Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks

Here is a very interesting paper that uses only one sensor:

"... ECG data is sampled at a frequency of 200 Hz and is collected from a single-lead, non- invasive and continuous monitoring device called the Zio Patch which has a wear period up to 14 days (Turakhia et al., 2013)."

What is interesting is that it can do very well compared to the gold standard established by the researchers in the paper. The second aspect that is fascinating to me is the need for 34 convolutional layers: an architecture that would have been difficult to guess in the first place. 

Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks by Pranav Rajpurkar, Awni Y. Hannun, Masoumeh Haghpanahi, Codie Bourn, Andrew Y. Ng

We develop an algorithm which exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single-lead wearable monitor. We build a dataset with more than 500 times the number of unique patients than previously studied corpora. On this dataset, we train a 34-layer convolutional neural network which maps a sequence of ECG samples to a sequence of rhythm classes. Committees of board-certified cardiologists annotate a gold standard test set on which we compare the performance of our model to that of 6 other individual cardiologists. We exceed the average cardiologist performance in both recall (sensitivity) and precision (positive predictive value).

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