Abstract

This paper puts forward a 1-D convolutional neural network (CNN) that exploits a novel analysis of the correlation between the two leads of the noisy electrocardiogram (ECG) to classify heartbeats. The proposed method is one-dimensional, enabling complex structures while maintaining a reasonable computational complexity. It is based on the combination of elementary handcrafted time domain features, frequency domain features through spectrograms and the use of autoregressive modeling. On the MIT-BIH database, a 95.52% overall accuracy is obtained by classifying 15 types, whereas a 95.70% overall accuracy is reached when classifying 7 types from the INCART database.

Highlights

  • Introduction and Related WorkCardiovascular diseases are the first cause of death in the world, with an estimated 17.9 million deaths each year

  • We propose the combination of hand-crafted features with a canonical correlation analysis network (CCANet) and SVMs for two-lead heartbeats classification

  • For the sake of comparison, we evaluate a suitable implemented 1-D convolutional neural network (CNN) solution based on residual networks (ResNet) [36]

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Summary

Introduction and Related Work

Cardiovascular diseases are the first cause of death in the world, with an estimated 17.9 million deaths each year. We propose the combination of hand-crafted features with a canonical correlation analysis network (CCANet) and SVMs for two-lead heartbeats classification. The analysis of the correlation between two leads of the ECG is exploited to increase heartbeat classification performance [20]. Our one-dimensional variant takes as input a combination of elementary hand crafted time domain features, frequency domain features through spectrograms, and the use of autoregressive modeling. We have designed a novel one-dimensional canonical correlation analysis network (1-D CCANet) to exploit two-lead ECGs for automatic classification of heartbeats that outperforms the state of the art;. We use the signals for which both II and V1 leads are available (see PhysioBank for further details)

INCART Database
Proposed Method
Autoregressive Modeling
Time-Domain Features
Neural Feature Extraction
First Convolutional Layer
First Extraction Stage
Second Convolution Layer and Extraction Stage
Final Output and PCA
Evaluation Metrics
Results and Discussion
Method
Conclusions

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