Clinical Potential of the Zeto One Point-of-Care EEG System

As neurologic care continues to evolve toward faster, more decentralized models, point-of-care electroencephalography (EEG) technologies are increasingly being explored as tools to improve access to seizure evaluation and acute neurologic assessment. Traditional EEG systems often require trained technologists, specialized infrastructure, and patient transfer to tertiary centers, creating barriers for smaller community hospitals and rural care settings.

Recently, Allina Health’s United Hospital – Hastings Regina Campus in Minnesota implemented the Zeto One headset, an AI-enabled, point-of-care EEG platform designed to facilitate rapid neurologic assessment closer to home. The wearable system includes a full array of EEG electrodes, integrated video monitoring, and built-in AI-supported seizure burden detection, allowing clinicians to quickly evaluate patients for seizure activity and potential status epilepticus without requiring immediate transfer to larger metropolitan hospitals.

In a recent conversation with NeurologyLive^®, Armin Jewell, DO, neurologist and epilepsy fellowship-trained physician at Allina Health Brain and Spine Institute, discussed the design and functionality of the Zeto One system, its implementation into clinical workflows, and how AI-assisted EEG technologies may continue to shape the future of epilepsy and neurocritical care.

NeurologyLive: Can you provide some background on the Zeto One headset and how the technology functions in clinical practice?

Armin Jewell, DO: For background, it is a type of EEG software and technology. Obviously, EEG technology has been around for many decades now, and traditionally EEGs are placed by EEG technologists who come in and put electrodes on the scalp and things like that. But then there’s been this new wave of devices, including the Zeto, that are called point-of-care EEGs, meaning they can basically be applied without a technologist.

There are different versions of point-of-care EEG systems, and Zeto One is one of them. The idea is that it’s a standalone device that can be sent out to different hospitals, especially outreach hospitals that don’t have dedicated EEG staff. That way, they can implement EEG testing locally without needing all the traditional resources.

The headset itself fits very easily over the head and has all the electrode labels already built into it. The electrodes attach magnetically, and unlike traditional EEG systems, the electrodes already contain conductive gel inside them. You don’t need to manually apply additional gel, which makes it much less cumbersome for people putting it on.

Once connected, the device hooks up to a bedside monitor that displays both the EEG tracing and integrated video monitoring. One of the things that differentiates Zeto from some other point-of-care EEG systems is that it has a full array of EEG electrodes as well as video capability. That helps bring it much closer to a traditional EEG setup compared with some of the more limited systems.

What are some of the major workflow or implementation considerations for institutions interested in adopting this type of technology?

When everything is established within a hospital system, it really becomes just another clinical tool. It’s often used in emergency departments, ICUs, or inpatient floors. A physician places the order for point-of-care EEG, and then a nurse or other trained staff member can apply the device to the patient.

Once the device is hooked up, the EEG data is uploaded to the cloud, where epileptologists like myself can review it remotely and provide feedback. So, it’s really a way to bring EEG capability directly to the patient without requiring transfer to another location that has traditional EEG resources.

A big part of the value is cost savings and patient convenience. It can prevent unnecessary transfers, allow patients to stay closer to home, and streamline care. From a workflow standpoint, it’s very similar to standard EEG ordering and interpretation — it’s just much quicker to deploy and implement.

Are there specific patient populations or clinical scenarios where point-of-care EEG may be particularly valuable?

Traditionally, EEG can be used for a lot of different reasons, but the strongest data for point-of-care EEG systems is around ruling out status epilepticus.

For example, you might have a patient who is unresponsive after a seizure or after another CNS injury, and the question becomes whether they are continuing to have underlying seizures that you’re missing. Historically, you either had to transfer the patient, wait for EEG access, or empirically treat them.

Now, with systems like this, you can often get the EEG on within 5 to 10 minutes and determine whether there is ongoing seizure activity. That’s really where the strongest evidence exists right now.

Because Zeto has the full electrode array and video capability, it can also expand beyond that use case somewhat. It can be used in post-cardiac arrest patients or individuals with fluctuating mental status, where longer monitoring and video review become helpful. The indications are expanding as the technology improves, but ruling out status epilepticus remains the primary use case supported by the strongest data.

How does this type of technology align with the broader movement toward AI integration and data-driven neurologic care?

It’s another way to get more objective information about patients, which is great because previously we either had to do more drastic measures to obtain that information or sometimes treat empirically without it.

Most of these EEG systems now have built-in AI components. Once the patient is hooked up, the software analyzes the EEG in real time and provides an estimate of seizure burden. It might indicate there’s a very high likelihood of seizures occurring and flag that information to bedside staff so they can respond quickly or contact the interpreting neurologist.

That AI integration is already becoming standard across EEG technology. Of course, there are still limitations. EEG interpretation can be affected by artifacts and false positives, and the systems are intentionally designed to over-alert rather than miss seizures.

While AI is very helpful, it still absolutely requires oversight from trained neurologists. Even if the algorithm flags status epilepticus, we still need to review the tracing and make the final interpretation because sometimes what looks like seizure activity is artifact. The AI is a tool that complements the neurologist rather than replacing them.

What lessons have you learned so far during the early implementation period with this headset?

This system is a little different from some other point-of-care EEG devices because of the full electrode array and video capability. Because of that, we’ve started shifting some of our use cases beyond just ruling out status epilepticus and more toward broader EEG applications.

There’s definitely a learning curve with any new technology. Staff need to become comfortable with applying the device, optimizing signal quality, using the video system, and integrating it into workflow. But overall, it’s been very promising.

The quality has been good, and it really does help expand EEG access. We also continue to face a nationwide shortage of EEG technologists, which is a well-recognized issue. Systems like this provide at least a partial solution to that shortage and can help expand access to neurologic evaluation in hospitals that otherwise might not have EEG capability readily available.

Over the past few months, we’ve already seen increasing utilization, and I think that will continue as clinicians become more familiar with the technology and workflows become more streamlined.