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Liu LR, Huang MY, Huang ST, Kung LC, Lee CH, Yao WT, Tsai MF, Hsu CH, Chu YC, Hung FH, Chiu HW. An Arrhythmia classification approach via deep learning using single-lead ECG without QRS wave detection. Heliyon 2024; 10:e27200. [PMID: 38486759 PMCID: PMC10937691 DOI: 10.1016/j.heliyon.2024.e27200] [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: 01/07/2024] [Revised: 02/18/2024] [Accepted: 02/26/2024] [Indexed: 03/17/2024] Open
Abstract
Arrhythmia, a frequently encountered and life-threatening cardiac disorder, can manifest as a transient or isolated event. Traditional automatic arrhythmia detection methods have predominantly relied on QRS-wave signal detection. Contemporary research has focused on the utilization of wearable devices for continuous monitoring of heart rates and rhythms through single-lead electrocardiogram (ECG), which holds the potential to promptly detect arrhythmias. However, in this study, we employed a convolutional neural network (CNN) to classify distinct arrhythmias without QRS wave detection step. The ECG data utilized in this study were sourced from the publicly accessible PhysioNet databases. Taking into account the impact of the duration of ECG signal on accuracy, this study trained one-dimensional CNN models with 5-s and 10-s segments, respectively, and compared their results. In the results, the CNN model exhibited the capability to differentiate between Normal Sinus Rhythm (NSR) and various arrhythmias, including Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Wolff-Parkinson-White syndrome (WPW), Ventricular Fibrillation (VF), Ventricular Tachycardia (VT), Ventricular Flutter (VFL), Mobitz II AV Block (MII), and Sinus Bradycardia (SB). Both 10-s and 5-s ECG segments exhibited comparable results, with an average classification accuracy of 97.31%. It reveals the feasibility of utilizing even shorter 5-s recordings for detecting arrhythmias in everyday scenarios. Detecting arrhythmias with a single lead aligns well with the practicality of wearable devices for daily use, and shorter detection times also align with their clinical utility in emergency situations.
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Affiliation(s)
- Liong-Rung Liu
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
| | - Ming-Yuan Huang
- Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
| | - Shu-Tien Huang
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
| | - Lu-Chih Kung
- Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
| | - Chao-hsiung Lee
- Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
| | - Wen-Teng Yao
- Division of Plastic Surgery, Department of Surgery, Mackay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
| | - Ming-Feng Tsai
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Division of Plastic Surgery, Department of Surgery, Mackay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
| | - Cheng-Hung Hsu
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Yu-Chang Chu
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Fei-Hung Hung
- Health Data Analytics and Statistics Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan
| | - Hung-Wen Chiu
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan
- Bioinformatics Data Science Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
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Zhang B, Lu G, Qiu P, Gui X, Shi Y. Advancing Federated Learning through Verifiable Computations and Homomorphic Encryption. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1550. [PMID: 37998241 PMCID: PMC10670442 DOI: 10.3390/e25111550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/01/2023] [Accepted: 11/04/2023] [Indexed: 11/25/2023]
Abstract
Federated learning, as one of the three main technical routes for privacy computing, has been widely studied and applied in both academia and industry. However, malicious nodes may tamper with the algorithm execution process or submit false learning results, which directly affects the performance of federated learning. In addition, learning nodes can easily obtain the global model. In practical applications, we would like to obtain the federated learning results only by the demand side. Unfortunately, no discussion on protecting the privacy of the global model is found in the existing research. As emerging cryptographic tools, the zero-knowledge virtual machine (ZKVM) and homomorphic encryption provide new ideas for the design of federated learning frameworks. We have introduced ZKVM for the first time, creating learning nodes as local computing provers. This provides execution integrity proofs for multi-class machine learning algorithms. Meanwhile, we discuss how to generate verifiable proofs for large-scale machine learning tasks under resource constraints. In addition, we implement the fully homomorphic encryption (FHE) scheme in ZKVM. We encrypt the model weights so that the federated learning nodes always collaborate in the ciphertext space. The real results can be obtained only after the demand side decrypts them using the private key. The innovativeness of this paper is demonstrated in the following aspects: 1. We introduce the ZKVM for the first time, which achieves zero-knowledge proofs (ZKP) for machine learning tasks with multiple classes and arbitrary scales. 2. We encrypt the global model, which protects the model privacy during local computation and transmission. 3. We propose and implement a new federated learning framework. We measure the verification costs under different federated learning rounds on the IRIS dataset. Despite the impact of homomorphic encryption on computational accuracy, the framework proposed in this paper achieves a satisfactory 90% model accuracy. Our framework is highly secure and is expected to further improve the overall efficiency as cryptographic tools continue to evolve.
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Affiliation(s)
| | | | | | | | - Yang Shi
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; (B.Z.); (G.L.); (P.Q.); (X.G.)
