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Single-scale convolution wavelet feature optimization classification model based on electrocardiogram coded image
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DOI：10.1016/j.bspc.2022.104202.png)
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Abstract
En 中文
Premature beats are a cardiovascular disease, which can lead to complications of other diseases. An electro-cardiograph (ECG) is the main tool for detecting premature beats, but accurate detection of premature beats still faces challenges. This work designs a single-scale convolution wavelet feature optimization (SSCWFO) classifi-cation model that is based on a single-scale convolutional neural network (SSCNN) model and optimized by Linear Discriminant Analysis (LDA) algorithm. First, the wavelet coefficient features are extracted from 12-lead ECG signals using Daubechies5, and then the 5th level detail coefficient features are transformed into images using Gramian Angular Difference Fields (GADFs). Thereafter the single-scale features are extracted by the SSCNN model. Next, the LDA algorithm was used to maximum inter-class distance and the minimum intra-class distance of each category. Finally, the Gaussian naive Bayes is used to classify-three types of signals. The results after LDA optimization show that the accuracy, precision, sensitivity, F1-score, and area under the ROC curve (AUC) of the SSCWFO model are improved to 91.12%, 92.29%, 92.55%, 92.13%, and 0.9197, respectively. Also, the network architectures of Resnet34, Resnet18, and LeNet 5 are used in this work for a comparison; their accuracy is 75.93%, 76.88%, and 68.48%, respectively. This shows that the method can effectively distinguish normal signals and two types of premature beat diseases and is helpful to the effective classification of diseases of premature beats.
Keywords:
Electrocardiograph
Linear Discriminant Analysis
Detail coefficient features
Feature optimization
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