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Wang Y, Feng X, Zhong G, Yang C. A "two-step classification" machine learning method for non-invasive localization of premature ventricular contraction origins based on 12-lead ECG. J Interv Card Electrophysiol 2024; 67:457-470. [PMID: 37097585 DOI: 10.1007/s10840-023-01551-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 04/14/2023] [Indexed: 04/26/2023]
Abstract
BACKGROUND Premature ventricular contraction (PVC) is a type of cardiac arrhythmia that originates from ectopic pacemaker in the ventricles. The localization of the origin of PVC is essential for successful catheter ablation. However, most studies on non-invasive PVC localization focus on elaborate localization in specific regions of the ventricle. This study aims to propose a machine learning algorithm based on 12-lead electrocardiogram (ECG) data that can improve the accuracy of PVC localization in the whole ventricle. METHODS We collected 12-lead ECG data from 249 patients with spontaneous or pacing-induced PVCs. The ventricle was divided into 11 segments. In this paper, we propose a machine learning method consisting of two consecutive classification steps. In the first classification step, each PVC beat was labeled to one of the 11 ventricular segments using six features, including a newly proposed morphological feature called "Peak_index." Four machine learning methods were tested for comparative multi-classification performance and the best classifier result was kept to the next step. In the second classification step, a binary classifier was trained using a smaller combination of features to further differentiate segments that are easily confused. RESULTS The Peak_index as a proposed new classification feature combined with other features is suitable for whole ventricle classification by machine learning methods. The test accuracy of the first classification reached 75.87%. It is shown that a second classification for confusable categories can improve the classification results. After the second classification, the test accuracy reached 76.84%, and when a sample classified into adjacent segments was considered correct, the test "rank accuracy" was improved to 93.49%. The binary classification corrected 10% of the confused samples. CONCLUSION This paper proposes a "two-step classification" method to localize the origin of PVC beats into the 11 regions of the ventricle using non-invasive 12-lead ECG. It is expected to be a promising technique to be used in clinical settings to help guide ablation procedures.
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Affiliation(s)
- Yiwen Wang
- Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, 200433, People's Republic of China
| | - Xujian Feng
- Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, 200433, People's Republic of China
| | - Gaoyan Zhong
- Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, 200433, People's Republic of China
| | - Cuiwei Yang
- Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, 200433, People's Republic of China.
- Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, 200093, People's Republic of China.
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2
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Zhou S, Wang R, Seagren A, Emmert N, Warren JW, MacInnis PJ, AbdelWahab A, Sapp JL. Improving localization accuracy for non-invasive automated early left ventricular origin localization approach. Front Physiol 2023; 14:1183280. [PMID: 37435305 PMCID: PMC10330701 DOI: 10.3389/fphys.2023.1183280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 06/02/2023] [Indexed: 07/13/2023] Open
Abstract
Background: We previously developed a non-invasive approach to localize the site of early left ventricular activation origin in real time using 12-lead ECG, and to project the predicted site onto a generic LV endocardial surface using the smallest angle between two vectors algorithm (SA). Objectives: To improve the localization accuracy of the non-invasive approach by utilizing the K-nearest neighbors algorithm (KNN) to reduce projection errors. Methods: Two datasets were used. Dataset #1 had 1012 LV endocardial pacing sites with known coordinates on the generic LV surface and corresponding ECGs, while dataset #2 included 25 clinically-identified VT exit sites and corresponding ECGs. The non-invasive approach used "population" regression coefficients to predict the target coordinates of a pacing site or VT exit site from the initial 120-m QRS integrals of the pacing site/VT ECG. The predicted site coordinates were then projected onto the generic LV surface using either the KNN or SA projection algorithm. Results: The non-invasive approach using the KNN had a significantly lower mean localization error than the SA in both dataset #1 (9.4 vs. 12.5 mm, p < 0.05) and dataset #2 (7.2 vs. 9.5 mm, p < 0.05). The bootstrap method with 1,000 trials confirmed that using KNN had significantly higher predictive accuracy than using the SA in the bootstrap assessment with the left-out sample (p < 0.05). Conclusion: The KNN significantly reduces the projection error and improves the localization accuracy of the non-invasive approach, which shows promise as a tool to identify the site of origin of ventricular arrhythmia in non-invasive clinical modalities.
