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Jing J, Zhang J, Liu A, Gao M, Qian R, Chen X. ECG-Based Multiclass Arrhythmia Classification Using Beat-Level Fusion Network. JOURNAL OF HEALTHCARE ENGINEERING 2023; 2023:1755121. [PMID: 38078159 PMCID: PMC10700922 DOI: 10.1155/2023/1755121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 08/08/2022] [Accepted: 11/24/2022] [Indexed: 12/18/2023]
Abstract
Cardiovascular disease (CVD) is one of the most severe diseases threatening human life. Electrocardiogram (ECG) is an effective way to detect CVD. In recent years, many methods have been proposed to detect arrhythmia using 12-lead ECG. In particular, deep learning methods have been proven to be effective and have been widely used. The attention mechanism has attracted extensive attention in many fields in a series of deep learning methods. Off-the-shelf solutions based on deep learning and attention mechanism for ECG classification mostly give weights to time points. None of the existing methods were considered using the attention mechanism dealing with ECG signals at the level of heartbeats. In this paper, we propose a beat-level fusion net (BLF-Net) for multiclass arrhythmia classification by assigning weights at the heartbeat level, according to the contribution of the heartbeat to diagnostic results. This algorithm consists of three steps: (1) segmenting the long ECG signal into short beats; (2) using a neural network to extract features from heartbeats; and (3) assigning weights to features extracted from heartbeats using an attention mechanism. We test our algorithm on the PTB-XL database and have superiority over state-of-the-art performance on six classification tasks. Besides, the principle of this architecture is clarified by visualizing the weight of the attention mechanism. The proposed BLF-Net is shown to be useful and automatically provides an effective network structure for arrhythmia classification, which is capable of aiding cardiologists in arrhythmia diagnosis.
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Affiliation(s)
- Junyuan Jing
- School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China
| | - Jing Zhang
- School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China
| | - Aiping Liu
- School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China
| | - Min Gao
- Department of Electrocardiogram, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, Anhui, China
| | - Ruobing Qian
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, Anhui, China
| | - Xun Chen
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, Anhui, China
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Multi-classification of arrhythmias using ResNet with CBAM on CWGAN-GP augmented ECG Gramian Angular Summation Field. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103684] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Karri M, Annavarapu CSR, Pedapenki KK. A Real-Time Cardiac Arrhythmia Classification Using Hybrid Combination of Delta Modulation, 1D-CNN and Blended LSTM. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10949-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Chandrasekar A, Shekar DD, Hiremath AC, Chemmangat K. Detection of arrhythmia from electrocardiogram signals using a novel gaussian assisted signal smoothing and pattern recognition. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103469] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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Arrhythmia detection and classification using ECG and PPG techniques: a review. Phys Eng Sci Med 2021; 44:1027-1048. [PMID: 34727361 DOI: 10.1007/s13246-021-01072-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 10/25/2021] [Indexed: 12/26/2022]
Abstract
Electrocardiogram (ECG) and photoplethysmograph (PPG) are non-invasive techniques that provide electrical and hemodynamic information of the heart, respectively. This information is advantageous in the diagnosis of various cardiac abnormalities. Arrhythmia is the most common cardiovascular disease, manifested as single or multiple irregular heartbeats. However, due to the continuous manual observation, it becomes troublesome for experts sometimes to identify the paroxysmal nature of arrhythmia correctly. Moreover, due to advancements in technology, there is an inclination towards wearable sensors which monitor such patients continuously. Thus, there is a need for automatic detection techniques for the identification of arrhythmia. In the presented work, ECG and PPG-based state-of-the-art methods have been described, including preprocessing, feature extraction, and classification techniques for the detection of various arrhythmias. Additionally, this review exhibits various wearable sensors used in the literature and public databases available for the evaluation of results. The study also highlights the limitations of the current techniques and pragmatic solutions to improvise the ongoing effort.
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Athanasios T, Yiannis K, Georgios C, Georgios P, Stavros G, Ioannis S, Vasilios T, Anastasios K. The use of an artificial neural network in the evaluation of the extracorporeal shockwave lithotripsy as a treatment of choice for urinary lithiasis. Asian J Urol 2021; 9:132-138. [PMID: 35509481 PMCID: PMC9051353 DOI: 10.1016/j.ajur.2021.09.005] [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: 11/26/2020] [Revised: 03/28/2021] [Accepted: 06/15/2021] [Indexed: 12/04/2022] Open
Abstract
Objective Artificial neural networks (ANNs) are widely applied in medicine, since they substantially increase the sensitivity and specificity of the diagnosis, classification, and the prognosis of a medical condition. In this study, we constructed an ANN to evaluate several parameters of extracorporeal shockwave lithotripsy (ESWL), such as the outcome and safety of the procedure. Methods Patients with urinary lithiasis suitable for ESWL treatment were enrolled. An ANN was designed using MATLAB. Medical data were collected from all patients and 12 nodes were used as inputs. Conventional statistical analysis was also performed. Results Finally, 716 patients were included in our study. Univariate analysis revealed that diabetes and hydronephrosis were positively correlated with ESWL complications. Regarding efficacy, univariate analysis revealed that stone location, stone size, the number and density of shockwaves delivered, and the presence of a stent in the ureter were independent factors of the ESWL outcome. This was further confirmed when adjusted for sex and age in a multivariate analysis. The performance of the ANN at the end of the training state reached 98.72%. The four basic ratios (sensitivity, specificity, positive predictive value, and negative predictive value) were calculated for both training and evaluation data sets. The performance of the ANN at the end of the evaluation state was 81.43%. Conclusion Our ANN achieved high score in predicting the outcome and the side effects of the ESWL treatment for urinary stones.
