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Zhou R, Lu L, Liu Z, Xiang T, Liang Z, Clifton DA, Dong Y, Zhang YT. Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction: A Multi-Dataset Study. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024; 46:3305-3320. [PMID: 38096090 DOI: 10.1109/tpami.2023.3342828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Electrocardiography (ECG) is a non-invasive tool for predicting cardiovascular diseases (CVDs). Current ECG-based diagnosis systems show promising performance owing to the rapid development of deep learning techniques. However, the label scarcity problem, the co-occurrence of multiple CVDs and the poor performance on unseen datasets greatly hinder the widespread application of deep learning-based models. Addressing them in a unified framework remains a significant challenge. To this end, we propose a multi-label semi-supervised model (ECGMatch) to recognize multiple CVDs simultaneously with limited supervision. In the ECGMatch, an ECGAugment module is developed for weak and strong ECG data augmentation, which generates diverse samples for model training. Subsequently, a hyperparameter-efficient framework with neighbor agreement modeling and knowledge distillation is designed for pseudo-label generation and refinement, which mitigates the label scarcity problem. Finally, a label correlation alignment module is proposed to capture the co-occurrence information of different CVDs within labeled samples and propagate this information to unlabeled samples. Extensive experiments on four datasets and three protocols demonstrate the effectiveness and stability of the proposed model, especially on unseen datasets. As such, this model can pave the way for diagnostic systems that achieve robust performance on multi-label CVDs prediction with limited supervision.
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Moreno-Sánchez PA, García-Isla G, Corino VDA, Vehkaoja A, Brukamp K, van Gils M, Mainardi L. ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review. Comput Biol Med 2024; 172:108235. [PMID: 38460311 DOI: 10.1016/j.compbiomed.2024.108235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 02/07/2024] [Accepted: 02/25/2024] [Indexed: 03/11/2024]
Abstract
Cardiovascular diseases (CVD) are a leading cause of death globally, and result in significant morbidity and reduced quality of life. The electrocardiogram (ECG) plays a crucial role in CVD diagnosis, prognosis, and prevention; however, different challenges still remain, such as an increasing unmet demand for skilled cardiologists capable of accurately interpreting ECG. This leads to higher workload and potential diagnostic inaccuracies. Data-driven approaches, such as machine learning (ML) and deep learning (DL) have emerged to improve existing computer-assisted solutions and enhance physicians' ECG interpretation of the complex mechanisms underlying CVD. However, many ML and DL models used to detect ECG-based CVD suffer from a lack of explainability, bias, as well as ethical, legal, and societal implications (ELSI). Despite the critical importance of these Trustworthy Artificial Intelligence (AI) aspects, there is a lack of comprehensive literature reviews that examine the current trends in ECG-based solutions for CVD diagnosis or prognosis that use ML and DL models and address the Trustworthy AI requirements. This review aims to bridge this knowledge gap by providing a systematic review to undertake a holistic analysis across multiple dimensions of these data-driven models such as type of CVD addressed, dataset characteristics, data input modalities, ML and DL algorithms (with a focus on DL), and aspects of Trustworthy AI like explainability, bias and ethical considerations. Additionally, within the analyzed dimensions, various challenges are identified. To these, we provide concrete recommendations, equipping other researchers with valuable insights to understand the current state of the field comprehensively.