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Affiliation(s)
- Shijie Zhou
- The Department of Chemical, Paper and Biomedical Engineering, Miami University, Oxford, OH, United States
- The Department of Computer Science and Software Engineering, Miami University, Oxford, OH, United States
| | | | - Avery Seagren
- The Department of Chemical, Paper and Biomedical Engineering, Miami University, Oxford, OH, United States
| | - Noah Emmert
- The Department of Computer Science and Software Engineering, Miami University, Oxford, OH, United States
| | - James W. Warren
- The Department of Physiology and Biophysics, Dalhousie University, Halifax, NS, Canada
| | - Paul J. MacInnis
- The Department of Physiology and Biophysics, Dalhousie University, Halifax, NS, Canada
| | - Amir AbdelWahab
- Cardiology Division, Department of Medicine, Queen Elizabeth II Health Sciences Centre, Halifax, NS, Canada
| | - John L. Sapp
- Cardiology Division, Department of Medicine, Queen Elizabeth II Health Sciences Centre, Halifax, NS, Canada
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Zhang Z, Zhang Z, Zou C, Pei Z, Yang Z, Wu J, Sun S, Gu F. ECGNet: An Efficient Network for Detecting Premature Ventricular Complexes Based on ECG Images. IEEE Trans Biomed Eng 2023; 70:446-458. [PMID: 35881595 DOI: 10.1109/tbme.2022.3193906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
BACKGROUND Preoperative prediction of the origin site of premature ventricular complexes (PVCs) is critical for the success of operations. However, current methods are not efficient or accurate enough. In addition, among the proposed strategies, there are few good prediction methods for electrocardiogram (ECG) images combined with deep learning aspects. METHODS We propose ECGNet, a new neural network for the classification of 12-lead ECG images. In ECGNet, 609 ECG images from 310 patients who had undergone successful surgery in the Division of Cardiology, the First Affiliated Hospital of Soochow University, are utilized to construct the dataset. We adopt dense blocks, special convolution kernels and divergent paths to improve the performance of ECGNet. In addition, a new loss function is designed to address the sample imbalance situation, whose cause is the uneven distribution of cases themselves, which often occurs in the medical field. We also conduct extensive experiments in terms of network prediction accuracy to compare ECGNet with other networks, such as ResNet and DarkNet. RESULTS Our ECGNet achieves extremely high prediction accuracy (91.74%) and efficiency with very small datasets. Our newly proposed loss function can solve the problem of sample imbalance during the training process. CONCLUSION The proposed ECGNet can quickly and accurately realize the multiclassification of PVCs after training with little data. Our network has the potential to be helpful to doctors with a preoperative diagnosis of PVCs. We will continue to collect similar cases and perfect our network structure to further improve the accuracy of our network's prediction.
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Chang TY, Chen KW, Liu CM, Chang SL, Lin YJ, Lo LW, Hu YF, Chung FP, Lin CY, Kuo L, Chen SA. A High-Precision Deep Learning Algorithm to Localize Idiopathic Ventricular Arrhythmias. J Pers Med 2022; 12:jpm12050764. [PMID: 35629186 PMCID: PMC9145898 DOI: 10.3390/jpm12050764] [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: 03/28/2022] [Revised: 04/30/2022] [Accepted: 05/06/2022] [Indexed: 12/04/2022] Open
Abstract
Background: An accurate prediction of ventricular arrhythmia (VA) origins can optimize the strategy of ablation, and facilitate the procedure. Objective: This study aimed to develop a machine learning model from surface ECG to predict VA origins. Methods: We obtained 3628 waves of ventricular premature complex (VPC) from 731 patients. We chose to include all signal information from 12 ECG leads for model input. A model is composed of two groups of convolutional neural network (CNN) layers. We chose around 13% of all the data for model testing and 10% for validation. Results: In the first step, we trained a model for binary classification of VA source from the left or right side of the chamber with an area under the curve (AUC) of 0.963. With a threshold of 0.739, the sensitivity and specification are 90.7% and 92.3% for identifying left side VA. Then, we obtained the second model for predicting VA from the LV summit with AUC is 0.998. With a threshold of 0.739, the sensitivity and specificity are 100% and 98% for the LV summit. Conclusions: Our machine learning algorithm of surface ECG facilitates the localization of VPC, especially for the LV summit, which might optimize the ablation strategy.
