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I assume these models, and your end-product goal, is to make some kind of diagnosis, or flag anomalies? This is very interesting, and any more info on what you

I assume these models, and your end-product goal, is to make some kind of diagnosis, or flag anomalies? This is very interesting, and any more info on what your use-case or end-product would be great.

antognini 10y ago
The end goal is to make routine EEG interpretation easier for neurologists. The usual way to look at an EEG is to page through the record 10 seconds at a time, and look for different (sometime subtle) features to make a diagnosis. For a 24 hour record, that's 8600 pages the neurologist has to look through, the vast majority of which contain nothing interesting. One of the main features of our software is epileptogenic spike detection. Epileptogenic spikes are little blips in the record and are indicative that the patient has epilepsy. Our models are now good enough at finding these spikes as human neurologists. There may be only a handful of these spikes in the record, so finding them automatically can be a huge time savings for the neurologist. Our software also finds and eliminates various EEG artifacts. Blinking, for example, causes a huge artifact in the records. (It turns out that your eye is a dipole, and rolls upward every time you blink, which produces a huge signal.) The next major goal is seizure detection. It turns out there are a lot of things that can look a lot like a seizure in an EEG record. Even disconnecting the electrodes can look like a seizure. The holy grail would be to give the program an EEG record and have it spit out the neurologist's report. We're not there yet, but we're working towards it. (By the way, we're hiring another ML engineer and software engineer. Email jobs@persyst.com if you're interested!)
- kejaed 10y ago
  That sounds like a very rewarding application that will be a great net benefit for humanity. I'm looking at using machine learning on time series data in a similar way, replacing EEG with 3D motion data. However, ML is not my forte and I am just cutting my teeth. I would really appreciate if you be able to share some of the 'buzzwords' or ML topics that I might look into as I explore this work?
  - antognini 10y ago
    I have found recurrent NNs and their variants (LSTMs and GRUs) to be very useful for the time series I've been working with. It can also be useful to use 1-d convolutional NNs as well. Depending on your data, sometimes working in frequency space can be easier (i.e., train on the FFT of your data). I'm still a neophyte in this area (my background is in astronomy), so I'm learning, too!
    - kejaed 10y ago
      Many thanks.
- gricardo99 10y ago
  Thanks for the detailed response! It's remarkable to see yet another example of the impact of innovations like this.

antognini 10y ago

The end goal is to make routine EEG interpretation easier for neurologists. The usual way to look at an EEG is to page through the record 10 seconds at a time, and look for different (sometime subtle) features to make a diagnosis. For a 24 hour record, that's 8600 pages the neurologist has to look through, the vast majority of which contain nothing interesting. One of the main features of our software is epileptogenic spike detection. Epileptogenic spikes are little blips in the record and are indicative that the patient has epilepsy. Our models are now good enough at finding these spikes as human neurologists. There may be only a handful of these spikes in the record, so finding them automatically can be a huge time savings for the neurologist. Our software also finds and eliminates various EEG artifacts. Blinking, for example, causes a huge artifact in the records. (It turns out that your eye is a dipole, and rolls upward every time you blink, which produces a huge signal.) The next major goal is seizure detection. It turns out there are a lot of things that can look a lot like a seizure in an EEG record. Even disconnecting the electrodes can look like a seizure. The holy grail would be to give the program an EEG record and have it spit out the neurologist's report. We're not there yet, but we're working towards it. (By the way, we're hiring another ML engineer and software engineer. Email jobs@persyst.com if you're interested!)

kejaed 10y ago
That sounds like a very rewarding application that will be a great net benefit for humanity. I'm looking at using machine learning on time series data in a similar way, replacing EEG with 3D motion data. However, ML is not my forte and I am just cutting my teeth. I would really appreciate if you be able to share some of the 'buzzwords' or ML topics that I might look into as I explore this work?
- antognini 10y ago
  I have found recurrent NNs and their variants (LSTMs and GRUs) to be very useful for the time series I've been working with. It can also be useful to use 1-d convolutional NNs as well. Depending on your data, sometimes working in frequency space can be easier (i.e., train on the FFT of your data). I'm still a neophyte in this area (my background is in astronomy), so I'm learning, too!
  - kejaed 10y ago
    Many thanks.
gricardo99 10y ago
Thanks for the detailed response! It's remarkable to see yet another example of the impact of innovations like this.