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]
Summary
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)
Published Version (Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.