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Affiliation(s)
- Ting-Yung Chang
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei 11217, Taiwan; (T.-Y.C.); (C.-M.L.); (Y.-J.L.); (L.-W.L.); (Y.-F.H.); (F.-P.C.); (C.-Y.L.); (L.K.); (S.-A.C.)
- Institute of Cardiovascular Research, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- Department of Nursing, National Taipei University of Nursing and Health Sciences, Taipei 112303, Taiwan
| | - Ke-Wei Chen
- Department of BioMedical Engineering, National Cheng Kung University, Tainan City 701401, Taiwan;
| | - Chih-Min Liu
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei 11217, Taiwan; (T.-Y.C.); (C.-M.L.); (Y.-J.L.); (L.-W.L.); (Y.-F.H.); (F.-P.C.); (C.-Y.L.); (L.K.); (S.-A.C.)
- Institute of Cardiovascular Research, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
| | - Shih-Lin Chang
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei 11217, Taiwan; (T.-Y.C.); (C.-M.L.); (Y.-J.L.); (L.-W.L.); (Y.-F.H.); (F.-P.C.); (C.-Y.L.); (L.K.); (S.-A.C.)
- Institute of Cardiovascular Research, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- Correspondence: ; Tel.: +886-2-7735-3832; Fax: +886-2-2872-4082
| | - Yenn-Jiang Lin
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei 11217, Taiwan; (T.-Y.C.); (C.-M.L.); (Y.-J.L.); (L.-W.L.); (Y.-F.H.); (F.-P.C.); (C.-Y.L.); (L.K.); (S.-A.C.)
- Institute of Cardiovascular Research, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
| | - Li-Wei Lo
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei 11217, Taiwan; (T.-Y.C.); (C.-M.L.); (Y.-J.L.); (L.-W.L.); (Y.-F.H.); (F.-P.C.); (C.-Y.L.); (L.K.); (S.-A.C.)
- Institute of Cardiovascular Research, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
| | - Yu-Feng Hu
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei 11217, Taiwan; (T.-Y.C.); (C.-M.L.); (Y.-J.L.); (L.-W.L.); (Y.-F.H.); (F.-P.C.); (C.-Y.L.); (L.K.); (S.-A.C.)
- Institute of Cardiovascular Research, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
| | - Fa-Po Chung
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei 11217, Taiwan; (T.-Y.C.); (C.-M.L.); (Y.-J.L.); (L.-W.L.); (Y.-F.H.); (F.-P.C.); (C.-Y.L.); (L.K.); (S.-A.C.)
- Institute of Cardiovascular Research, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
| | - Chin-Yu Lin
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei 11217, Taiwan; (T.-Y.C.); (C.-M.L.); (Y.-J.L.); (L.-W.L.); (Y.-F.H.); (F.-P.C.); (C.-Y.L.); (L.K.); (S.-A.C.)
- Institute of Cardiovascular Research, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
| | - Ling Kuo
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei 11217, Taiwan; (T.-Y.C.); (C.-M.L.); (Y.-J.L.); (L.-W.L.); (Y.-F.H.); (F.-P.C.); (C.-Y.L.); (L.K.); (S.-A.C.)
