in post-cardiac arrest patients. Resuscitation. 2019;139:343-350. PMID:30951843 doi:10.1016/j.resuscitation.2019.03.035

Video EEG is an important tool in diagnosing and monitoring patients with epilepsy or other neurological conditions. It helps distinguish physiologic or external artifacts from epileptic seizures or epileptiform discharges associated with seizures. Video EEG also helps to differentiate epileptic seizures from psychogenic nonepileptic spells that are mistaken for seizures and other episodic abnormal movements associated with other conditions, such as tic disorders, tremor, or periodic limb movements of sleep. Simultaneous video and EEG recordings enable the establishment of correlations between abnormal movements and abnormal wave activity.^1,2,3,4

For awake patients, video can identify sources of noise such as muscle artifacts, blinking, chewing, forehead wrinkling, and even ear wiggling. For unconscious patients, video can identify external sources of artifact, such as electronic devices in the ICU (for example, an artificial respiration device). The inclusion of video in the EEG improves the ability to accurately diagnose a patient. Additionally, video can confirm correct electrode placement and headset positioning.

In this blog, we discuss the benefits of using video integration in EEG and what Zeto EEG can offer.

Different noise sources in EEG

EEG recordings can be impacted by various sources of noise and interference, including physiological noise, environmental noise, EEG electrode placement, electrical artifacts from EEG electrode malfunctions or from external electrical devices, and motion artifacts. These artifacts can make it difficult to distinguish genuine brain activity from noise, compromising the ability to accurately interpret EEG recordings.

Physiological noise arises from the body’s internal processes like muscle contractions, heartbeats, and eye movements, generating electrical signals that can interfere with the EEG recording. Environmental noise, on the other hand, encompasses external disturbances like electrical noise from equipment, electromagnetic interference, or ambient noise, which can introduce unwanted signals into the EEG data. Electrical artifacts can also be produced by the EEG equipment itself, resulting from issues like poor grounding, incorrect amplifier settings, or malfunctioning electrodes. Lastly, motion artifacts arise from bodily movements such as head motions, jaw opening and closing, or eye blinks, leading to distortions in the EEG signal and complicating accurate data interpretation.

To account for artifacts and enhance the interpretation of EEG recordings, video recording is often used in conjunction with EEG to identify and exclude sources of noise from the data.

Video EEG: Complement or Necessity?

Video is particularly useful for identifying artifacts related to movements or other physical activities that may produce electrical signals in the EEG recording. For example, if a patient moves their arm during an EEG recording, this movement can cause changes in the electrical activity picked up by the electrodes, which is recorded in the EEG. By observing the video recording, it is possible to identify the movement as an artifact and avoid misinterpreting the EEG data. This helps ensure that EEG signals identified as epileptiform activity represent genuine brain activity, rather than artifacts from other sources. Epileptiform waves can sometimes be difficult to distinguish from artifacts, as they can have similar characteristics in the EEG recording.

Video can also help diagnose and classify epileptic seizures and distinguish them from psychogenic nonepileptic spells and episodic abnormal movements due to other medical conditions that are not epileptic seizures.^1,2,3,4,5 For example, if a patient experiences an epileptic seizure during an EEG recording, the video can help correlate the EEG signal with the visible movements and behaviors of the seizure. A psychogenic nonepileptic spell will show motion artifacts on the EEG but will not show underlying abnormal brain wave activity. Furthermore, certain behaviors such as squeezing the eyes shut while shaking, awareness during the seizure, and returning to baseline mental status right after the event can suggest a psychogenic cause.⁵

Sometimes tics, tremors, muscle twitching, and other abnormal movements in the tongue or a limb or digit can be mistaken for epileptic seizures, particularly in the ICU setting where subclinical seizures occur more frequently.  Subclinical seizures are epileptic seizures that are not associated with characteristic abnormal movements usually associated with seizures.  Video EEG can determine if the suspicious subtle abnormal movements are in fact due to epileptic seizures in most cases.  If the abnormal movements represent subclinical epileptic seizures, they would be expected to correlate with epileptic seizure brain wave activity on the EEG.

