Key result
A proposed 1-D convolution neural network architecture for multiclass ECG classification was superior to existing classifiers and competitive with state-of-the-art models in terms of accuracy.
Why the study?
Existing machine learning methods for ECG analysis require additional computation due to signal challenges, and prior work has focused only on binary classification rather than multiclass cardiac disease diagnosis.
Does a novel 1-D CNN architecture improve the accuracy of multiclass arrhythmia diagnosis from ECG signals compared to existing classifiers?
Does a novel 1-D CNN architecture improve the accuracy of multiclass arrhythmia diagnosis from ECG signals compared to existing classifiers?
A novel 1-D CNN architecture improves the automated multiclass classification of arrhythmias from ECG signals.
The electrocardiogram (ECG) is an important signal in the health informatics for the detection of cardiac abnormalities. There have been several researches on using machine learning techniques for analyzing ECG. However, they need additional computation owning to ECG signals challenges. We introduce a new architecture of 1-D convolution neural network (CNN) to diagnose arrhythmia diseases automatically. The proposed architecture consists of four convolution layers, three pooling layers, and three fully connected layers evaluated on the arrhythmia dataset. All previous researches are conducted to classify healthy people from people with Arrhythmia disease. In this paper, we propose to go further multiclass classification with two classes of cardiac diseases and one class of healthy people. The results are compared with common 1-D CNN and seven different classifiers. The experimental results demonstrate that the proposed architecture is superior to existing classifiers and also competitive with state of the art in terms of accuracy.
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Fasihi et al. (2020) studied Arrhythmia / Cardiovascular diseases. 1-D convolution neural network (CNN) architecture vs. Common 1-D CNN and seven different classifiers was evaluated on Multiclass classification accuracy. A proposed 1-D convolution neural network architecture for multiclass ECG classification was superior to existing classifiers and competitive with state-of-the-art models in terms of accuracy.
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