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Yang X, Liu L, Yan Z, Yu J, Hu X, Yu X, Dong C, Chen J, Liu H, Yu Z, Deng X, Ni D, Huang X, Gou Z. Hierarchical online contrastive anomaly detection for fetal arrhythmia diagnosis in ultrasound. Med Image Anal 2024; 97:103229. [PMID: 38897033 DOI: 10.1016/j.media.2024.103229] [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: 07/19/2023] [Revised: 03/10/2024] [Accepted: 06/03/2024] [Indexed: 06/21/2024]
Abstract
Arrhythmia is a major cardiac abnormality in fetuses. Therefore, early diagnosis of arrhythmia is clinically crucial. Pulsed-wave Doppler ultrasound is a commonly used diagnostic tool for fetal arrhythmia. Its key step for diagnosis involves identifying adjacent measurable cardiac cycles (MCCs). As cardiac activity is complex and the experience of sonographers is often varied, automation can improve user-independence and diagnostic-validity. However, arrhythmias pose several challenges for automation because of complex waveform variations, which can cause major localization bias and missed or false detection of MCCs. Filtering out non-MCC anomalies is difficult because of large intra-class and small inter-class variations between MCCs and non-MCCs caused by agnostic morphological waveform variations. Moreover, rare arrhythmia cases are insufficient for classification algorithms to adequately learn discriminative features. Using only normal cases for training, we propose a novel hierarchical online contrastive anomaly detection (HOCAD) framework for arrhythmia diagnosis during test time. The contribution of this study is three-fold. First, we develop a coarse-to-fine framework inspired by hierarchical diagnostic logic, which can refine localization and avoid missed detection of MCCs. Second, we propose an online learning-based contrastive anomaly detection with two new anomaly scores, which can adaptively filter out non-MCC anomalies on a single image during testing. With these complementary efforts, we precisely determine MCCs for correct measurements and diagnosis. Third, to the best of our knowledge, this is the first reported study investigating intelligent diagnosis of fetal arrhythmia on a large-scale and multi-center ultrasound dataset. Extensive experiments on 3850 cases, including 266 cases covering three typical types of arrhythmias, demonstrate the effectiveness of the proposed framework.
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Affiliation(s)
- Xin Yang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China, Shenzhen, China; Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
| | - Lian Liu
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China, Shenzhen, China; Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
| | - Zhongnuo Yan
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China, Shenzhen, China; Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
| | - Junxuan Yu
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China, Shenzhen, China; Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
| | - Xindi Hu
- Shenzhen RayShape Medical Technology Co., Ltd, Shenzhen, Guangdong, China
| | - Xuejuan Yu
- Department of Ultrasonography, Suzhou Xiangcheng People's Hospital, Suzhou, Jiangsu, China
| | - Caixia Dong
- Department of Ultrasonography, Wulin Hospital, Hangzhou, Zhejiang, China
| | - Ju Chen
- Department of Ultrasonography, Taicang First People's Hospital, Suzhou, Jiangsu, China
| | - Hongmei Liu
- Department of Ultrasonography, Panzhou Emerging Hospital, Panzhou, Guizhou, China
| | - Zhuan Yu
- Department of Ultrasonography, The Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu, China
| | - Xuedong Deng
- Center for Medical Ultrasound, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China
| | - Dong Ni
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China, Shenzhen, China; Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
| | - Xiaoqiong Huang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China, Shenzhen, China; Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China.
| | - Zhongshan Gou
- Center for Cardiovascular Disease, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China.