Video recording provides additional information about the patient’s behavior or movements at the time of a seizure or seizure-like event, which is called a seizure’s semiology.^1,3,5 Gaze deviation and arm stretching right before the seizure or at seizure onset can point to the seizure origin in the brain and inform localization of seizure foci relative to brain hemispheres or specific areas such as the motor cortex, supplementary motor areas, and frontal and parietal eye fields. Seizure semiology also includes the subjective sensations and experiences immediately preceding and during a seizure.  It is critical to the diagnosis and classification of seizure disorders, which in turn affects the treatment plan. A video EEG is critical to presurgical evaluation prior to epilepsy surgery to diagnose and classify epileptic seizures when present and avoid unnecessary surgery due to psychogenic nonepileptic spells or other episodic abnormal movements not due to epileptic seizures.¹

Zeto EEG: Seamless Compatibility with Any Camera, Reimbursable, Supports Up to Four Cameras

Zeto’s video integration feature reshapes the users expectations of what is possible with modern technology. The Zeto system enables simple camera controls (zooming and panning) through the cloud, ensuring simple usability.

One of the most appreciated features by users of the Zeto platform is its compatibility with any integrated or USB enabled camera. That eliminates the need for additional camera purchases and helps operators to use Zeto EEG more easily across a wide range of settings and recording locations. The Zeto Cloud Platform supports the use of up to four cameras simultaneously, providing medical professionals with the flexibility to monitor patients from multiple angles.

In addition to its compatibility with integrated and external USB cameras, Zeto offers the simple integration of IP cameras, whether they are wired or wireless. This capability is particularly beneficial when medical personnel already have pre-installed IP cameras in the room, as they can add these cameras to the Zeto Cloud Platform for a seamless integration. IP camera integration often depends on existing local IT infrastructure and cybersecurity guidelines which can result in longer integration timelines compared to integrated or USB – based camera solutions. But with the added configuration time come additional conveniences and features (such as lowlight or night vision capabilities) which often make this extended setup more than worth it.

With any of these camera options, maximum image resolution depends on the utilized camera hardware, but integrated, USB, or IP camera options are available with Ultra 4k resolutions or higher. The consideration then becomes data storage size and related storage costs more than the technical ability – providers may still choose to save data in lower resolution settings simply to save storage costs.

Another powerful tool of the Zeto Cloud platform is the ability for the clinician to remotely access the recording through any device connected to the internet. Allowing for real time analysis when the provider is not present in the recording environment.

Medical practitioners will find the real-time monitoring capability invaluable, enabling them to observe patients with precision. Additionally, Zeto’s video EEGs, utilizing the integrated cameras, are eligible for reimbursement through CPT codes, specifically for EEGs lasting 24 hours or longer.

By utilizing video, Zeto ensures accurate positioning of the headset and proper electrode placement. This attention to detail guarantees the collection of reliable and precise EEG data.

In conclusion, video recording is an important complement to EEG recordings. It helps identify and exclude sources of artifact in EEG and confirms the presence of epileptic seizure activity and epileptiform discharges important for the diagnosis and classification of epileptic seizures. Video EEG also helps to identify psychogenic nonepileptic events and episodic abnormal movements from other medical conditions mistaken for epileptic seizures. By using video in conjunction with EEG, clinicians can improve the interpretation of EEG recordings, leading to more accurate diagnoses and better treatment outcomes for patients with neurological conditions.

Zeto’s video integration feature enhances convenience and accuracy, ultimately resulting in better diagnosis and treatment outcomes for patients.

Sources:

1. https://onlinelibrary.wiley.com/doi/10.1111/j.1528-1167.2006.00656.x
2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9259997/
3. https://researchopenworld.com/wp-content/uploads/2020/06/AWHC-3-3-315.pdf
4. https://www.sciencedirect.com/science/article/abs/pii/0887899495000217
5. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4206377/

Event Related Potentials (ERPs) in EEG provide insight into how our brain processes information, reacts to its environment and adapts to challenges. ERPs differ from the traditional clinical tradition of evaluating continuous spontaneous brainwaves in patients. With ERPs, experimenters can examine the brain’s response to succinct events. For a summary on ERPs, see here.

One of the most crucial aspects in capturing clean ERPs is knowing precisely when specific target events occur. So-called event markers are commonly used to timestamp the onset of target events in the continuous EEG tracings to enable further data processing. The target events can have different modalities and can either be initiated or perceived from the person receiving the EEG.

