Determining the type of arrhythmia is crucial for prevention and early diagnosis of cardiovascular diseases.
Objective
This aims to address potential information loss caused by preprocessing, improve model performance, and accurately identify multiple types of arrhythmias.
Methods
This study proposes the use of wavelet transform denoising and convolutional neural network (CNN) model to classify and identify six types of arrhythmias. The original electrocardiosignal was transformed into a two-dimensional gray image by construction, and the data were amplified by fixed template clipping. Then, six arrhythmias were identified using an improved two-dimensional CNN model.
Results
The classification accuracy, sensitivity, and specificity of the proposed method reached 90.50%, 81.70%, and 97.16%, respectively, and six types of arrhythmias were accurately identified.
Conclusions
The results showed that the wavelet transform as a preprocessing method can effectively improve the classification accuracy of the multiple types of arrhythmias. The method proposed in this study can provide a new reference for clinicians in diagnosing arrhythmia.
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