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Ramkumar M, Alagarsamy M, Balakumar A, Pradeep S. Ensemble classifier fostered detection of arrhythmia using ECG data. Med Biol Eng Comput 2023; 61:2453-2466. [PMID: 37145258 DOI: 10.1007/s11517-023-02839-6] [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: 08/27/2022] [Accepted: 04/13/2023] [Indexed: 05/06/2023]
Abstract
Electrocardiogram (ECG) is a non-invasive medical tool that divulges the rhythm and function of the human heart. This is broadly employed in heart disease detection including arrhythmia. Arrhythmia is a general term for abnormal heart rhythms that can be identified and classified into many categories. Automatic ECG analysis is provided by arrhythmia categorization in cardiac patient monitoring systems. It aids cardiologists to diagnose the ECG signal. In this work, an Ensemble classifier is proposed for accurate arrhythmia detection using ECG Signal. Input data are taken from the MIT-BIH arrhythmia dataset. Then the input data was pre-processed using Python in Jupyter Notebook which run the code in an isolated manner and was able to keep code, formula, comments, and images. Then, Residual Exemplars Local Binary Pattern is applied for extracting statistical features. The extracted features are given to ensemble classifiers, like Support vector machines (SVM), Naive Bayes (NB), and random forest (RF) for classifying the arrhythmia as normal (N), supraventricular ectopic beat (S), ventricular ectopic beat (V), fusion beat (F), and unknown beat (Q). The proposed AD-Ensemble SVM-NB-RF method is implemented in Python. The proposed AD-Ensemble SVM-NB-RF method is 44.57%, 52.41%, and 29.49% higher accuracy; 2.01%, 3.33%, and 3.19% higher area under the curve (AUC); and 21.52%, 23.05%, and 12.68% better F-Measure compared with existing models, like multi-model depending on the ensemble of deep learning for ECG heartbeats arrhythmia categorization (AD-Ensemble CNN-LSTM-RRHOS), ECG signal categorization utilizing VGGNet: a neural network based classification method (AD-Ensemble CNN-LSTM) and higher performance arrhythmic heartbeat categorization utilizing ensemble learning along PSD based feature extraction method (AD-Ensemble MLP-NB-RF).
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Affiliation(s)
- M Ramkumar
- Department of Electronics and Communication Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, 641-008, Tamil Nadu, India.
| | - Manjunathan Alagarsamy
- Department of Electronics and Communication Engineering, K.Ramakrishnan College of Technology, Trichy, 621112, Tamil Nadu, India
| | - A Balakumar
- Department of Electronics and Communication Engineering, K.Ramakrishnan College of Engineering, Trichy, 621112, Tamil Nadu, India
| | - S Pradeep
- Department of Electronics and Communication Engineering, K.S.Rangasamy College of Technology, Tiruchengode, 637215, Tamil Nadu, India
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Neri L, Oberdier MT, van Abeelen KCJ, Menghini L, Tumarkin E, Tripathi H, Jaipalli S, Orro A, Paolocci N, Gallelli I, Dall’Olio M, Beker A, Carrick RT, Borghi C, Halperin HR. Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:4805. [PMID: 37430719 PMCID: PMC10223364 DOI: 10.3390/s23104805] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/09/2023] [Accepted: 05/12/2023] [Indexed: 07/12/2023]
Abstract
Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data.
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Affiliation(s)
- Luca Neri
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Matt T. Oberdier
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Kirsten C. J. van Abeelen
- Department of Informatics, Systems, and Communication, University of Milano-Bicocca, 20126 Milan, Italy
- Department of Internal Medicine, Radboud University Medical Center, 6525 AJ Nijmegen, The Netherlands
| | - Luca Menghini
- Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto, Italy
| | - Ethan Tumarkin
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Hemantkumar Tripathi
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Sujai Jaipalli
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Alessandro Orro
- Institute of Biomedical Technologies, National Research Council, 20054 Segrate, Italy
| | - Nazareno Paolocci
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Ilaria Gallelli
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Massimo Dall’Olio
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Amir Beker
- AccYouRate Group S.p.A., 67100 L’Aquila, Italy
| | - Richard T. Carrick
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Claudio Borghi
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Henry R. Halperin
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Radiology, Johns Hopkins University, Baltimore, MD 21205, USA
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Patel S, Wang M, Guo J, Smith G, Chen C. A Study of R-R Interval Transition Matrix Features for Machine Learning Algorithms in AFib Detection. SENSORS (BASEL, SWITZERLAND) 2023; 23:3700. [PMID: 37050761 PMCID: PMC10099376 DOI: 10.3390/s23073700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/14/2023] [Accepted: 03/28/2023] [Indexed: 06/19/2023]
Abstract
Atrial Fibrillation (AFib) is a heart condition that occurs when electrophysiological malformations within heart tissues cause the atria to lose coordination with the ventricles, resulting in "irregularly irregular" heartbeats. Because symptoms are subtle and unpredictable, AFib diagnosis is often difficult or delayed. One possible solution is to build a system which predicts AFib based on the variability of R-R intervals (the distances between two R-peaks). This research aims to incorporate the transition matrix as a novel measure of R-R variability, while combining three segmentation schemes and two feature importance measures to systematically analyze the significance of individual features. The MIT-BIH dataset was first divided into three segmentation schemes, consisting of 5-s, 10-s, and 25-s subsets. In total, 21 various features, including the transition matrix features, were extracted from these subsets and used for the training of 11 machine learning classifiers. Next, permutation importance and tree-based feature importance calculations determined the most predictive features for each model. In summary, with Leave-One-Person-Out Cross Validation, classifiers under the 25-s segmentation scheme produced the best accuracies; specifically, Gradient Boosting (96.08%), Light Gradient Boosting (96.11%), and Extreme Gradient Boosting (96.30%). Among eleven classifiers, the three gradient boosting models and Random Forest exhibited the highest overall performance across all segmentation schemes. Moreover, the permutation and tree-based importance results demonstrated that the transition matrix features were most significant with longer subset lengths.