For example, participant initiated movements are known to elicit robust ERPs.^{1 2} However, more commonly, ERPs are recorded from participants perceiving sounds, language, images, or smells.^3,4,5 In principle, ERPs will emerge via subsequent processing as long as experimenters established a reliable method to repeatedly mark the onset of such events in the EEG.

There are two technical aspects that determine the quality of ERP event markers:

• Delay: Time from when an event occurred to when it is marked in the EEG data
• Jitter: Consistency of the delay with which the event is marked in the EEG data

Long delays with large jitter complicate EEG analysis up to the point in which the targeted ERP component becomes unobtainable or requires too many trial repetitions to appear. Short delays with minimal jitter create the ideal technical conditions to obtain ERPs with a minimally possible amount of trial repetitions.

With Zeto’s event marker integration, users benefit from accurate event marker timing and distributed cloud data access and management – see Figure 1.

Figure 1. Schematic diagram of Zeto’s local event marker integration and remote data streaming and management features. Event markers from the presentation computer timing are merged with the EEG data locally via the Zeto Interface Box 2 (ZIB2) and then passed on to the Zeto Cloud.  A simultaneous LSL integration and related multi-model data recording become possible out of the box while maintaining Zeto’s existing cloud streaming, data, and user management features.

ZETO ERP FEATURES

The Zeto EEG platform offers the ability to integrate external markers wirelessly at an 8-bit resolution within a 2 ms delay and less than 1 ms technical jitter. In other words, the user can distinguish between 255 unique event markers that they can repeat as closely as 4 ms from one another. With these features, Zeto EEG is equipped to reveal accurate auditory, visual, and senso-motoric ERPs across a wide range of applications.

Event markers are collected by the system via an 8-bit DB9 connector built into the Zeto Interface Box Version 2 (ZIB2) using Transistor-Transistor-Logic (TTL) signals.⁶ The ZIB2 acts as a data access point for the wireless Zeto WR19 headset. Synchronization between the ZIB2 and Zeto WR19 is handled at a nano-second range, eliminating both the delay and jitter introduced by the wireless data transmission. Incoming event markers are retroactively aligned with the data point at the time of collection.

Users can extract the event marker data from the Zeto system in multiple ways:

1) EDF+ File
Zeto users can export finished EEG recordings in various ways but the most popular is the EDF+ file format that is readable by most common EEG analysis tools. Event markers appear in the EDF+ file as digital I/O channels synchronized with the EEG data. Some EDF readers will also display the embedded event markers in the viewer.

2) Visualization
In the Zeto cloud software, users can visualize the TTL event marker inputs along with the EEG by selecting the “ALL” montage in the montage menu. This view is particularly useful for troubleshooting when setting up the ERP experiment. Offline or in real-time the user will see incoming event marker codes visualized high or low values in separate channels. The event marker channels are simultaneously translated into event marker labels that co-appear at the bottom of the screen (Figure 2).

Figure 2. Close-Up of the “All” Display montage: Output (“A”) or Input (“B”) event marker channels for 8 bits each, translated into up to 8-bit (255) unique event marker labels. The event marker mapping can be freely configured and labeled as desired prior to the recording. In this example, eight input event marker signals are embedded in the data file (pins 1 to 8) and show up as square waves (“C”).  Each input event marker channel is mapped to an annotation, labeled “One” through “Eight” respectively at the bottom of the data screen.

3) Real-time lab streaming layer (LSL) Export
Eight digital input and output channels each are made available via lab streaming layer (LSL) API in real-time, enabling the user to note the stimulus onset directly in the data stream. Event makers and EEG are synchronized and merged prior to providing this data to the LSL streaming socket. As a result, event timing and EEG data remain perfectly synchronized even if there are LSL related streaming delays. Additional LSL API synchronization features remain available to users for additional real-time data integration.

4) Offline Event Marker Files
Users have the option to export marker files after the recording is completed. That marker file contains precise marker timing and label information for all event markers recorded for easy processing in third party analysis tools such as MATLAB, ERPLAB, Python or others. This allows for separate analysis of event data and EEG data found in the exported EDF+ file.

