3 ms·
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 Neura
by ivan_ah 8y ago
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.
- MAXPOOL 8y agoIt's deep and large model. Maybe unnecessarily so. In image processing a single pixel or region almost always belongs to one object. Car can be behind a tree, or tree can be behind a car but they are not transparent and mixed together. In raw 1d radio, audio, ECG or EEG signals there can be multiple additively overlapping singnals across different frequency ranges. Signal patterns are transparent to other signals. ConvNet on raw singnal must learn to discriminate overlapping pattern combinations.
- killjoywashere 8y agoI 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 8y agoTheir 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.