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Affiliation(s)
| | - Guadalupe García-Isla
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Italy
| | - Valentina D A Corino
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Italy
| | - Antti Vehkaoja
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | | | - Mark van Gils
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Luca Mainardi
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Italy
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Safdar MF, Nowak RM, Pałka P. Pre-Processing techniques and artificial intelligence algorithms for electrocardiogram (ECG) signals analysis: A comprehensive review. Comput Biol Med 2024; 170:107908. [PMID: 38217973 DOI: 10.1016/j.compbiomed.2023.107908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/19/2023] [Accepted: 12/24/2023] [Indexed: 01/15/2024]
Abstract
Electrocardiogram (ECG) are the physiological signals and a standard test to measure the heart's electrical activity that depicts the movement of cardiac muscles. A review study has been conducted on ECG signals analysis with the help of artificial intelligence (AI) methods over the last ten years i.e., 2012-22. Primarily, the method of ECG analysis by software systems was divided into classical signal processing (e.g. spectrograms or filters), machine learning (ML) and deep learning (DL), including recursive models, transformers and hybrid. Secondly, the data sources and benchmark datasets were depicted. Authors grouped resources by ECG acquisition methods into hospital-based portable machines and wearable devices. Authors also included new trends like advanced pre-processing, data augmentation, simulations and agent-based modeling. The study found improvement in ECG examination perfection made each year through ML, DL, hybrid models, and transformers. Convolutional neural networks and hybrid models were more targeted and proved efficient. The transformer model extended the accuracy from 90% to 98%. The Physio-Net library helps acquire ECG signals, including the popular benchmark databases such as MIT-BIH, PTB, and challenging datasets. Similarly, wearable devices have been established as a appropriate option for monitoring patient health without the time and place limitations and are also helpful for AI model calibration with so far accuracy of 82%-83% on Samsung smartwatch. In the pre-processing signals, spectrogram generation through Fourier and wavelet transformations are erected leading approaches promoting on average accuracy of 90%-95%. Likewise, data enhancement using geometrical techniques is well-considered; however, extraction and concatenation-based methods need attention. As the what-if analysis in healthcare or cardiac issues can be performed using a complex simulation, the study reviews agent-based modeling and simulation approaches for cardiovascular risk event assessment.
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Affiliation(s)
- Muhammad Farhan Safdar
- Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland.
| | - Robert Marek Nowak
- Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland
| | - Piotr Pałka
- Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland
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Zhou R, Yao J, Hong Q, Zheng Y, Zheng L. DAMS-Net: Dual attention and multi-scale information fusion network for 12-lead ECG classification. Methods 2023; 220:134-141. [PMID: 37967757 DOI: 10.1016/j.ymeth.2023.10.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 10/08/2023] [Accepted: 10/23/2023] [Indexed: 11/17/2023] Open
Abstract
Automated 12-lead electrocardiographic (ECG) classification algorithms play an important role in the diagnosis of clinical arrhythmias. Current methods that perform well in the field of automatic ECG classification are usually based on Convolutional Neural Networks (CNN) or Transformer. However, due to the intrinsic locality of convolution operations, CNN can't extract long-dependence between series. On the other side, the Transformer design includes a built-in global self-attention mechanism, but it doesn't pay enough attention to local features. In this paper, we propose DAMS-Net, which combines the advantages of Transformer and CNN, introducing a spatial attention module and a channel attention module using a CNN-Transformer hybrid encoder to adaptively focus on the significant features of global and local parts between space and channels. In addition, our proposal fuses multi-scale information to capture high and low-level semantic information by skip-connections. We evaluate our method on the 2018 Physiological Electrical Signaling Challenge dataset, and our proposal achieves a precision rate of 83.6%, a recall rate of 84.7%, and an F1-score of 0.839. The classification performance is superior to all current single-model methods evaluated in this dataset. The experimental results demonstrate the promising application of our proposed method in 12-lead ECG automatic classification tasks.
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Affiliation(s)
- Rongzhou Zhou
- Center for Digital Media Computing, School of Film, Xiamen University, Xiamen, Fujian, 361005, China.
| | - Junfeng Yao
- Center for Digital Media Computing, School of Film, Xiamen University, Xiamen, Fujian, 361005, China; School of Information, Xiamen University, Xiamen, Fujian, 361005, China; Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan, China.
| | - Qingqi Hong
- Center for Digital Media Computing, School of Film, Xiamen University, Xiamen, Fujian, 361005, China.
| | - Yuan Zheng
- School of Information, Xiamen University, Xiamen, Fujian, 361005, China.
| | - Liling Zheng
- Department of Cardiovascular Surgery, First Hospital of Quanzhou Affiliated to Fujian Medical University, Quanzhou, Fujian, 36200, China.