The event file can be exported in “.csv” format (Figure 3), or a comma-delimited format called “.zmrk”. Both are compatible with most common EEG processing tools currently available for research. 

Figure 3. Event Markers listed in .csv format.

ZETO EVENT MARKER TIMING VALIDATION

Zeto validated the event marker timing to establish the delay and jitter attributes under working conditions.
To do this, a testing setup split the incoming TTL trigger voltages via an analog splitter into two exact 8-channel copies. One copy of the event marker signals got connected to the ZIB2 input trigger ports while the second copy got connected to 8 channels of the WR19 headset. As a result, incoming event markers both appeared as digital events in the datastream and voltage changes in the EEG channels (Figure 4). Subsequent processing revealed the real-life delay and jitter between the incoming event marker signals and the EEG recording.

Figure 4. Schematic of the event marker timing test setup. The presentation computer sends 8-bit TTL event markers to an analog splitter box. One copy of the TTL signals arrives at the ZIB2 and gets converted into event labels. The other copy arrives at the headset and feeds into 8 of the EEG channels to show up as signals in the EEG data file.

Using this approach, event marker timing was established as stable, at < 2 ms delay and <1 ms jitter, which is a good basis to reliably capture ERP signals in EEG.

ZETO’S STIMULATION AND SYNCHRONIZATION PLATFORM PARTNERS

It is important to note that the Zeto system provides extremely precise synchronization on the receiving end of the event marker only. In fact, a much more likely source of both delay and jitter in ERP experiments occurs during stimulus presentation and subsequent event marker generation.

To avoid timing complications that occur prior to event markers entering the Zeto system, Zeto has partnered with two stimulus and synchronization platforms – both tested with our products.  These stimulus presentation and synchronization products are designed to eliminate delay and jitter on the event marker onset. In addition, they offer a variety of additional functions, including experiment writing and presentation software, participant response boxes, and photodiodes. 

Both partners have implemented out-of-the-box integrations for Zeto and are ready to service Zeto customers.

Cedrus, Inc.	Psychology Software Tools
Cedrus devices are designed for precise, jitter-free event marking and fit a variety of budgets. SuperLab is an experiment writing application, while software support for their hardware interfaces also includes Matlab, E-Prime, Python, C++, etc.	Psychology Software Tools isa prominent software and hardware company that helps researchers address challenges in human behavioral studies. PST are creators of E-Prime, a market-leading experiment writing platform.
C-POD Sent precise event markers via USB	Chronos Hardware synchronization and delivery system for millisecond-accurate event markers to external devices using various I/O port options.
M-POD Sent precise event markers, plus incorporate a response pad and photodiode	E-Prime Experiment writing platform that integrates stimulus presentation and behavioral software with research equipment
StimTracker Duo Comprehensive audio/visual/response synchronization platform
SuperLab Easy to use experiment writing software that does not require programming

References

1. Hai Li et al. (2018). “Combining Movement-Related Cortical Potentials and Event-Related Desynchronization to Study Movement Preparation and Execution.” Frontiers in Neurology.
   https://www.frontiersin.org/articles/10.3389/fneur.2018.00822/full
2. Fedor Jagla, Vladislav Zikmund, in Studies in Visual Information Processing (1994). “Visual and Oculomotor Functions.” ScienceDirect.
   https://www.sciencedirect.com/topics/neuroscience/movement-related-potential
3. Sean McWeeny, Elizabeth S. Norton. “Understanding Event-Related Potentials (ERPs) in Clinical and Basic Language and Communication Disorders Research: A Tutorial.” PMC.
   https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3016705/
4. Shravani Sur, V. K. Sinha (2009). “Event-related potential: An overview.” PMC.
   https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3016705/
5. Thomas Hörberg et al. (2020). “Olfactory Influences on Visual Categorization: Behavioral and ERP Evidence.” Cerebral Cortex.
   https://academic.oup.com/cercor/article/30/7/4220/5811850
6. Fiorenzo Artoni et al. (2017). “Effective Synchronization of EEG and EMG for Mobile Brain/Body Imaging in Clinical Settings.” PMC.
   https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5770891/

As a healthcare professional, you know how important it is to have reliable equipment that can withstand the daily grind of a busy medical practice. That’s why we designed Zeto EEG – a rugged, clinical-grade headset that is built to last.

