Open AccessAn Efficient Arrhythmia Classifier Using Convolutional Neural Network with Incremental Quantification
Author(s) -
Junguang Huang,
Zijing Liu,
Hao Lv
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1966/1/012022
Subject(s) - convolutional neural network , computer science , classifier (uml) , artificial intelligence , pattern recognition (psychology) , artificial neural network , deep learning , machine learning , data mining
Cardiovascular disease (CVD) is a dangerous disease, which can be effectively prevented by detecting arrhythmia early. In order to detect arrhythmia accurately, more and more researches use artificial intelligence methods to realize the classification and detection of electrocardiogram (ECG) signals. However, most of these designs are unfriendly to the hardware design due to too many parameters which lead to large calculation power and data accessing power. In this paper, we present an efficient arrhythmia classifier based on the convolutional neural network with the incremental quantification. This more efficient design can classify ECG signals accurately with lower capacity of parameters. The simulation results show that the recognition rate of the network with the incremental quantification has reached 92.76% with 39.34KB memory footprint, which is beneficial to hardware design and has better accuracy than other advanced quantification methods.