Cardiac Arrhythmia Detection Using Wavelet Transform and convolutional neural networ

The detection of cardiac arrhythmias is highly significant as these irregularities can indicate serious heart conditions such as atrial fibrillation, tachycardia, or heart blocks. If not identified promptly, arrhythmias can lead to severe complications like stroke, heart failure, or cardiac arrest. Leveraging technologies such as deep learning and time-frequency transformations enhances the accuracy and speed of arrhythmia detection. This advancement plays a crucial role in improving treatment outcomes and reducing the risks associated with these disorders. In this paper, a new method based on wavelet transform (WT) and convolutional neural network (CNN) is presented. The simulatioan results on the MIT-BIH dataset shows that the accuracy of the proposed method is ۹۹.۸%.

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Keywords: Cardiac arrhythmia , Arrhythmia detection , Wavelet transform , Convolutional neural network.