Out Of The Storm

“And once the storm is over, you won’t remember how you made it through, how you managed to survive. You won’t even be sure, whether the storm is really over. But one thing is certain. When you come out of the storm, you won’t be the same person who walked in. That’s what this storm’s all about.” ~Haruki Murakami

We never saw the storm coming. Before we knew what was happening, we were surrounded by it. The pounding rain and furious wind disoriented us and knocked us from the path that we were on. And the lightning. The lightning shot through my son’s brain and contorted his tiny body. With relentless force, it changed our lives forever.

When the storm first hit, it scattered us. It pulled us away from each other and left us feeling lost and alone. I was angry at the storm. Angry for trying to take my son. Angry for trying to take my family. Angry for making me feel helpless. I shouted at it. I kept shouting, but it didn’t relent. Even after I lost my voice, I kept shouting until I realized that shouting wasn’t going to help me find my family. So I stopped shouting and began my search.

It took awhile for me to catch my bearings. The storm forced me to shed some of the baggage I was carrying to make progress and move forward. My wife was on a similar path, and she had started moving forward, too. Eventually, we found each other through the endless rain. We found our son, too. His frail body was exposed to the storm more than ours and we weren’t sure if he would recover. So we took turns covering him until he was finally able to move. Tired, battered, but together, we set off as a family to find our way through the storm.

It was years before we could see even a few feet ahead of us. Years where our hands would slip for each other’s grasp but we managed to reach for each other before we slipped too far apart. Some days we would take turns carrying our son or carrying each other. We kept moving, but it felt like we were going in circles. The storm would seem to let up only to return in force with another step. We’d tread over the same ground, seeing the footsteps we’d left pooled up with water.

After years of wandering, we stopped walking. If we weren’t going to make it out of the storm, we knew we needed shelter. At first, it wasn’t much. The wind would easily push over our weak walls, forcing us to rebuild. But we learned and built stronger walls. When the weight of the rain was too much and collapsed the roof, we rebuilt it, too, stronger than it was before. We found other people who were in the same storm, and we helped each other. And there were people living outside the storm who would send in their support, too.

Today, we find ourselves both out of the storm but still in it. We can see it through the window, threatening to take down our shelter if we let our guard down. So we continue to reinforce the walls we used to build it. We’re doing it as a family, closer than ever before because of the journey we are on together. None of us are the same people that we were when we walked in. We are changed. Tighter. Stronger.

The storm isn’t over and it won’t give up. And neither will we.

Paying The Toll

We were coming off a good weekend. We celebrated my wife’s birthday on Saturday, and we ended Memorial Day visiting friends, having a swim lesson, and staying up a little later to see part of the first game of the hockey finals. We put my son to bed tired but happy.

Just after midnight, the first seizure came. I heard the sound come from my son’s room a second or two before the sound came through the speaker of his monitor. By the time I got to him, it had passed. He was sitting up in his bed disoriented, so I helped him lay back down and waited for him to fall back to sleep.

The next seizure came a few hours later. The next one an hour after that. And the next one an hour after that. It was like aftershocks after an earthquake, except each of them was just as intense as the one before it. He had at least four that I saw, but we learned during the overnight EEGs that we don’t see them all.

When he does anything that exerts an effort mentally or physically, a nap-time seizure or a collection of seizures during the night is likely to follow. We bowled for an hour and he had a seizure during his nap. After a morning baseball game, a seizure. Even though he only goes to school for a few hours, he’ll often have a seizure during his nap.

We tried to explain it to his school. It’s not just about what he can handle in the moment. The exertion carries beyond the activity itself. It show’s up as more seizures, which set him up to be more tired the next day. That lowers his seizure threshold for the next day, too, making him more likely to have seizures or requiring him to spend more energy regulating his emotions or attention. It’s downward spiral that ends with the husk of a boy too tired to function.

It feels like the universe collects a toll from my son based on how much he gets to actually live his life. It imposes a penalty to knock him back down and remind him of his limitations when he tries to exceed them. Someone with uncontrolled seizures shouldn’t play baseball. Seizures. Someone with uncontrolled seizures shouldn’t be progressing in school. Seizures. Someone with uncontrolled seizures shouldn’t be going to the skate park, or an amusement park, or a hockey game. Seizures.

Every time it happens, I question whether we did too much. But I gave up wondering if we should be doing anything at all, because that’s having no life. That’s letting epilepsy win. That’s not giving my son the life and the world that he deserves. So we’re careful and we’re calculated in deciding what to do and how much to do. We do our best to protect our son but let him be part of the world. We introduce as much downtime as possible so that we can distrupt his pattern of exhaustion and let him do the things he loves.

The universe seems committed to collecting its toll, but we’re doing everything we can to minimize how much my son has to pay. Because we’re going to keep on living.

Seizure Detection And Prediction

This post is part of the Epilepsy Blog Relay™ which will run from March 1 through March 31, 2018. Follow along and add comments to posts that inspire you!