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Lun X, Zhang Y, Zhu M, Lian Y, Hou Y. A Combined Virtual Electrode-Based ESA and CNN Method for MI-EEG Signal Feature Extraction and Classification. SENSORS (BASEL, SWITZERLAND) 2023; 23:8893. [PMID: 37960592 PMCID: PMC10649179 DOI: 10.3390/s23218893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 10/27/2023] [Accepted: 10/30/2023] [Indexed: 11/15/2023]
Abstract
A Brain-Computer Interface (BCI) is a medium for communication between the human brain and computers, which does not rely on other human neural tissues, but only decodes Electroencephalography (EEG) signals and converts them into commands to control external devices. Motor Imagery (MI) is an important BCI paradigm that generates a spontaneous EEG signal without external stimulation by imagining limb movements to strengthen the brain's compensatory function, and it has a promising future in the field of computer-aided diagnosis and rehabilitation technology for brain diseases. However, there are a series of technical difficulties in the research of motor imagery-based brain-computer interface (MI-BCI) systems, such as: large individual differences in subjects and poor performance of the cross-subject classification model; a low signal-to-noise ratio of EEG signals and poor classification accuracy; and the poor online performance of the MI-BCI system. To address the above problems, this paper proposed a combined virtual electrode-based EEG Source Analysis (ESA) and Convolutional Neural Network (CNN) method for MI-EEG signal feature extraction and classification. The outcomes reveal that the online MI-BCI system developed based on this method can improve the decoding ability of multi-task MI-EEG after training, it can learn generalized features from multiple subjects in cross-subject experiments and has some adaptability to the individual differences of new subjects, and it can decode the EEG intent online and realize the brain control function of the intelligent cart, which provides a new idea for the research of an online MI-BCI system.
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Affiliation(s)
| | | | | | | | - Yimin Hou
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; (X.L.); (Y.Z.); (M.Z.); (Y.L.)
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Li N, Liu L, Yang Z, Qin S. A self-adjusting ant colony clustering algorithm for ECG arrhythmia classification based on a correction mechanism. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 235:107519. [PMID: 37040683 DOI: 10.1016/j.cmpb.2023.107519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/17/2023] [Accepted: 03/28/2023] [Indexed: 05/08/2023]
Abstract
BACKGROUND AND OBJECTIVE As a representative type of cardiovascular disease, persistent arrhythmias can often become life-threatening. In recent years, machine learning-based ECG arrhythmia classification aided methods have been effective in assisting physicians with their diagnosis, but these methods have problems such as complex model structures, poor feature perception ability, and low classification accuracy. METHODS In this paper, a self-adjusting ant colony clustering algorithm for ECG arrhythmia classification based on a correction mechanism is proposed. This method does not distinguish between subjects when establishing the dataset in order to reduce the effect of differences in ECG signal features between individuals, thus improving the robustness of the model. When the classification is achieved, a correction mechanism is introduced to correct outliers caused by the accumulation of errors in the classification process in order to improve the classification accuracy of the model. According to the principle that the flow rate of gas can be increased under the convergence channel, a dynamically updated pheromone volatilization coefficient ρ, namely the increased flow rate ρ, is introduced to help the model converge more stably and faster. As the ants move, the next transfer target is selected by a truly self-adjusting transfer method, and the transfer probability is dynamically adjusted according to the pheromone concentration and the path distance. RESULTS Based on the MIT-BIH arrhythmia dataset, the new algorithm achieved classification of five heart rhythm types, with an overall accuracy of 99.00%. Compared to other experimental models, the classification accuracy of the proposed method represents a 0.2% to 16.6% improvement, and compared to other current studies, the classification accuracy of the proposed method is 0.65% to 7.5% better. CONCLUSIONS This paper addresses the shortcomings of ECG arrhythmia classification methods based on feature engineering, traditional machine learning and deep learning, and presents a self-adjusting ant colony clustering algorithm for ECG arrhythmia classification based on a correction mechanism. Experiments demonstrate the superiority of the proposed method compared to basic models as well as those with improved partial structures. Furthermore, the proposed method achieves very high classification accuracy with a simple structure and fewer iterations than other current methods.
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Affiliation(s)
- Ning Li
- Xi'an University of Technology, Xi'an, China.
| | - Linyue Liu
- Xi'an Technological University, Xi'an, China
| | | | - Shuguang Qin
- The Second Affiliate Hospital of Xi'an Jiaotong University, Xi'an, China
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Serhal H, Abdallah N, Marion JM, Chauvet P, Oueidat M, Humeau-Heurtier A. An EMD-based approach for atrial fibrillation classification using wavelets and convolutional neural network. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Denysyuk HV, Pinto RJ, Silva PM, Duarte RP, Marinho FA, Pimenta L, Gouveia AJ, Gonçalves NJ, Coelho PJ, Zdravevski E, Lameski P, Leithardt V, Garcia NM, Pires IM. Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review. Heliyon 2023; 9:e13601. [PMID: 36852052 PMCID: PMC9958295 DOI: 10.1016/j.heliyon.2023.e13601] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 01/31/2023] [Accepted: 02/05/2023] [Indexed: 02/12/2023] Open
Abstract
The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient's autonomy.