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Affiliation(s)
- Sahil Patel
- John T. Hoggard High School, Wilmington, NC 28403, USA
- Department of Mathematics and Statistics, University of North Carolina Wilmington, Wilmington, NC 28403, USA
| | - Maximilian Wang
- Department of Mathematics and Statistics, University of North Carolina Wilmington, Wilmington, NC 28403, USA
- Isaac M. Bear Early College High School, Wilmington, NC 28403, USA
| | - Justin Guo
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Georgia Smith
- Department of Biostatistics, University of Colorado’s Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Cuixian Chen
- Department of Mathematics and Statistics, University of North Carolina Wilmington, Wilmington, NC 28403, USA
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6
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Bui TH, Hoang VM, Pham MT. Automatic varied-length ECG classification using a lightweight DenseNet model. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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7
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Ma H, Ding F, Wang Y. A novel multi-innovation gradient support vector machine regression method. ISA TRANSACTIONS 2022; 130:343-359. [PMID: 35354538 DOI: 10.1016/j.isatra.2022.03.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 03/06/2022] [Accepted: 03/06/2022] [Indexed: 06/14/2023]
Abstract
For the regression problem of support vector machine, the solution processes of the most existing methods use offline datasets, which cannot be realized online. For this problem, this paper presents a new online approach to identify these unknown parameters contained in the support vector machine. A new cost function is constructed by substituting the error term into the standard cost function, which is different from the standard support vector machine, and the gradient descent approach is then used to minimize the newly created loss function, thus proposing a stochastic gradient support vector machine algorithm to estimate the unknown parameters based on the recursive identification methods. Furthermore, to advance the property of the stochastic gradient support vector machine algorithm, a moving data window is used to widen the scalar information into a fixed-length innovation vector, thereby increasing the amount of information used in the parameter estimation based on the multi-innovation identification theory. In addition, the forgetting factor is brought into the proposed algorithms, and the corresponding forgetting factor recursive algorithms are derived. These methods are recursive identification methods, which may be implemented online and are more efficient in terms of computing. Finally, utilizing the MatLab platform, the validity and usefulness of the explored methodologies are proven using several numerical simulation examples.
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Affiliation(s)
- Hao Ma
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China.
| | - Feng Ding
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China; School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, PR China.
| | - Yan Wang
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China.
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Zhou Y, Ding F, Yang E. Two-stage extended recursive gradient algorithm for locally linear RBF-based autoregressive models with colored noises. ISA TRANSACTIONS 2022; 129:284-294. [PMID: 35219454 DOI: 10.1016/j.isatra.2022.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 02/06/2022] [Accepted: 02/06/2022] [Indexed: 06/14/2023]
Abstract
A novel parameter identification method for locally linear radial basis function-based autoregressive models in presence of colored noises is proposed in this paper. Taking advantage of the global nonlinear and local linear structural characteristics of the models, two dynamical criterion functions are constructed based on the separated parameters to realize the dynamical acquisition and utilization of the entire process data. Two recursive gradient sub-algorithms are derived for estimating the separated parameters by using the nonlinear gradient optimization. To coordinate the associated variables existing in the sub-algorithms and to estimate the unmeasurable noise terms, we combine the sub-algorithms and propose a two-stage extended recursive gradient (2S-ERG) algorithm. In addition, an extended recursive gradient algorithm is given as a comparison. The feasibility of the 2S-ERG algorithm is validated by numerical simulations.
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Affiliation(s)
- Yihong Zhou
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China.
| | - Feng Ding
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China; College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, PR China.
| | - Erfu Yang
- Department of Design, Manufacturing and Engineering Management, University of Strathclyde, Glasgow, G1 1XJ, Scotland, United Kingdom
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