As the parent of a child with epilepsy, I rarely sleep through the night. Instead, I periodically wake to check in on my son. We use a wireless camera that has an app that we run on an iPad that I prop up beside our bed. I can see in to his room, even at night, and hear any activity or seizures. For the most part, it’s a good setup. But occasionally a wireless issue will cause the connection to drop. I’ll wake up facing a dark screen, wondering if I missed a seizure as I fumble in the dark to restart the app.

That scenario repeats a few times a month, which is why the news that the FDA approved the Empatica Embrace as a medical device was so exciting. The Embrace is a wearable device that detects generalized tonic-clonic seizures and sends an alert to caregivers. Devices like the Embrace will provide a piece of mind to many people with seizures and those that care for them.

Unfortunately for us, we haven’t yet found a device that can reliably detect my son’s seizures. His seizures are short and without much movement, making them harder to detect. Generally, the longer a seizure is and the more activity it generates, the more likely it will be detected. But with new sensors and smarter algorithms, these devices will continue to improve. They’ll have a higher sensitivity to detect shorter and more subtle seizures. Instead of relying on my own eyes and ears to catch every seizure, I’m hopeful that these devices will work for my son someday, too.

Since the theme this week is technology and epilepsy, I thought I would spend some time talking about the magic behind these devices.

Detection versus Prediction

Detection

First, I wanted to differentiate between detection and prediction. Devices like the Embrace focus on seizure detection. Detection figures out when a seizure is happening. The device monitors activity from embedded sensors and runs it through an algorithm. The algorithm has been trained to look for patterns that look like seizure activity. Once it is confident enough that a seizure is occurring, it will send out an alert.

Prediction

Seizure prediction tries to figure out when a seizure is likely to happen. Some people have auras or other cues that let them know that a seizure is coming. Imagine a device that could provide that same warning to everyone. This is a hard but achievable goal. The clues may be more subtle and harder to see. We may need more data or new sensors, but we’re well on our way to developing them. When we figure it out, the warning it provides cold allow a person about to have a seizure to go sit down or get to a safe area. It could alert caregivers ahead of time so that they provide help before or during the seizure.

Training an Algorithm

Both seizure detection and seizure prediction use much of the same data but for different goals. The techniques used to learn the algorithm are similar, too. Data is collected from a group of people wearing different sensors. The data includes both seizure and non-seizure activity and it’s fed in to a computer with a label such as “seizure” or ”no seizure.” The computer learns the difference between the two and creates a model that can be used to look at new data to classify it as a “seizure” or ”not a seizure.” The more examples the algorithm sees, the better it gets at identifying the common traits in the data that are associated with a seizure.

The process is similar to teaching an algorithm to identify a cat. You feed the system a bunch of examples of cats and it identifies that a cat has two eyes, to ears, a nose, and whiskers. It generalizes traits using a technique called induction. Once it generalizes the traits, it can use them to identify a cat that it has never seen before using those traits. This is called deduction.

The same approach happens with seizures. People and seizures are different. If we trained a model to look for a specific heart rate, it wouldn’t be useful because that would differ for everyone. Instead, we train a model to associate common changes that happen during a seizure. Then, when it sees the data coming in from sensors in a device, it looks for those similar markers to decide how to classify the data.

No Algorithm Is Perfect

As in the cat example, there are an infinite number of combinations of data points necessary to always get it right. We can’t practically train a model by showing it every angle of every cat that might exist. And we can’t give it data reflecting every possible seizure for every person. But we don’t have to. The magic of these algorithms is that they can do a pretty good job using subsets of the data. But that does mean they can make mistakes.

There are two types of mistakes that are the most common: false positive and false negative. In the case of seizure detection, a false positive is when the algorithm said there was a seizure but there wasn’t. A false negative would be when the algorithm didn’t think there was a seizure but there was.

These two error types present different challenges. In seizure detection, a false positive means that a caregiver might have been alerted. This can be annoying, especially if it happens too much, like The Boy Who Cried Wolf. Too many false positives means people may turn off the notification feature or stop wearing the device altogether.

In seizure detection, the false negative is a much more severe problem because it means a seizure occured but the algorithm missed it. That means no notification was sent to alert a caregiver. If that is the primary purpose for the device then it can’t be relied on and won’t be used.

Making Things Better

The good news is that algorithms can learn from their mistakes and get better. We can use the times it was right and wrong to retrain the algorithm so that it can get better. That’s what Google, Facebook, and every other company that uses data does to make their products better. A popular concept in the world of machine learning and AI products is the Virtuous Circle of AI.

We create products and give them to customers. The customers use the product and generate more data. The data is used to make the product better by making the algorithms better or adding new features. This is how Alexa gets better at understanding what you’re asking for, how Google gives you better search results, and how music and movie recommendations today are many times more accurate than even a few years ago. In the same way, as more devices like the Embrace find their way on to the market and more people use them, these products will use the data to get better, too.

NEXT UP: Be sure to check out the next post tomorrow by Joe Stevenson at epilepticman.com for more on epilepsy awareness. For the full schedule of bloggers visit livingwellwithepilepsy.com.

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