Electrocardiogram signal classification algorithm based on the continuous wavelet transform and googlenet in an internet of things context

The developments in computer-aided diagnostic technology have promoted the advancement of the Medical Internet of Things. Because electrocardiogram (ECG) signals can reflect the health of the human heart, their classification is conducive to diagnosing heart disease. In this study, a novel classification method for ECG signals based on the continuous wavelet transform (CWT) and a convolutional neural network (CNN) is proposed. The proposed method can construct the time-frequency image of an ECG signal using CWT. Subsequently, the image is input to a CNN model. By leveraging the GoogLeNet network model for image recognition, the model establishes an accurate mapping for recognizing different ECG signals. Multiple public datasets are used to validate the effectiveness of the proposed algorithm. In addition, this method exhibits a higher recognition accuracy than other methods.