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. 2019 Sep 5;12(9):e005289. doi: 10.1161/CIRCOUTCOMES.118.005289

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© 2019 The Authors.

Circulation: Cardiovascular Quality and Outcomes is published on behalf of the American Heart Association, Inc., by Wolters Kluwer Health, Inc. This is an open access article under the terms of the Creative Commons Attribution Non-Commercial-NoDerivs License, which permits use, distribution, and reproduction in any medium, provided that the original work is properly cited, the use is noncommercial, and no modifications or adaptations are made.

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Figure 2. — ecgAI method of ECG segmentation. A, Architecture of convolutional neural network used for ECG segmentation. Gray rectangles represent layers with dimensions listed below. The notation for each layer indicates the size of the input (eg, 2000 ms, initially) by the number of leads (eg, 12) by the number of filters. The size of the filter is specified in the body of the rectangle. B, Architecture of hidden Markov model (HMM) used after convolutional neural network (CNN) based segmentation. Gray boxes represent states that are traversed in order in the ECG. C, Example of CNN-HMM output for an ECG. CNN-HMM based classes are shown below the image. The ST and T wave segments have been combined.