Multi-scale features for heartbeat classification using directed acyclic graph CNN

Multi-scale features for heartbeat classification using directed acyclic graph CNN

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A new architecture of deep neural networks, directed acyclic graph convolutional neural networks (DAG-CNNs), is used to classify heartbeats from electrocardiogram (ECG) signals into different subject-based classes.DAG-CNNs not only fuse the feature extraction and classification stages of the ECG classification into a single automated learning procedure, but also utilized multi-scale features and perform score-level fusion Sneakers for Men - Grey - Canvas Mesh Athletic Running Shoes of multiple classifiers automatically.Therefore, DAG-CNN negates the necessity to extract hand-crafted features.In most of the current approaches, only the high level features color touch 7/97 which extracted by the last layer of CNN are used.Instead of performing feature level fusion manually and feeding the results into a classifier, the proposed multi-scale system can automatically learn different level of features, combine them and predict the output label.

The results over the MIT-BIH arrhythmia benchmarks database demonstrate that the proposed system achieves a superior classification performance compared to most of the state-of-the-art methods.