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Yang L, Zheng Y, Liu Z, Tang R, Ma L, Chen Y, Zhang T, Li W. SAR model for accurate detection of multi-label arrhythmias from electrocardiograms. Heliyon 2023; 9:e21627. [PMID: 38027936 PMCID: PMC10663866 DOI: 10.1016/j.heliyon.2023.e21627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 10/06/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Objective Arrhythmias are prevalent symptoms of cardiovascular disease, necessitating accurate and timely detection to mitigate associated risks. Detecting arrhythmias from ECGs quickly and accurately holds great significance in preventing heart disease and reducing mortality. This research endeavors to outperform previous studies by developing a scientific neural network model capable of training and predicting ECG signals for 11 categories of arrhythmias, accounting for up to 5 co-existing labels. Methods In this study, we initially address the issue of imbalanced datasets by employing Borderline SMOTE and Cluster Centroids techniques during preprocessing. Subsequently, we propose a novel SAR model that combines attention and resnet mechanisms. The dataset is subjected to a 10-fold validation process to train and evaluate the model. Finally, several metrics such as HammingLoss, RankingLoss, F1-score, AUC and Coverage are used to evaluate the model. Results By evaluating the results of the tests, the average Hamming Loss is 1.12 %, the average Ranking Loss is 1.17 %, the average Micro F1-score is 98.46 %, the average Micro AUC is 98.76 %, and the average Coverage is 3.2762. The results show that the SAR model outperforms previous related studies on the task of classifying arrhythmia signals with multiple categories and labels. Conclusion The SAR model demonstrated excellent performance in accurately classifying multi-category and multi-label arrhythmia signals, affirming its scientific validity. Compared with previous studies, the model achieves a certain improvement in performance, which can help cardiologists to achieve scientific and accurate diagnosis of arrhythmia diseases.
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Affiliation(s)
- Liuyang Yang
- The Affiliated Hospital of Kunming University of Science and Technology. The First People's Hospital of Yunnan Province, Kunming, Yunnan, China
- Department of Management Science and Information System, Faculty of Management and Economics, Kunming University of Science and Technology, Kunming, Yunnan, China
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Yaqing Zheng
- The Affiliated Hospital of Kunming University of Science and Technology. The First People's Hospital of Yunnan Province, Kunming, Yunnan, China
| | - Zhimin Liu
- The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan,China
| | - Rui Tang
- Department of Management Science and Information System, Faculty of Management and Economics, Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Libing Ma
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Department of Respiratory and Critical Care Medicine, the Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, China
| | - Yu Chen
- Department of Management Science and Information System, Faculty of Management and Economics, Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Ting Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Wei Li
- The Affiliated Hospital of Kunming University of Science and Technology. The First People's Hospital of Yunnan Province, Kunming, Yunnan, China
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Nahak S, Saha G. Ensembled Feature based Multi-Label ECG Arrhythmia Classification. 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: 38083668 DOI: 10.1109/embc40787.2023.10340005] [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
Arrhythmia is one of the major contributors to sudden cardiac death. Compared to single-label arrhythmia classification, multi-label classification provides the advantage of simultaneous detection of different cardiac ailments. Existing multi-label approaches employ deep learning models, which have several limitations, e.g., data dependency, overfitting, complexity, and lack of interpretability. In this work, we propose a multi-label classification framework with ensembled hand-crafted features added with label powerset, which better preserves the correlation between classes. Experimentation with CPSC 2018 database shows that the proposed model provides accuracy and F1-score of 88.52% and 88.45% for single-label data and 88.11% and 86.32% for multi-label data, respectively. The proposed method improves individual class performance for some important diseases compared to the existing multi-label state-of-the-art methods.Clinical relevance- Early detection of life-threatening multi-label ECG patterns consisting of atrial fibrillation, atrioventricular block, and right bundle branch block with high accuracy can reduce the sudden cardiac death rate.