- Institute of Cardiovascular Research, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
| | - Shih-Ann Chen
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei 11217, Taiwan; (T.-Y.C.); (C.-M.L.); (Y.-J.L.); (L.-W.L.); (Y.-F.H.); (F.-P.C.); (C.-Y.L.); (L.K.); (S.-A.C.)
- Institute of Cardiovascular Research, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- Cardiovascular Center, Taichung Veterans General Hospital, Taichung 40705, Taiwan
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Zhou S, AbdelWahab A, Sapp JL, Sung E, Aronis KN, Warren JW, MacInnis PJ, Shah R, Horáček BM, Berger R, Tandri H, Trayanova NA, Chrispin J. Assessment of an ECG-Based System for Localizing Ventricular Arrhythmias in Patients With Structural Heart Disease. J Am Heart Assoc 2021; 10:e022217. [PMID: 34612085 PMCID: PMC8751877 DOI: 10.1161/jaha.121.022217] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Background We have previously developed an intraprocedural automatic arrhythmia‐origin localization (AAOL) system to identify idiopathic ventricular arrhythmia origins in real time using a 3‐lead ECG. The objective was to assess the localization accuracy of ventricular tachycardia (VT) exit and premature ventricular contraction (PVC) origin sites in patients with structural heart disease using the AAOL system. Methods and Results In retrospective and prospective case series studies, a total of 42 patients who underwent VT/PVC ablation in the setting of structural heart disease were recruited at 2 different centers. The AAOL system combines 120‐ms QRS integrals of 3 leads (III, V2, V6) with pace mapping to predict VT exit/PVC origin site and projects that site onto the patient‐specific electroanatomic mapping surface. VT exit/PVC origin sites were clinically identified by activation mapping and/or pace mapping. The localization error of the VT exit/PVC origin site was assessed by the distance between the clinically identified site and the estimated site. In the retrospective study of 19 patients with structural heart disease, the AAOL system achieved a mean localization accuracy of 6.5±2.6 mm for 25 induced VTs. In the prospective study with 23 patients, mean localization accuracy was 5.9±2.6 mm for 26 VT exit and PVC origin sites. There was no difference in mean localization error in epicardial sites compared with endocardial sites using the AAOL system (6.0 versus 5.8 mm, P=0.895). Conclusions The AAOL system achieved accurate localization of VT exit/PVC origin sites in patients with structural heart disease; its performance is superior to current systems, and thus, it promises to have potential clinical utility.
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Affiliation(s)
- Shijie Zhou
- Alliance for Cardiovascular Diagnostic and Treatment Innovation Johns Hopkins University Baltimore MD
| | - Amir AbdelWahab
- Department of Medicine Queen Elizabeth II Health Sciences Centre Halifax NS Canada
| | - John L Sapp
- Department of Medicine Queen Elizabeth II Health Sciences Centre Halifax NS Canada.,Department of Physiology and Biophysics Dalhousie University Halifax NS Canada
| | - Eric Sung
- Alliance for Cardiovascular Diagnostic and Treatment Innovation Johns Hopkins University Baltimore MD.,Department of Biomedical Engineering Johns Hopkins University Baltimore MD
| | - Konstantinos N Aronis
- Division of Cardiology Department of Medicine Section of Cardiac Electrophysiology Johns Hopkins Hospital Baltimore MD.,Department of Biomedical Engineering Johns Hopkins University Baltimore MD
| | - James W Warren
- Department of Physiology and Biophysics Dalhousie University Halifax NS Canada
| | - Paul J MacInnis
- Department of Physiology and Biophysics Dalhousie University Halifax NS Canada
| | - Rushil Shah
- Division of Cardiology Department of Medicine Section of Cardiac Electrophysiology Johns Hopkins Hospital Baltimore MD
| | - B Milan Horáček
- School of Biomedical Engineering Dalhousie University Halifax NS Canada
| | - Ronald Berger
- Alliance for Cardiovascular Diagnostic and Treatment Innovation Johns Hopkins University Baltimore MD.,Division of Cardiology Department of Medicine Section of Cardiac Electrophysiology Johns Hopkins Hospital Baltimore MD
| | - Harikrishna Tandri
- Alliance for Cardiovascular Diagnostic and Treatment Innovation Johns Hopkins University Baltimore MD.,Division of Cardiology Department of Medicine Section of Cardiac Electrophysiology Johns Hopkins Hospital Baltimore MD
