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Zhu H, Jiang N, Xia S, Tong J. Atrial Fibrillation Prediction Based on Recurrence Plot and ResNet. SENSORS (BASEL, SWITZERLAND) 2024; 24:4978. [PMID: 39124025 PMCID: PMC11314825 DOI: 10.3390/s24154978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Revised: 07/27/2024] [Accepted: 07/30/2024] [Indexed: 08/12/2024]
Abstract
Atrial fibrillation (AF) is the most prevalent form of arrhythmia, with a rising incidence and prevalence worldwide, posing significant implications for public health. In this paper, we introduce an approach that combines the Recurrence Plot (RP) technique and the ResNet architecture to predict AF. Our method involves three main steps: using wavelet filtering to remove noise interference; generating RPs through phase space reconstruction; and employing a multi-level chained residual network for AF prediction. To validate our approach, we established a comprehensive database consisting of electrocardiogram (ECG) recordings from 1008 AF patients and 48,292 Non-AF patients, with a total of 2067 and 93,129 ECGs, respectively. The experimental results demonstrated high levels of prediction precision (90.5%), recall (89.1%), F1 score (89.8%), accuracy (93.4%), and AUC (96%) on our dataset. Moreover, when tested on a publicly available AF dataset (AFPDB), our method achieved even higher prediction precision (94.8%), recall (99.4%), F1 score (97.0%), accuracy (97.0%), and AUC (99.7%). These findings suggest that our proposed method can effectively extract subtle information from ECG signals, leading to highly accurate AF predictions.
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Affiliation(s)
- Haihang Zhu
- School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China; (H.Z.); (N.J.)
| | - Nan Jiang
- School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China; (H.Z.); (N.J.)
| | - Shudong Xia
- The Fourth Affiliated Hospital Zhejiang University School of Medicine, Jinhua 321000, China;
| | - Jijun Tong
- School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China; (H.Z.); (N.J.)
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Zhang H, Liu C, Tang F, Li M, Xia L, Crozier S, Xu W, Liu F. AF automatic classification based on different time-delay values of the recurrence plot. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083402 DOI: 10.1109/embc40787.2023.10340928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Recurrence plot (RP) has been widely used to transform 1D ECG waveforms into 2D images and explore the recurrence patterns of the electrical signals generated by the cardiac system. However, selecting the critical parameter time-delay τ for creating an RP has not been well-reported. In this work, we investigated the influence of different τ values on the RP-based AF prediction. And the results illustrated that the best classification performance could be achieved at τ=1 with full characters of the dynamic system.Clinical Relevance-This work established the AF classification system based on recurrence features and found the optimal parameters of the recurrence plot improving the ECG-based classification performance.
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Zhang H, Liu C, Tang F, Li M, Zhang D, Xia L, Crozier S, Gan H, Zhao N, Xu W, Liu F. Atrial fibrillation classification based on the 2D representation of minimal subset ECG and a non-deep neural network. Front Physiol 2023; 14:1070621. [PMID: 36866172 PMCID: PMC9971936 DOI: 10.3389/fphys.2023.1070621] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 01/30/2023] [Indexed: 02/16/2023] Open
Abstract
Atrial fibrillation (AF) is the most common cardiac arrhythmia, and its early detection is critical for preventing complications and optimizing treatment. In this study, a novel AF prediction method is proposed, which is based on investigating a subset of the 12-lead ECG data using a recurrent plot and ParNet-adv model. The minimal subset of ECG leads (II &V1) is determined via a forward stepwise selection procedure, and the selected 1D ECG data is transformed into 2D recurrence plot (RP) images as an input to train a shallow ParNet-adv Network for AF prediction. In this study, the proposed method achieved F1 score of 0.9763, Precision of 0.9654, Recall of 0.9875, Specificity of 0.9646, and Accuracy of 0.9760, which significantly outperformed solutions based on single leads and complete 12 leads. When studying several ECG datasets, including the CPSC and Georgia ECG databases of the PhysioNet/Computing in Cardiology Challenge 2020, the new method achieved F1 score of 0.9693 and 0.8660, respectively. The results suggested a good generalization of the proposed method. Compared with several state-of-art frameworks, the proposed model with a shallow network of only 12 depths and asymmetric convolutions achieved the highest average F1 score. Extensive experimental studies proved that the proposed method has a high potential for AF prediction in clinical and particularly wearable applications.
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Affiliation(s)
- Hua Zhang
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Chengyu Liu
- School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China
| | - Fangfang Tang
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Mingyan Li
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Dongxia Zhang
- Zhejiang Provincial Centre for Disease Control and Prevention CN, Hangzhou, Zhejiang, China
| | - Ling Xia
- Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Stuart Crozier
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Hongping Gan
- School of Software, Northwestern Polytechnical University, Xi’an, China
| | - Nan Zhao
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Wenlong Xu
- Department of Biomedical Engineering, China Jiliang University, Hangzhou, Zhejiang, China,*Correspondence: Wenlong Xu, ; Feng Liu,
| | - Feng Liu
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia,*Correspondence: Wenlong Xu, ; Feng Liu,
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Lu X, Wang X, Zhang W, Wen A, Ren Y. An end-to-end model for ECG signals classification based on residual attention network. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Research on Recurrence Plot Feature Quantization Method Based on Image Texture Analysis. JOURNAL OF ENVIRONMENTAL AND PUBLIC HEALTH 2022; 2022:2495024. [PMID: 35978591 PMCID: PMC9377861 DOI: 10.1155/2022/2495024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/15/2022] [Accepted: 07/09/2022] [Indexed: 11/17/2022]
Abstract
The nonlinear time-series analysis method, based on the recurrence plot theory, has received great attention from researchers and has been successfully used in multiple fields. However, traditional recurrence plots that use Heaviside step functions to determine the recursive behavior of a point in the phase space have two problems: (1) Heaviside step functions produce a rigid boundary, resulting in information loss; and (2) the selection of the critical distance, ε, is crucial; if the selection is inappropriate, it will result in a low-dimensional dynamics error, and as of now, there exists no unified method for selecting this parameter. With regard to the problems described above, the novelty of this article lies in the following: (1) when determining the state-phase point recursiveness, a Gaussian function is used to replace the Heaviside function, thereby solving the rigidity and binary value problems of the recursive analysis results caused by the Heaviside step function; and (2) texture analysis is performed on a recurrence plot, new ways of studying complex system dynamics features are proposed, and a system of complex system dynamic-like measurement methods is built.
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