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Key Words
- AI, Artificial Intelligence
- BNN, Binarized Neural Network
- CNN, Concolutional Neural Networks
- Cardiovascular diseases
- DL, Deep Learning
- DNN, Deep Neural Networks
- Diagnosis
- ECG sensors
- ECG, Electrocardiography
- GAN, Generative Adversarial Networks
- GMM, Gaussian Mixture Model
- GNB, Gaussian Naive bayes
- GRU, Gated Recurrent Unit
- LASSO, Least Absolute Shrinkage and Selection Operator
- LDA, Linear Discriminant Analysis
- LR, Linear Regression
- LSTM, Long Short-Term Memory
- ML, Machine Learning
- MLP, Multiplayer Perceptron
- MLR, Multiple Linear Regression
- NLP, Natural Language Processing
- POAF, Postoperative Atrial Fibrillation
- RF, Random Forest
- RNN, Recurrent Neural Network
- SHAP, SHapley Additive exPlanations
- SVM, Support Vector Machine
- Systematic review
- WHO, World Health Organization
- kNN, k-nearest neighbors
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Affiliation(s)
| | - Rui João Pinto
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Pedro Miguel Silva
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Rui Pedro Duarte
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Francisco Alexandre Marinho
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Luís Pimenta
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - António Jorge Gouveia
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Norberto Jorge Gonçalves
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Paulo Jorge Coelho
- Polytechnic of Leiria, Leiria, Portugal
- Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra), Coimbra, Portugal
| | - Eftim Zdravevski
- Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, Macedonia
| | - Petre Lameski
- Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, Macedonia
| | - Valderi Leithardt
- VALORIZA, Research Center for Endogenous Resources Valorization, Instituto Politécnico de Portalegre, 7300-555 Portalegre, Portugal
- COPELABS, Universidade Lusófona de Humanidades e Tecnologias, Lisboa, Portugal
| | - Nuno M. Garcia
- Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
| | - Ivan Miguel Pires
- Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
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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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A Reparameterization Multifeature Fusion CNN for Arrhythmia Heartbeats Classification. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:7401175. [DOI: 10.1155/2022/7401175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 10/25/2022] [Accepted: 11/03/2022] [Indexed: 11/25/2022]
Abstract
Aiming at arrhythmia heartbeats classification, a novel multifeature fusion deep learning-based method is proposed. The stationary wavelet transforms (SWT) and RR interval features are firstly extracted. Based on the traditional one-dimensional convolutional neural network (1D-CNN), a parallel multibranch convolutional network is designed for training. The subband of SWT is input into the multiscale 1D-CNN separately. The output fused with RR interval features are fed to the fully connected layer for classification. To achieve the lightweight network while maintaining the powerful inference capability of the multibranch structure, the redundant branches of the network are removed by reparameterization. Experimental results and analysis show that it outperforms existing methods by many in arrhythmic heartbeat classification.
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Liotto C, Petrillo A, Santini S, Toscano G, Tufano V. A multiclass CNN cascade model for the clinical detection support of cardiac arrhythmia based on subject-exclusive ECG dataset. Biomed Eng Lett 2022; 12:433-444. [PMID: 36238367 PMCID: PMC9551136 DOI: 10.1007/s13534-022-00246-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/26/2022] [Accepted: 09/01/2022] [Indexed: 11/07/2022] Open
Abstract
The accurate analysis of Electrocardiogram waveform plays a crucial role for supporting cardiologist in detecting and diagnosing the heartbeat disorders. To improve their detection accuracy, this work is devoted to the design of a novel classification algorithm which is composed of a cascade of two convolutional neural network (CNN), i.e a Binary CNN allowing the detection of the arrhythmic heartbeat and a Multiclass CNN able to recognize the specific disorder. Moreover, by combining the cascade architecture solution with a rule-based data splitting, which leverages the subject-exclusive and balances among the classes criteria, it is possible predicting the health status of unseen patients. Numerical results, carried out considering Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database, disclose a classification accuracy of \documentclass[12pt]{minimal}
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\begin{document}$$96.2\%$$\end{document}96.2%. Finally, a cross-database performance evaluation and a comparison analysis w.r.t. the current state-of-art further disclose the effectiveness and the efficiency of the proposed solution, as well as its benefits in terms of patient health status prediction.
