Comment by tgokh | Hacker News Reader

Comment by tgokh | Hacker News Reader

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This is really cool! It sounds like you're trying to determine whether you can identify AF out of data that's complicated by poor recording quality and regular activity? Is your longer term goal/business model to use this as a diagnostic tool (i.e. replace a Holter monitor)?

And since you offered to answer questions, I have a somewhat niche one. I'm an MD/PhD student doing my research in computational electrophysiological modeling, and I'm getting interested in the idea of using machine learning in medicine, specifically in signals analysis (i.e. EKG), and in time-series analysis (like heart rate variability). I'm teaching myself machine learning via a fantastic UW Coursera series that covers the basics (regression, classification, clustering, etc) with good mathematical rigor, but I'm not really sure where to go from there. What techniques would you suggest I look at? Would learning something like TensorFlow (I know Google has put out a course on Udacity) be a good idea? Obviously at some point, I just need to find some small projects to jump in on, but I want to build a decent skillset first if I can. Any thoughts would be appreciated!

If we succeed in training an accurate enough machine learning algorithm, I think the primary purpose will be to screen for asymptomatic atrial fibrillation, and then use a Holter or Zio Patch for a final diagnosis.

The main benefit is that you can prevent cryptogenic strokes, which are often caused by undiagnosed atrial fibrillation: http://www.nejm.org/doi/full/10.1056/NEJMoa1311376

For machine learning, I think you're on exactly the right path. The Udacity TensorFlow course taught by the Google Brain team is good; if you can complete that, you'll be as good as anyone at applying neural networks. The Stanford courses (http://cs231n.stanford.edu/, http://cs224d.stanford.edu/) are likewise great, as is Chris Olah's blog for intuition (http://colah.github.io/).

And down the road, if you're ever looking for an internship, definitely let us know! brandon@cardiogr.am.

Having worked with some physicians on AF diagnostic instrumentation, I took away that an outsized difficulty in this diagnosis is patient compliance. One doc called it a "one shower" problem; very many patients would wear holters as prescribed until they needed to take it off, and then fail to don it again. Clearly this is an issue when hunting for an infrequent arrhythmia, and it seems that the comfort and ease of use of the device are inherent to the problem.

From what you're gathering, do you imagine an eventuality in which a medical device can take a form factor similar to these popular consumer choices and provide a signal of diagnostic quality? Or do you think PPG-style sensing is limited to a fundamental degree of uncertainty with respect to arrhythmias?

Yeah! Absolutely. In healthcare we call it "patient compliance," but it's really just usability.

My guess is that by the time we get to the Apple Watch or Android Wear 3 or 4, it'll include a built-in ECG sensor. There are some devices out there already (like Nymi) that have built ECG into a wristband -- if the user touches a wristband worn on one hand with a finger on the other hand, that completes a circuit through the heart and gives you a 1-lead ECG.

I think the downstream implication is that, just like smart phones replaced dedicated GPSes and music players, many medical use cases will become "apps" that run on general purpose wristbands. The big advantage is that since they'll be always-on and non-intrusive, we'll be able to catch conditions much earlier.

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