Analysis of Cardiac Arrhythmias Based on ResNet-ICBAM-2DCNN Dual-Channel Feature Fusion
Cardiovascular disease (CVD) poses a significant challenge to global health, with cardiac arrhythmia representing one of its most prevalent manifestations. The timely and precise classification of arrhythmias is critical for the effective management of CVD. This study introduces an innovative approa...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/3/661 |
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| author | Chuanjiang Wang Junhao Ma Guohui Wei Xiujuan Sun |
| author_facet | Chuanjiang Wang Junhao Ma Guohui Wei Xiujuan Sun |
| author_sort | Chuanjiang Wang |
| collection | DOAJ |
| description | Cardiovascular disease (CVD) poses a significant challenge to global health, with cardiac arrhythmia representing one of its most prevalent manifestations. The timely and precise classification of arrhythmias is critical for the effective management of CVD. This study introduces an innovative approach to enhancing arrhythmia classification accuracy through advanced Electrocardiogram (ECG) signal processing. We propose a dual-channel feature fusion strategy designed to enhance the precision and objectivity of ECG analysis. Initially, we apply an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and enhanced wavelet thresholding for robust noise reduction. Subsequently, in the primary channel, region of interest features are emphasized using a ResNet-ICBAM network model for feature extraction. In parallel, the secondary channel transforms 1D ECG signals into Gram angular difference field (GADF), Markov transition field (MTF), and recurrence plot (RP) representations, which are then subjected to two-dimensional convolutional neural network (2D-CNN) feature extraction. Post-extraction, the features from both channels are fused and classified. When evaluated on the MIT-BIH database, our method achieves a classification accuracy of 97.80%. Compared to other methods, our approach of two-channel feature fusion has a significant improvement in overall performance by adding a 2D convolutional network. This methodology represents a substantial advancement in ECG signal processing, offering significant potential for clinical applications and improving patient care efficiency and accuracy. |
| format | Article |
| id | doaj-art-3a7576d17e1441e78c84c2270ec93dbf |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-3a7576d17e1441e78c84c2270ec93dbf2025-08-20T02:48:10ZengMDPI AGSensors1424-82202025-01-0125366110.3390/s25030661Analysis of Cardiac Arrhythmias Based on ResNet-ICBAM-2DCNN Dual-Channel Feature FusionChuanjiang Wang0Junhao Ma1Guohui Wei2Xiujuan Sun3College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, ChinaZhuhai Inpower Electric Co., Ltd., Zhuhai 519000, ChinaCollege of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, ChinaCardiovascular disease (CVD) poses a significant challenge to global health, with cardiac arrhythmia representing one of its most prevalent manifestations. The timely and precise classification of arrhythmias is critical for the effective management of CVD. This study introduces an innovative approach to enhancing arrhythmia classification accuracy through advanced Electrocardiogram (ECG) signal processing. We propose a dual-channel feature fusion strategy designed to enhance the precision and objectivity of ECG analysis. Initially, we apply an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and enhanced wavelet thresholding for robust noise reduction. Subsequently, in the primary channel, region of interest features are emphasized using a ResNet-ICBAM network model for feature extraction. In parallel, the secondary channel transforms 1D ECG signals into Gram angular difference field (GADF), Markov transition field (MTF), and recurrence plot (RP) representations, which are then subjected to two-dimensional convolutional neural network (2D-CNN) feature extraction. Post-extraction, the features from both channels are fused and classified. When evaluated on the MIT-BIH database, our method achieves a classification accuracy of 97.80%. Compared to other methods, our approach of two-channel feature fusion has a significant improvement in overall performance by adding a 2D convolutional network. This methodology represents a substantial advancement in ECG signal processing, offering significant potential for clinical applications and improving patient care efficiency and accuracy.https://www.mdpi.com/1424-8220/25/3/661arrhythmiaabnormal ECG signal detectiondeep learningfeature fusionattention mechanism |
| spellingShingle | Chuanjiang Wang Junhao Ma Guohui Wei Xiujuan Sun Analysis of Cardiac Arrhythmias Based on ResNet-ICBAM-2DCNN Dual-Channel Feature Fusion Sensors arrhythmia abnormal ECG signal detection deep learning feature fusion attention mechanism |
| title | Analysis of Cardiac Arrhythmias Based on ResNet-ICBAM-2DCNN Dual-Channel Feature Fusion |
| title_full | Analysis of Cardiac Arrhythmias Based on ResNet-ICBAM-2DCNN Dual-Channel Feature Fusion |
| title_fullStr | Analysis of Cardiac Arrhythmias Based on ResNet-ICBAM-2DCNN Dual-Channel Feature Fusion |
| title_full_unstemmed | Analysis of Cardiac Arrhythmias Based on ResNet-ICBAM-2DCNN Dual-Channel Feature Fusion |
| title_short | Analysis of Cardiac Arrhythmias Based on ResNet-ICBAM-2DCNN Dual-Channel Feature Fusion |
| title_sort | analysis of cardiac arrhythmias based on resnet icbam 2dcnn dual channel feature fusion |
| topic | arrhythmia abnormal ECG signal detection deep learning feature fusion attention mechanism |
| url | https://www.mdpi.com/1424-8220/25/3/661 |
| work_keys_str_mv | AT chuanjiangwang analysisofcardiacarrhythmiasbasedonresneticbam2dcnndualchannelfeaturefusion AT junhaoma analysisofcardiacarrhythmiasbasedonresneticbam2dcnndualchannelfeaturefusion AT guohuiwei analysisofcardiacarrhythmiasbasedonresneticbam2dcnndualchannelfeaturefusion AT xiujuansun analysisofcardiacarrhythmiasbasedonresneticbam2dcnndualchannelfeaturefusion |