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Han Z, Huang Q, Zhang J, Huang C, Wang H, Huang X. GA-GWNN: Detecting anomalies of online learners by granular computing and graph wavelet convolutional neural network. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03337-2] [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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Fully learnable deep wavelet transform for unsupervised monitoring of high-frequency time series. Proc Natl Acad Sci U S A 2022; 119:2106598119. [PMID: 35181603 PMCID: PMC8872732 DOI: 10.1073/pnas.2106598119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/10/2022] [Indexed: 12/02/2022] Open
Abstract
Monitoring of industrial assets often relies on high-frequency (HF) signal measurements. One difficulty of dealing with such measurements in the industrial context is the conciliation between the high-frequency sampling and low-dimensional decision states (e.g., healthy/unhealthy), in a context where, very often, labels are not available. Here, we propose a fully unsupervised deep-learning framework for high-frequency time series that is able to extract meaningful and sparse representation of raw signals and is able to handle different lengths of time series flexibly, overcoming thereby several of the limitations of existing deep-learning approaches. The decomposition framework will be very useful for handling in an automatic manner high-frequency signals and is an important basis for future applications with HF data. High-frequency (HF) signals are ubiquitous in the industrial world and are of great use for monitoring of industrial assets. Most deep-learning tools are designed for inputs of fixed and/or very limited size and many successful applications of deep learning to the industrial context use as inputs extracted features, which are a manually and often arduously obtained compact representation of the original signal. In this paper, we propose a fully unsupervised deep-learning framework that is able to extract a meaningful and sparse representation of raw HF signals. We embed in our architecture important properties of the fast discrete wavelet transform (FDWT) such as 1) the cascade algorithm; 2) the conjugate quadrature filter property that links together the wavelet, the scaling, and transposed filter functions; and 3) the coefficient denoising. Using deep learning, we make this architecture fully learnable: Both the wavelet bases and the wavelet coefficient denoising become learnable. To achieve this objective, we propose an activation function that performs a learnable hard thresholding of the wavelet coefficients. With our framework, the denoising FDWT becomes a fully learnable unsupervised tool that does not require any type of pre- or postprocessing or any prior knowledge on wavelet transform. We demonstrate the benefits of embedding all these properties on three machine-learning tasks performed on open-source sound datasets. We perform an ablation study of the impact of each property on the performance of the architecture, achieve results well above baseline, and outperform other state-of-the-art methods.
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A wavelet-based neural network scheme for supervised and unsupervised learning. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05968-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Yang J, Cai W, Wang M. Premature beats detection based on a novel convolutional neural network. Physiol Meas 2021; 42. [PMID: 34167103 DOI: 10.1088/1361-6579/ac0e82] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 06/24/2021] [Indexed: 11/11/2022]
Abstract
Objective.Automatic detection of premature beats on long electrocardiogram (ECG) recordings is of great significance for clinical diagnosis. In this paper, we propose a novel deep learning model, the ECGDet, to detect premature beats, including premature ventricular contractions (PVCs) and supraventricular premature beats (SPBs) on single-lead long-term ECGs.Approach.The ECGDet is proposed based on a convolutional neural network and squeeze-and-excitation network. It outputs the probabilities that the ECG samples belong to a premature contraction. Non-max suppression was used to select the most appropriate locations for the premature beats. The ECGDet was trained and tested on the MIT-BIH arrhythmia database (MITDB) using a five-fold cross-validation approach. A novel loss calculation method was introduced in the model training process. Then it was tuned and further tested on the China Physiological Signal Challenge (2020) database (CPSCDB).Main results.The results showed that the average F1 value of PVC detection was 92.6%, while that of SPB detection was 72.2% on MITDB. The ECGDet bagged the 2nd place for PVC detection and ranked 7th place of SPB detection in the China Physiological Signal Challenge (2020).Significance.The proposed ECGDet can automatically detect premature heartbeats without manually extracting the features. This technique can be used for long-term ECG signal analysis and has potential for clinical applications.
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Affiliation(s)
- Jingying Yang
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, People's Republic of China
| | - Wenjie Cai
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, People's Republic of China
| | - Mingjie Wang
- Shanghai Key Laboratory of Bioactive Small Molecules, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, People's Republic of China
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