Improving ECG Classification Performance by Using an Optimized One-Dimensional Residual Network Model
Cardiovascular disease and its consequences on human health have never stopped and even show a trend of appearing in increasingly younger generations. The establishment of an excellent deep learning algorithm model to assist physicians in identifying and the early screening of ECG abnormalities can...
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| Format: | Article |
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MDPI AG
2022-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/24/12957 |
| _version_ | 1797461541875875840 |
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| author | Junbin Zang Juliang Wang Zhidong Zhang Yongqiu Zheng Chenyang Xue |
| author_facet | Junbin Zang Juliang Wang Zhidong Zhang Yongqiu Zheng Chenyang Xue |
| author_sort | Junbin Zang |
| collection | DOAJ |
| description | Cardiovascular disease and its consequences on human health have never stopped and even show a trend of appearing in increasingly younger generations. The establishment of an excellent deep learning algorithm model to assist physicians in identifying and the early screening of ECG abnormalities can effectively improve the accuracy of diagnosis. Therefore, in this study, the deep residual network model is adapted for feature extraction and classification of ECG signals by pooling embedded into layers and double channel connection. At the same time, the wavelet adaptive threshold denoising algorithm is used to complete the high signal-to-noise filtering of ECG signals. Then, the alternate pooling residual network (APRN) is compared with the convolutional neural network (CNN), CNN with one residual unit (CNN-R), and the deep residual network (ResNet-18) using ECG datasets from the American MIT-BIH arrhythmia and ST segment abnormality database, European ST-T database, and sudden cardiac death ambulatory ECG database. The results are as follows: The average classification accuracy of the APRN on the four datasets is 97.89%, while the accuracies on CNN, CNN-R, and ResNet-18 are 97.17%, 97.53%, and 97.73%, respectively. In addition, compared with ResNet-18, the classification accuracy of our APRN on each class of data improves by 16.44% in total. |
| first_indexed | 2024-03-09T17:20:53Z |
| format | Article |
| id | doaj.art-2dd9e378b07b4fcd9822965a0352b36b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:20:53Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2dd9e378b07b4fcd9822965a0352b36b2023-11-24T13:07:39ZengMDPI AGApplied Sciences2076-34172022-12-0112241295710.3390/app122412957Improving ECG Classification Performance by Using an Optimized One-Dimensional Residual Network ModelJunbin Zang0Juliang Wang1Zhidong Zhang2Yongqiu Zheng3Chenyang Xue4School of Software, North University of China, Taiyuan 030051, ChinaKey Laboratory of Instrumentation Science and Dynamic Measurement of Ministry of Education, North University of China, Taiyuan 030051, ChinaKey Laboratory of Instrumentation Science and Dynamic Measurement of Ministry of Education, North University of China, Taiyuan 030051, ChinaKey Laboratory of Instrumentation Science and Dynamic Measurement of Ministry of Education, North University of China, Taiyuan 030051, ChinaKey Laboratory of Instrumentation Science and Dynamic Measurement of Ministry of Education, North University of China, Taiyuan 030051, ChinaCardiovascular disease and its consequences on human health have never stopped and even show a trend of appearing in increasingly younger generations. The establishment of an excellent deep learning algorithm model to assist physicians in identifying and the early screening of ECG abnormalities can effectively improve the accuracy of diagnosis. Therefore, in this study, the deep residual network model is adapted for feature extraction and classification of ECG signals by pooling embedded into layers and double channel connection. At the same time, the wavelet adaptive threshold denoising algorithm is used to complete the high signal-to-noise filtering of ECG signals. Then, the alternate pooling residual network (APRN) is compared with the convolutional neural network (CNN), CNN with one residual unit (CNN-R), and the deep residual network (ResNet-18) using ECG datasets from the American MIT-BIH arrhythmia and ST segment abnormality database, European ST-T database, and sudden cardiac death ambulatory ECG database. The results are as follows: The average classification accuracy of the APRN on the four datasets is 97.89%, while the accuracies on CNN, CNN-R, and ResNet-18 are 97.17%, 97.53%, and 97.73%, respectively. In addition, compared with ResNet-18, the classification accuracy of our APRN on each class of data improves by 16.44% in total.https://www.mdpi.com/2076-3417/12/24/12957alternate pooling residual networkwavelet adaptive thresholding denoisingECG databasedepth residual network |
| spellingShingle | Junbin Zang Juliang Wang Zhidong Zhang Yongqiu Zheng Chenyang Xue Improving ECG Classification Performance by Using an Optimized One-Dimensional Residual Network Model Applied Sciences alternate pooling residual network wavelet adaptive thresholding denoising ECG database depth residual network |
| title | Improving ECG Classification Performance by Using an Optimized One-Dimensional Residual Network Model |
| title_full | Improving ECG Classification Performance by Using an Optimized One-Dimensional Residual Network Model |
| title_fullStr | Improving ECG Classification Performance by Using an Optimized One-Dimensional Residual Network Model |
| title_full_unstemmed | Improving ECG Classification Performance by Using an Optimized One-Dimensional Residual Network Model |
| title_short | Improving ECG Classification Performance by Using an Optimized One-Dimensional Residual Network Model |
| title_sort | improving ecg classification performance by using an optimized one dimensional residual network model |
| topic | alternate pooling residual network wavelet adaptive thresholding denoising ECG database depth residual network |
| url | https://www.mdpi.com/2076-3417/12/24/12957 |
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