Trends In Wearables For Seizure Detection And Prediction

This post is part of the Epilepsy Blog Relay™ which will run from June 1 through June 30. Follow along and add comments to posts that inspire you!

Today kicks off week 3 of the Epilepsy Blog Relay when the theme changes to Tech and Innovation in Epilepsy. As a technologist and father of a child with epilepsy, this week represents the intersection of my two worlds. I am excited to be writing this week because of the promise of technology in managing epilepsy.

The Story So Far…

More than a year ago, I found a crowdfunding campaign for a wearable device that could detect seizures. At the time, we were struggling with detecting and recording my son’s seizures. It was difficult because he had many types of seizures and we knew from EEGs that we weren’t catching every one. The seizure devices already on the market didn’t work for him. Most used accelerometers and gyroscopes to capture exaggerated arm movements or falls. But his seizures often only created subtle body movements that were not detected. This new device included other seizure markers, such as galvanic skin response. I hoped the new sensors would make the difference. Since the device showed promise, I backed it and then anxiously awaited its release.

After a long delay, the device finally shipped. When we received it, I strapped it to my son’s wrist and hoped. The next night, my son had his usual handful of seizures but the device didn’t detect any of them. Initially, I thought I had configured the device wrong or that it lost connectivity to my phone. But after a few weeks of seizures with no detection, we stopped wearing the device and put it on the shelf.

Our story is one of many similar stories of unrealized expectations. But this post is not one of failure and despair but one of hope. While the device didn’t work for us, it does work for some people. Moreover, better methods of seizure detection continue to be developed. These techniques are being included in the growing number of wearable devices on the market. Soon, we’ll have clothing and accessories capturing biometric markers that will be able to detect seizures more reliably. We’ll have data captured that we’ll be able to use to predict when a seizure will occur. And this will happen in the very near future.

Devices, Data, and Machine Learning

There are three components necessary to create a device capable of detecting and predicting seizures: devices, data, and machine learning.


The devices represent the things that are collecting data. Today, we have wearables like watches and clothing that have sensors in them. These sensors measure some attribute such as heart rate, steps, or stress level. The trend towards smaller, cheaper, and more energy-efficient sensors will continue. New sensors to measure new markers will be created. Manufacturers will be putting sensors in nearly everything they create. The result will be a wealth of information streaming from us at all times.


With the proliferation of sensors, the result will be a tsunami of data. Every measurement and data point we can collect will be available in near-real time. We’ll have access to data that required equipment at a hospital to measure. We’ll also be able to correlate that data with information from the world around us. The outside temperature, what our thermostat is set to, what we ate, how much television we watched. The more things we connect and make available, the larger the pool of data we will have with which to swim in and find answers.

Machine Learning

The component that I am most excited about is machine learning. Now that we have all of this data, what do we do with it? It’s too much data coming in too fast for a human to make sense out of. So we use machine learning to try to make sense out of it for us. We can train a system using real data so that, over time, it can use what it learned to predict better than a human can. It can find patterns in data that are invisible to us and make connections that we can’t. It can figure out when the data is aligning in a way that previously resulted in a seizure and notify us. It can help adjust our behavior in a way that reduces our risk of a seizure. And it will never stop learning and will continue to make more accurate predictions.

epilepsy dad seizure data machine learning sensors devices

As depicted in the image about, machine learning isn’t the final stop. Instead, we will use the algorithms we develop to feed back into the devices. We’ll create new sensors to fill in our gaps in data. We’ll push the intelligence further down to the device to allow it to make smarter decisions closer to the person wearing it. The updates to the devices will result in more data, or better, more refined and reliable data. That, in turn, will make our predictions better. The cycle will continue to a point where many devices will be able to detect and predict seizures. It won’t be the job of one specialized device but, instead, a collaboration of things we wear everyday.


There are challenges ahead. Securing the data and privacy are two top concerns. Battery life and powering the devices and sensors follow closely behind. These are huge problems and concerns. But just as there are trends impacting the components above, these challenges are not unique to wearables. Advancements in encryption and identify management will make their way into wearables. New battery and wireless charging technologies will keep our devices powered longer and without us thinking about it. A rising tide lifts all boats, and wearables will benefit from much of the same innovations as other technologies.


Even though our current experience with wearable devices to predict seizures has been disappointing, I am still optimistic. The trends in devices, data, and machine learning will continue to result in more reliable detection and prediction of seizures. In the near future, we’ll have these capabilities in everyday wearables, not just in specialized devices. The result will be a dramatic increase in peace of mind and in overall quality of life.

NEXT UP: Be sure to check out the next post tomorrow by Leila Zorzie at for more on epilepsy awareness. For the full schedule of bloggers visit

Don’t miss your chance to connect with bloggers on the #LivingWellChat on June 30 at 7PM ET.

The Lonely Record – A Story Of Data In Epilepsy Diagnosis

Somewhere in a cluster of servers in windowless rooms spread around the world, there is a Great Machine. On that machine, there lives a database. In that database, there is a table, and in that table, there is a record about my son. The 1s and 0s in that record contain an anonymous listing of a six-year-old boy. Those bits and bytes also contain data of expressed genes captured during an exome sequencing.

In the same database, there are records of thousands of other children that have had their exome sequenced, too. Each individual record has at least one common attribute with all the other records…the child that the record represents has epilepsy.

The goal of the exome sequencing is to identify one of the known genetic variations that is known to cause seizures. If that happens, patients can benefit from those that came before. A potential treatment plan identified by other patients with the same genetic condition might provide a more targeted approach for the newly matched patient. It might also offer some insight in to prognosis. For better or worse, it may at least give some answers.

For the new records that are placed in to the database that are not matched to a known genetic cause, they sit unattached and alone. These records lack causation and, in their unjoined state, they also lack correlation. There are no patients that came before, no best practices or lessons learned. There is no prognosis. There are no answers.

My son’s record is that way. It rests alone in a database, somewhere in the world, on a cold, metal server in a windowless room, waiting to be called. Waiting to be joined with another record. Waiting for correlation. Because with correlation, there can be association. With association, there can be coordination. With coordination, there can be answers.

Every six months, the Great Machine tries to use new discoveries, new cases, to find patterns that were previously unseen or unknowable. It calls out to each record, looking to find those that it may bestow the gift of correlation. For most, there is no correlation, no gift, and they return to their lonely state until they are once again called upon.

Maybe someday, my son’s record will find a partner. A commonality between another record in a database. A non-uniqueness in the universe. In all likelihood, the result of a match will not produce a cause for the seizures. It may, however, start to provide correlation between cases that can be studied further.

To a lonely record, it’s at least something.