| | - Natalia A Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation Johns Hopkins University Baltimore MD.,Department of Biomedical Engineering Johns Hopkins University Baltimore MD
| | - Jonathan Chrispin
- Alliance for Cardiovascular Diagnostic and Treatment Innovation Johns Hopkins University Baltimore MD.,Division of Cardiology Department of Medicine Section of Cardiac Electrophysiology Johns Hopkins Hospital Baltimore MD
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Li G, Tan Z, Xu W, Xu F, Wang L, Chen J, Wu K. A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification. BMC Med Inform Decis Mak 2021; 21:99. [PMID: 34330266 PMCID: PMC8322832 DOI: 10.1186/s12911-021-01453-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 02/22/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND As proven to reflect the work state of heart and physiological situation objectively, electrocardiogram (ECG) is widely used in the assessment of human health, especially the diagnosis of heart disease. The accuracy and reliability of abnormal ECG (AECG) decision depend to a large extent on the feature extraction. However, it is often uneasy or even impossible to obtain accurate features, as the detection process of ECG is easily disturbed by the external environment. And AECG got many species and great variation. What's more, the ECG result obtained after a long time past, which can not reach the purpose of early warning or real-time disease diagnosis. Therefore, developing an intelligent classification model with an accurate feature extraction method to identify AECG is of quite significance. This study aimed to explore an accurate feature extraction method of ECG and establish a suitable model for identifying AECG and the diagnosis of heart disease. METHODS In this research, the wavelet combined with four operations and adaptive threshold methods were applied to filter the ECG and extract its feature waves first. Then, a BP neural network (BPNN) intelligent model and a particle swarm optimization (PSO) improved BPNN (PSO-BPNN) intelligent model based on MIT-BIH open database was established to identify ECG. To reduce the complexity of the model, the principal component analysis (PCA) was used to minimize the feature dimension. RESULTS Wavelet transforms combined four operations and adaptive threshold methods were capable of ECG filtering and feature extraction. PCA can significantly deduce the modeling feature dimension to minimize the complexity and save classification time. The PSO-BPNN intelligent model was suitable for identifying five types of ECG and showed better effects while comparing it with the BPNN model. CONCLUSION In summary, it was further concluded that the PSO-BPNN intelligent model would be a suitable way to identify AECG and provide a tool for the diagnosis of heart disease.
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Affiliation(s)
- Guixiang Li
- National Engineering Research Center for Healthcare Devices, Guangdong Key Lab of Medical Electronic Instruments and Polymer Material Products, Guangdong Institute of Medical Instruments, Institute of Medicine and Health, Guangdong Academy of Sciences, Guangzhou, 510500, China.,Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, 510500, China
| | - Zhongwei Tan
- National Engineering Research Center for Healthcare Devices, Guangdong Key Lab of Medical Electronic Instruments and Polymer Material Products, Guangdong Institute of Medical Instruments, Institute of Medicine and Health, Guangdong Academy of Sciences, Guangzhou, 510500, China
| | - Weikang Xu
- National Engineering Research Center for Healthcare Devices, Guangdong Key Lab of Medical Electronic Instruments and Polymer Material Products, Guangdong Institute of Medical Instruments, Institute of Medicine and Health, Guangdong Academy of Sciences, Guangzhou, 510500, China
| | - Fei Xu
- National Engineering Research Center for Healthcare Devices, Guangdong Key Lab of Medical Electronic Instruments and Polymer Material Products, Guangdong Institute of Medical Instruments, Institute of Medicine and Health, Guangdong Academy of Sciences, Guangzhou, 510500, China
| | - Lei Wang
- Department of Artificial Intelligence, College of Information and Communication Engineering, Hainan University, Haikou, 570228, China.
