
New AI System Predicts Seizures - riffraff
https://spectrum.ieee.org/the-human-os/biomedical/diagnostics/this-new-ai-system-can-predict-seizures-with-nearperfect-accuracy
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sanchezdev
Time will tell, but as a machine learning engineer, when you see results this
good it's more probable that a mistake was made. They could be reporting the
training error on an overfit model or data leakage could be occuring due to an
improper train-test spilt of the data.

Also, it is definitely appropriate to use the term AI in this case. AI is not
a technical term so it's really in the eye of the beholder, but I think it's
safe to say that ML is a subset of AI. Perhaps people are conflating AI with
AGI?

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hwbehrens
It's a 14-person training set with an 8-person test set, so my guess is that
it can pretty accurately predict seizures _in the small group of people it is
trained on_. Whether the model could be kernelized for a useful general
deployment is unclear. It still requires many electrodes attached to the
scalp, so there is a still a ways to go before it can be integrated into a
watch, for example.

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gnarcoregrizz
I assume it’ll end up in an implant like RNS

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est31
Yeah watches can't even detect whether you are sleeping or not. The products
in the market are mainly accelerometer based and aren't really reliable if you
e.g. are awake but don't move.

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brenden2
The sample size is 22 patients.

Also, I've noticed the definition of the word "AI" has grown to encompass
pretty much any type of software that does something with data.

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etbebl
What, do you think that's too low? Sounds pretty normal for this type of
study.

Of course, that does mean the results don't necessarily generalize to the
whole population, which is why you do eventually need much larger sample sizes
when you're working toward FDA approval.

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brenden2
It seems a little premature to proclaim "Near-Perfect Accuracy" on such a
small sample size. It sounds to me like they may have overfit their model.

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siftrics
"A false positive rate of 0.004 per hour" is a sly way of putting "a false
positive once every 10 days."

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freeflight
That's 3 false positives in a month, depending on how many seizures a patient
suffers from, this might actually not that bad. Afaik a false positive doesn't
have any direct consequences except "be careful and keep medication ready"?

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matthewdgreen
How does this become a treatment, assuming the results actually hold up? Is it
possible to prevent seizures with a timely dose of a powerful epilepsy
medication? Are there portable EEG rigs that can produce sufficiently powerful
readings?

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tuukkah
At least it lets you get into a safe place and position. Perhaps you can even
get someone to watch over and be ready to call 911 if it comes to that.

Edit: Perhaps this hints at some medication that is not currently viable but
could be administered if the seisure was predicted: "Notably, seizures are
controllable with medication in up to 70 percent of these patients."

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yomly
Correct - a friend had a seizure on the stairs and was lucky to get away with
just a cut lip...

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peter303
Its interesting that service animals like dogs or monkeys can sometimes be
trained to anticipate seizures. And in many of these cases its not clear what
the signal is- a change in activity, odor etc.

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moioci
Work proceeds on natural intelligence system for predicting seizures:
[https://www.nature.com/articles/s41598-019-40721-4](https://www.nature.com/articles/s41598-019-40721-4)

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black_puppydog
Actual paper, linked from the article:

[https://ieeexplore.ieee.org/document/8765420](https://ieeexplore.ieee.org/document/8765420)

"Efficient Epileptic Seizure Prediction Based on Deep Learning"

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leoh
Some questions not addressed by the article:

* what is the input to the model? It sounds like it's EEG, but how practical is it to collect those data on a day-to-day basis?

* how far ahead of time are seizures predicted?

In other words -- how practical is this research?

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tuukkah
"predict the occurrence of seizures up to one hour before onset"

"We are currently working on the design of an efficient hardware [device] that
deploys this algorithm, considering many issues like system size, power
consumption, and latency to be suitable for practical application in a
comfortable way to the patient"