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Ran S, Li X, Zhao B, Jiang Y, Yang X, Cheng C. Label correlation embedding guided network for multi-label ECG arrhythmia diagnosis. Knowl Based Syst 2023. [DOI: 10.1016/j.knosys.2023.110545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
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Feng P, Fu J, Wang N, Zhou Y, Zhou B, Wang Z. Semantic-aware alignment and label propagation for cross-domain arrhythmia classification. Knowl Based Syst 2023. [DOI: 10.1016/j.knosys.2023.110323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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NOSMFuse: An infrared and visible image fusion approach based on norm optimization and slime mold architecture. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03591-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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A New 12-Lead ECG Signals Fusion Method Using Evolutionary CNN Trees for Arrhythmia Detection. MATHEMATICS 2022. [DOI: 10.3390/math10111911] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The 12 leads of electrocardiogram (ECG) signals show the heart activities from different angles of coronal and axial planes; hence, the signals of these 12 leads have functional dependence on each other. This paper proposes a novel method for fusing the data of 12-lead ECG signals to diagnose heart problems. In the first phase of the proposed method, the time-frequency transform is employed to fuse the functional data of leads and extract the frequency data of ECG signals in 12 leads. After that, their dependence is evaluated through the correlation analysis. In the second phase, a structural learning method is adopted to extract the structural data from these 12 leads. Moreover, deep convolutional neural network (CNN) models are coded in this phase through genetic programming. These trees are responsible for learning deep structural features from functional data extracted from 12 leads. These trees are upgraded through the execution of the genetic programming (GP) algorithm to extract the optimal features. These two phases are used together to fuse the leads of ECG signals to diagnose various heart problems. According to the test results on ChapmanECG, including the signals of 10,646 patients, the proposed method enjoys the mean accuracy of 97.60% in the diagnosis of various types of arrhythmias in the Chapman dataset. It also outperformed the state-of-the-art methods.
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Liang X, Li L, Liu Y, Chen D, Wang X, Hu S, Wang J, Zhang H, Sun C, Liu C. ECG_SegNet: An ECG delineation model based on the encoder-decoder structure. Comput Biol Med 2022; 145:105445. [PMID: 35366468 DOI: 10.1016/j.compbiomed.2022.105445] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 03/21/2022] [Accepted: 03/21/2022] [Indexed: 01/10/2023]
Abstract
With the increasing usage of wearable electrocardiogram (ECG) monitoring devices, it is necessary to develop models and algorithms that can analyze the large amounts of ECG data obtained in real-time. Accurate ECG delineation is key to assisting cardiologists in diagnosing cardiac diseases. The main objective of this study is to design a delineation model based on the encoder-decoder structure to detect different heartbeat waveforms, including P-waves, QRS complexes, T-waves, and No waves (NW), as well as the onset and offset of these waveforms. First, the introduction of a standard dilated convolution module (SDCM) into the encoder path enabled the model to extract more useful ECG signal-informative features. Subsequently, bidirectional long short-term memory (BiLSTM) was added to the encoding structure to obtain numerous temporal features. Moreover, the feature sets of the ECG signals at each level in the encoder path were connected to the decoder part for multi-scale decoding to mitigate the information loss caused by the pooling operation in the encoding process. Finally, the proposed model was trained and tested on both QT and LU databases, and it achieved accurate results compared to other state-of-the-art methods. Regarding the QT database, the average accuracy of ECG waveform classification was 96.90%, and an average classification accuracy of 95.40% was obtained on the LU database. In addition, average F1 values of 99.58% and 97.05% were achieved in the ECG delineation task of the QT and LU databases, respectively. The results show that the proposed ECG_SegNet model has good flexibility and reliability when applied to ECG delineation, and it is a reliable method for analyzing ECG signals in real-time.
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Affiliation(s)
- Xiaohong Liang
- School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China
| | - Liping Li
- College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250355, China
| | - Yuanyuan Liu
- School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China.
| | - Dan Chen
- Department of Cardiology Electrocardiogram Room, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
| | - Xinpei Wang
- School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China
| | - Shunbo Hu
- School of Information Science and Engineering, Linyi University, Linyi, Shandong, 276005, China
| | - Jikuo Wang
- School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China
| | - Huan Zhang
- School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China
| | - Chengfa Sun
- School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China
| | - Changchun Liu
- School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China
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