Arrhythmia Detection by Data Fusion of ECG Scalograms and Phasograms

被引：0
作者：
Scarpiniti, Michele [1 ]
机构：
[1] Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, Rome
来源：
Sensors | 2024年 / 24卷 / 24期
关键词：
arrhythmia; continuous wavelet transform (CWT); deep learning; ECG classification; phasogram; scalogram;
D O I：
10.3390/s24248043
中图分类号：
学科分类号：
摘要：
The automatic detection of arrhythmia is of primary importance due to the huge number of victims caused worldwide by cardiovascular diseases. To this aim, several deep learning approaches have been recently proposed to automatically classify heartbeats in a small number of classes. Most of these approaches use convolutional neural networks (CNNs), exploiting some bi-dimensional representation of the ECG signal, such as spectrograms, scalograms, or similar. However, by adopting such representations, state-of-the-art approaches usually rely on the magnitude information alone, while the important phase information is often neglected. Motivated by these considerations, the focus of this paper is aimed at investigating the effect of fusing the magnitude and phase of the continuous wavelet transform (CWT), known as the scalogram and phasogram, respectively. Scalograms and phasograms are fused in a simple CNN-based architecture by using several fusion strategies, which fuse the information in the input layer, some intermediate layers, or in the output layer. Numerical results evaluated on the PhysioNet MIT-BIH Arrhythmia database show the effectiveness of the proposed ideas. Although a simple architecture is used, their competitiveness is high compared to other state-of-the-art approaches, by obtaining an overall accuracy of about 98.5% and sensitivity and specificity of 98.5% and 95.6%, respectively. © 2024 by the author.
引用
收藏
相关论文
共 111 条
  • [21] Zhang J., Liu A., Gao M., Chen X., Zhang X., Chen X., ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network, Artif. Intell. Med, 106, (2020)
  • [22] Ahmed A.A., Ali W., Abdullah T.A.A., Malebary S.J., Classifying cardiac arrhythmia from ECG signal using 1D CNN deep learning model, Mathematics, 11, (2023)
  • [23] Degirmenci M., Ozdemir M., Izci E., Akan A., Arrhythmic heartbeat classification using 2D convolutional neural networks, IRBM, 43, pp. 422-433, (2022)
  • [24] Naz M., Shah J.H., Khan M.A., Sharif M., Raza M., Damasevicius R., From ECG signals to images: A transformation based approach for deep learning, PeerJ Comput. Sci, 7, (2021)
  • [25] Kuila S., Dhanda N., Joardar S., ECG signal classification to detect heart arrhythmia using ELM and CNN, Multimed. Tools Appl, 82, pp. 29857-29881, (2023)
  • [26] Atal D.K., Singh M., Arrhythmia classification with ECG signals based on the optimization-enabled deep convolutional neural network, Comput. Methods Programs Biomed, 196, (2020)
  • [27] Li T., Zhou M., ECG classification using wavelet packet entropy and random forests, Entropy, 18, (2016)
  • [28] Ozaltin O., Yeniay O., A novel proposed CNN–SVM architecture for ECG scalograms classification, Soft Comput, 27, pp. 4639-4658, (2023)
  • [29] Madan P., Singh V., Singh D.P., Diwakar M., Pant B., Kishor A., A hybrid deep learning approach for ECG-based arrhythmia classification, Bioengineering, 9, (2022)
  • [30] Krol-Jozaga B., Atrial fibrillation detection using convolutional neural networks on 2-dimensional representation of ECG signal, Biomed. Signal Process. Control, 74, (2022)
12345678910