| | - Jun Chen
- National Engineering Research Center for Healthcare Devices, Guangdong Key Lab of Medical Electronic Instruments and Polymer Material Products, Guangdong Institute of Medical Instruments, Institute of Medicine and Health, Guangdong Academy of Sciences, Guangzhou, 510500, China. .,Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, 510500, China.
| | - Kai Wu
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, 510006, China. .,Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, 510500, China. .,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China. .,The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, 510370, China. .,National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, 510006, China. .,Key Laboratory of Biomedical Engineering of Guangdong Province, South China University of Technology, Guangzhou, 510006, China. .,Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, 980-8575, Japan.
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An Intelligent Heartbeat Classification System Based on Attributable Features with AdaBoost+Random Forest Algorithm. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:9913127. [PMID: 34336169 PMCID: PMC8289583 DOI: 10.1155/2021/9913127] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 05/20/2021] [Accepted: 05/27/2021] [Indexed: 12/02/2022]
Abstract
Arrhythmia is a common cardiovascular disease that can threaten human life. In order to assist doctors in accurately diagnosing arrhythmia, an intelligent heartbeat classification system based on the selected optimal feature sets and AdaBoost + Random Forest model is developed. This system can acquire ECG signals through the Holter and transmit them to the cloud platform for preprocessing and feature extraction, and the features are input into AdaBoost + Random Forest for heartbeat classification. The analysis results are output in the form of reports. In this system, by comparing and analyzing the classification accuracy of different feature sets and classifiers, the optimal classification algorithm is obtained and applied to the system. The algorithm accuracy of the system is tested based on the MIT-BIH data set. The result shows that AdaBoost + Random Forest achieved 99.11% accuracy with optimal feature sets. The intelligent heartbeat classification system based on this algorithm has also achieved good results on clinical data.
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8
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Huang Y, Li H, Yu X. A multiview feature fusion model for heartbeat classification. Physiol Meas 2021; 42. [PMID: 33984841 DOI: 10.1088/1361-6579/ac010f] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 05/13/2021] [Indexed: 11/11/2022]
Abstract
Objective.An electrocardiogram (ECG) is one of the most common means to diagnose arrhythmia according to different waveforms clinically. Although there are advanced classification methods such as deep learning, the single view feature cannot meet the demand of classification accuracy for new individuals. To this end, a classification model based on multiview fusion was proposed.Approach.First, handcrafted view features were extracted from heartbeats and then deep view features were obtained from the deep learning model. The features of two different perspectives were fused in the fully connected layer, and the random forest classifier was used instead of the Softmax classifier for classification. Notably, Bayesian optimization was utilized in the hyper-parameter tuning of the classifier. The proposed method employed the MIT-BIH database to classify five classes: normal heartbeat (N), left bundle branch block heartbeat (LB), right bundle branch block heartbeat (RB), atrial premature contraction (APC) and premature ventricular contraction (PVC).Main results.The experimental results achieved a higher average accuracy of 98.93%, average precision of 96.92%, average sensitivity of 96.46%, and average specificity of 99.33% in five types of heartbeat classification for inter-patient.Significance.The proposed framework improves the performance of ECG detection for new individuals. And it provides an feasible algorithmic model for single-lead wearable devices with multiview fusion.
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Affiliation(s)
- Youhe Huang
- College of Information Sciences and Engineering, Northeastern University, Shenyang, People's Republic of China
| | - Hongru Li
- College of Information Sciences and Engineering, Northeastern University, Shenyang, People's Republic of China
| | - Xia Yu
- College of Information Sciences and Engineering, Northeastern University, Shenyang, People's Republic of China
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