Zeto’s durable EEG headset is made from high-quality, clinical-grade materials that are easy to clean and maintain. It has a light but substantial feel, making it comfortable to wear for extended periods of time. But don’t let its light weight fool you – Zeto’s resistant EEG headset is tough enough to handle collecting EEG data in even the most demanding clinical environments.

To back up our commitment to quality, we offer up to 4 years hardware warranty that covers intended use. Customers have the choice to purchase this warranty at once upfront or extend it annually. This means that you can use Zeto with confidence, knowing that it is built to last. Excluded from that warranty is unintended use such as submerging or washing, sitting on, tearing, or intentionally over-bending the headset.

Our standard Service Level Agreements (SLAs) provide 72 hours replacement assurance. For those who need even faster replacement, our premium SLAs assure that a replacement headset can be with you within 24 hours during weekdays as long as we receive your request by 2 p.m. (ET).

In conclusion, if you’re looking for a quick-to-apply dry EEG system that is built to withstand the rigors of daily clinical use, look no further than Zeto.

Curious about how well our Zeto’s reliable EEG headset can withstand the daily wear and tear of a clinical setting? We put our product to the test with a drop test, simulating the accidental drops and impacts that can occur during everyday use. Watch the video to see just how rugged and durable Zeto truly is:

Sweat artifacts are a common problem in electroencephalography (EEG) recordings. They can noticeably affect the quality of the recorded tracings and make it difficult to read the underlying EEG signals. Sweat artifacts in EEGs occur when the body’s biological sweat response alters the conductivity of the skin in a way that affects the electric signals picked up by the electrodes. Such changes not only occur when sweat is visible on the scalp but also occur when the body heats up and prepares to sweat.

In this blog, we will explore the causes of EEG sweat artifacts, their effects on EEG recordings, and strategies for mitigating their impact.

What Causes Sweating

Sweat is crucial for human thermoregulation and can be caused by a variety of factors, including anxiety, nervousness, or physical exertion. Biological changes during menopause also increase the chance of sweating. Regardless of these and other factors, a warm testing environment is the main driver of sweating.¹ Systematic studies revealed that temperatures above 79°F (~26°C) can have a noticeable effect on the EEG and signal morphology.²

The Biology of Sweat Artifacts in EEG

Sweating is not simply the appearance of sweat on the skin but the result of a cascade of biological changes that lead to the skin’s ability to secrete liquid from the sweat glands, onto the skin (Figure 1).

The filling of the sweat glands with liquid in preparation of sweat excretion increases the electrical conductivity of the skin rapidly which affects the morphology of the EEG signals. These rapid changes in skin conductivity and the uneven distribution of the sweat glands across the skin result in recordings prone to major EEG artifacts, with single channels showing large signal changes at different times and locations.³

Figure 1. Cross section of epidermis and dermis skin layers with embedded hair follicle, eccrine, and apocrine sweat glands. Source: Mayo Clinic

The Physics of Sweat Potentials in EEG

In addition to biological changes in the skin’s conductivity, the composition of the sweat itself is contributing to electrical potentials that EEG amplifiers pick up. Sweat contains high sodium chloride and lactic acid which react with metallic components of the EEG electrodes, generating electrical potentials.⁴ These electrical potentials combine with skin and sweat gland potentials into what is visible in the EEG as sweat artifacts.

Appearance of Sweat Artifacts in EEG

Sweat artifacts in EEG can appear in various morphologies or shapes that are affected by biological factors such as the severity and generality of the sweat response. The sudden onset of sweating across the entire body will appear different from sweating that occurs over time or may be more limited by body part or region. More relevant for the appearance in EEG though, are the analog or digital filter settings of the recording.

Amplifiers with a built-in low-frequency hardware filter will show a more subdued sweat artifact even without any digital filtering. True direct current (DC) amplifiers that do not have any analog low-cut-off filter will show the build-up to a sweat artifact in their raw data much more because small changes over time can be picked up much better.

Most clinical EEGs are viewed at a 1 Hz–70Hz bandpass filter as recommended by ACNS.⁵ EEG Sweat artifacts viewed using a 1 Hz low-cut-off filter generally show up as slow wave components around a 1 Hz–3 Hz frequency in otherwise normal background activity; for an example, see Figure 2. Disabling the low-cut-off filters, however, exposes additional low-frequency drifts related to sweat that are otherwise masked by digital signal processing; for an example, see Figure 3.

