
Cardiologist-Level Arrhythmia Detection Using a Deep Neural Network - ArtWomb
https://www.nature.com/articles/s41591-018-0268-3
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carbocation
The 12-lead ECG is the standard for cardiac diagnosis in a clinic or an
emergency department. This uses numerous points of contact on the skin. Each
lead (whether directly measured, or virtually measured as a function of the
directly measured leads) then is plotted in a standardized way. The X-axis
represents time, the Y-axis represents voltage. When the voltage is toward the
lead, the value is positive; it can of course be neutral, or pointed away from
the lead (negative).

In contrast, when someone needs to be monitored while at home, a several-lead
device is cumbersome. So, people have devised single-lead devices that can
remain attached for days-to-weeks (e.g., Zio patch), or which can be activated
when desired (e.g., Apple Watch, AliveCor).

A single lead ECG can be more difficult to read, in part because you simply
get much less information. (Though, generally, what you're looking for
[arrhythmia] is a subset of what you look for in a 12-lead ECG.)

Obviously, there are automated algorithms that attempt to read these already.
However, deep learning approaches are appealing here, and at a glance this
looks like it performed well and is using a reasonable set of comparisons.
(Will have to read the methods section to know for sure; heading to bed so
won't get a chance to look for awhile.)

~~~
bobowzki
The "classical" I,II,III leads are balanced. aVR,aVL,aVF are derived from
I,II,III (through voltage dividers). V1-6 are singled ended with a common
"ground" lead.

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cliffdover
I always wondered how precise were the computerized ECG interpretations from
24-48h Holter results. Considering you have to take notes and report date and
time whenever you feel "something" it might need a manual check.

Edit: Code is open source and found it here
[https://github.com/awni/ecg](https://github.com/awni/ecg)

~~~
arkades
They’re pretty shit. Probably half of the essentially normal EKGs I see come
with the automated interpretation of “Abnormal EKG,” and no meaningful
information. You absolutely 100% cannot rely on them, and all doctors are
trained - from the get-go - not to.

~~~
jazoom
Yup. We pretty much just ignore what the machine says.

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signa11
link to the paper:
[https://arxiv.org/pdf/1707.01836.pdf](https://arxiv.org/pdf/1707.01836.pdf)

~~~
ivan_ah
Pretty impressive.

From the Figure in on page 2
[https://arxiv.org/pdf/1707.01836.pdf#page=2](https://arxiv.org/pdf/1707.01836.pdf#page=2)

> _Figure 2. Deep Neural Network architecture. Our deep neural network
> consisted of 33 convolutional layers followed by a linear output layer into
> a softmax. The network accepts raw ECG data as input (sampled at 200 Hz, or
> 200 samples per second), and outputs a prediction of one out of 12 possible
> rhythm classes every 256 input samples. The first and last layer are
> special-cased due to the pre-activation residual blocks._

^^^ 33 convolutional layers --- wow that's pretty deep for 1-D input.

~~~
killjoywashere
I find it interesting they don't cite any framework. They presumably wrote all
the DNN code from scratch, which can certainly be done, but seems like a lot
of effort unless you plan to commercialize, which clearly they are.

~~~
ovi256
Their published code
([https://github.com/awni/ecg/tree/master/ecg](https://github.com/awni/ecg/tree/master/ecg))
uses Keras. It's a pity they didn't cite it though.

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mnemotronic
What is "single lead" device? Is that a single electrical contact patch? How
can that detect anything? I've used a single patch with 4 electrical contacts
for a 30 day Holter test, plus the standard 24 hour Holter monitor with a
bunch of patches and wires.

~~~
diimdeep
What device have you used for 30 day test?

~~~
Tharkun
There are various options, but for long periods single lead devices are often
used, can't really walk around for 30 days with a 12 lead holter strapped to
you...would make bathing rather difficult.

It's possible to implant small loop recorders under the skin on the chest.
These can record for months. Those are mostly useful in cases where symptoms
occur infrequently.

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assblaster
For those who think this will revolutionize medicine, it won't. ECG analysis
is a tiny part of diagnostics, and can only give a very limited amount of
information for most people, and for the small number of people who absolutely
need ECG for diagnosis, a clinician is still required and the ECG is a part of
the information needed for determining treatment.

Further, the bar is set fairly low: as good as a panel of cardiologists at a
very specific task, that can also be done by non-cardiologists depending on
level of training and desire for mastery.

Lastly, this technology will never be a panacea where you walk into a Walmart
and the ECG analysis spits out a report of your overall "health".

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nicwilson
I would have thought that you could get pretty damn close with just some
simple stats in frequency space normalised to the base frequency, i.e. Fourier
transform divided by the fundamental frequency.

~~~
Tharkun
Not likely. Heart rhythm isn't so much about the rate at which the whole thing
beats, it's rather a little more complicated than that. It's about the
synchronization between different parts of the heart, about the way the signal
propagates from the Sinus Node, about how it relates to the body's requested
demand.

Many things can go wrong. Many things do go wrong even in healthy hearts, for
instance most people get ectopic beats every now and again, often without
their knowledge. A simplistic FT approach would highlight stuff like that and
no one would benefit from it.

~~~
nicwilson
Thats... not quite what I meant, perhaps I worded that a little bit weird. I
meant frequency invariant, not amplitude normalised. As in divide the
frequencies by the fundamental frequency (i.e. subtract their logs on the
'scope), not divide the amplitudes by the amplitude on the fundamental.

Any deviation from the profile of the FT irrespective of its base frequency,
from the normal sinus rhythm, would easily show. There might be a few problems
dealing with cardiac load transitions, and I suppose if you are trying to
differentiate the exact class of arrhythmia (as in the paper) then perhaps
not, but differentiating normal (sinus), noise, abnormal it should work fine.

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rodionos
The dataset is here
[https://physionet.org/physiobank/database/mitdb/](https://physionet.org/physiobank/database/mitdb/).
Only a handful of files...

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foxyv
For those of you concerned about heart disease, whether you have risk factors
or a family history, consider getting a calcium score. It's the gold standard
for detecting heart disease. It's usually not covered by insurance but it's
very inexpensive (About $200).

Involves a CAT scan of the chest so it's not a blood test and there are some
X-Rays involved. But it's totally worth it.

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KKKKkkkk1
The study contains no comparison with competing algorithms, so I don't see
what supports the claim that

 _If confirmed in clinical settings, this approach could reduce the rate of
misdiagnosed computerized ECG interpretations and improve the efficiency of
expert human ECG interpretation by accurately triaging or prioritizing the
most urgent conditions._

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agumonkey
sidenote: that there are 24 leads ecg

[https://www.ncbi.nlm.nih.gov/pubmed/18433615](https://www.ncbi.nlm.nih.gov/pubmed/18433615)

[https://www.ncbi.nlm.nih.gov/pubmed/24880763](https://www.ncbi.nlm.nih.gov/pubmed/24880763)

