Key result
RA-CNN model classifies ECG arrhythmias with ~99% accuracy, outperforming traditional approaches.
Why the study?
Efficient and accurate automatic ECG arrhythmia classification remains challenging due to limited feature extraction and model generalization ability.
Does the RA-CNN deep learning architecture accurately classify interpatient ECG arrhythmias in Holter records?
Does the RA-CNN deep learning architecture accurately classify interpatient ECG arrhythmias in Holter records?
The proposed RA-CNN algorithm demonstrates high accuracy in automatic beat-by-beat ECG arrhythmia classification, highlighting the potential of advanced deep learning structures for automated diagnostics.
The precise identification of arrhythmia is critical in electrocardiogram (ECG) research. Many automatic classification methods have been suggested so far. However, efficient and accurate classification is still a challenge due to the limited feature extraction and model generalization ability. We integrate attention mechanism and residual skip connection into the U-Net (RA-UNET); besides, a skip connection between the RA-UNET and a residual block is executed as a residual attention convolutional neural network (RA-CNN) for accurate classification. The model was evaluated using the MIT-BIH arrhythmia database and achieved an accuracy of 98.5% and <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:msub> <a:mrow> <a:mi>F</a:mi> </a:mrow> <a:mrow> <a:mn>1</a:mn> </a:mrow> </a:msub> </a:math> scores for the classes <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mi>S</c:mi> </c:math> and <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" id="M3"> <e:mi>V</e:mi> </e:math> of 82.8% and 91.7%, respectively, which is far superior to other approaches.
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Xu et al. (2022) studied Arrhythmia (n=47). Residual Attention Convolutional Neural Network (RA-CNN) vs. Other traditional and deep learning classification methods was evaluated on Classification accuracy. The RA-CNN model achieved an overall accuracy of 98.5% for interpatient ECG arrhythmia classification, outperforming traditional approaches.
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