Figure 2. Filtered sweat artifact in a full 19-channel clinical EEG viewed in a referential montage. 1 Hz low-frequency forward Butterworth filter applied. The slow meandering signal drifts almost completely disappears after filtering (red frame).

Sharper signal drifts remain visible even after filtering (blue frame). For most clinical recordings, EEG tracings such as this are indicators of the biological changes that are caused by a sweat response. Data was recorded using Zeto’s WR19 headset at 79°F (~26°C).

Figure 3. Unfiltered sweat artifact during the same data segment, as presented in Figure 2. Slow meandering (red frame) and at times sharper signal drifts (blue frame) reflect the biological changes in the skin’s conductivity due to sweating.

How to Get Rid of Sweat Artifacts in EEG

There are two common ways to reduce or avoid sweat artifacts in EEG recordings.

1. EEG operators can reduce the biologically triggered changes that lead to sweating. In preparation for the EEG recording, operators can ask patients to avoid strenuous exercise, caffeine, and alcohol prior to a scheduled EEG session, ideally 24 hours before the test. During EEG recordings, Kappenman and Luck recommend maintaining a cool temperature in the recording environment to minimize the occurrence of EEG sweat artifacts. They recommend a comfortable temperature of 68°F –72°F (20°C –22°C) and using fans or air conditioning to prevent humidity buildup.²
2. During the EEG session, EEG operators should assure the best possible electrode contact with the scalp to reduce skin impedance under the electrode. In traditional amplifier systems with wired electrodes, this can be achieved via additional skin preparation and abrasion. With active quick-apply EEG recording systems, such as Zeto’s headset, operators can assure proper electrode landing with each conductive leg touching the scalp.

Bottom Line – Recommendations

In hectic clinical day-to-day EEG schedules, the easiest way to avoid sweat artifacts in most patients is to avoid sweating in the first place. For that reason, option #1, mentioned previously (reducing sweating), is the most robust way to assure consistent EEG data quality.

• Keep the EEG room temperature at 68°F – 72°F (20°C–22°C), especially when recording unconscious patients who cannot communicate their comfort levels; keeping an optimal temperature reduces the body’s need for sweating.
• Use fans or air conditioning to accommodate individual patient’s temperature requests. Each patient is different; ask to make sure they are not hot.
• If EEG sweat artifacts are detected, consider pausing the study to cool down the room (i.e., opening the door, reducing the room temperature, and/or the use of a fan).
• Relaxation techniques: Encouraging the patient to relax and breathe deeply. This can help to reduce sweat caused by anxiety or nervousness.

By implementing these strategies, EEG operators can help minimize sweat artifacts in EEGs and obtain cleaner results. It is important to work closely with the patient and monitor the EEG tracings for any signs of EEG sweat artifacts during the test to more adequately address issues with data quality.

References

1. Baker, L. B. (2019). Physiology of sweat gland function: The roles of sweating and sweat composition in human health.
   https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6773238/


2. Kappenman, E. S., & Luck S. J. (2010). The effects of electrode impedance on data quality and statistical significance in ERP recordings.
   https://static1.squarespace.com/static/5abefa62d274cb16de90e935/t/5ac6962a8a922d0b8b8be6a1/1522964012664/Kappenman+2010+Psychophys+Impedance.pdf


3. Kalevo, L., Miettinen, T., Leino, A., Kainulainen, S., Korkalainen, H., Myllymaa, K., … & Myllymaa, S. (2020). Effect of sweating on electrode-skin contact impedances and artifacts in EEG recordings with various screen-printed Ag/Agcl electrodes.
   https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9017959


4. Siddiqui, F., Osuna, E., Walters, A., Chokroverty, S. (2006). Sweat artifact and respiratory artifact occurring simultaneously in polysomnogram.
   https://pubmed.ncbi.nlm.nih.gov/16461004/


5. ACNS – American Clinical Neurophysiology Society Guidelines and Consensus Statements.
   Guidelines and Consensus Statements | ACNS – American Clinical Neurophysiology Society