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Zhang J, Liang D, Liu A, Gao M, Chen X, Zhang X, Chen X. MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2021; 9:1900211. [PMID: 33777544 PMCID: PMC7963211 DOI: 10.1109/jtehm.2021.3064675] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 01/24/2021] [Accepted: 03/04/2021] [Indexed: 11/10/2022]
Abstract
Automatic arrhythmia detection using 12-lead electrocardiogram (ECG) signal plays a critical role in early prevention and diagnosis of cardiovascular diseases. In the previous studies on automatic arrhythmia detection, most methods concatenated 12 leads of ECG into a matrix, and then input the matrix to a variety of feature extractors or deep neural networks for extracting useful information. Under such frameworks, these methods had the ability to extract comprehensive features (known as integrity) of 12-lead ECG since the information of each lead interacts with each other during training. However, the diverse lead-specific features (known as diversity) among 12 leads were neglected, causing inadequate information learning for 12-lead ECG. To maximize the information learning of multi-lead ECG, the information fusion of comprehensive features with integrity and lead-specific features with diversity should be taken into account. In this paper, we propose a novel Multi-Lead-Branch Fusion Network (MLBF-Net) architecture for arrhythmia classification by integrating multi-loss optimization to jointly learning diversity and integrity of multi-lead ECG. MLBF-Net is composed of three components: 1) multiple lead-specific branches for learning the diversity of multi-lead ECG; 2) cross-lead features fusion by concatenating the output feature maps of all branches for learning the integrity of multi-lead ECG; 3) multi-loss co-optimization for all the individual branches and the concatenated network. We demonstrate our MLBF-Net on China Physiological Signal Challenge 2018 which is an open 12-lead ECG dataset. The experimental results show that MLBF-Net obtains an average [Formula: see text] score of 0.855, reaching the highest arrhythmia classification performance. The proposed method provides a promising solution for multi-lead ECG analysis from an information fusion perspective.
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Affiliation(s)
- Jing Zhang
- Department of Electronic Science and TechnologyUniversity of Science and Technology of ChinaHefei230027China
| | - Deng Liang
- Department of Electronic Science and TechnologyUniversity of Science and Technology of ChinaHefei230027China
| | - Aiping Liu
- Department of Electronic Science and TechnologyUniversity of Science and Technology of ChinaHefei230027China
| | - Min Gao
- Department of ElectrocardiogramThe First Affiliated Hospital of USTC, Division of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefei230001China
| | - Xiang Chen
- Department of Electronic Science and TechnologyUniversity of Science and Technology of ChinaHefei230027China
| | - Xu Zhang
- Department of Electronic Science and TechnologyUniversity of Science and Technology of ChinaHefei230027China
| | - Xun Chen
- Department of Electronic Engineering and Information ScienceUniversity of Science and Technology of ChinaHefei230027China
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Zhang J, Liu A, Gao M, Chen X, Zhang X, Chen X. ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network. Artif Intell Med 2020; 106:101856. [DOI: 10.1016/j.artmed.2020.101856] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 03/05/2020] [Accepted: 04/02/2020] [Indexed: 01/16/2023]
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Kaplan Berkaya S, Uysal AK, Sora Gunal E, Ergin S, Gunal S, Gulmezoglu MB. A survey on ECG analysis. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.03.003] [Citation(s) in RCA: 197] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Mert A. ECG feature extraction based on the bandwidth properties of variational mode decomposition. Physiol Meas 2016; 37:530-43. [PMID: 26987295 DOI: 10.1088/0967-3334/37/4/530] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
It is a difficult process to detect abnormal heart beats, known as arrhythmia, in long-term ECG recording. Thus, computer-aided diagnosis systems have become a supportive tool for helping physicians improve the diagnostic accuracy of heartbeat detection. This paper explores the bandwidth properties of the modes obtained using variational mode decomposition (VMD) to classify arrhythmia electrocardiogram (ECG) beats. VMD is an enhanced version of the empirical mode decomposition (EMD) algorithm for analyzing non-linear and non-stationary signals. It decomposes the signal into a set of band-limited oscillations called modes. ECG signals from the MIT-BIH arrhythmia database are decomposed using VMD, and the amplitude modulation bandwidth B AM, the frequency modulation bandwidth B FM and the total bandwidth B of the modes are used as feature vectors to detect heartbeats such as normal (N), premature ventricular contraction (V), left bundle branch block (L), right bundle branch block (R), paced beat (P) and atrial premature beat (A). Bandwidth estimations based on the instantaneous frequency (IF) and amplitude (IA) spectra of the modes indicate that the proposed VMD-based features have sufficient class discrimination capability regarding ECG beats. Moreover, the extracted features using the bandwidths (B AM, B FM and B) of four modes are used to evaluate the diagnostic accuracy rates of several classifiers such as the k-nearest neighbor classifier (k-NN), the decision tree (DT), the artificial neural network (ANN), the bagged decision tree (BDT), the AdaBoost decision tree (ABDT) and random sub-spaced k-NN (RSNN) for N, R, L, V, P, and A beats. The performance of the proposed VMD-based feature extraction with a BDT classifier has accuracy rates of 99.06%, 99.00%, 99.40%, 99.51%, 98.72%, 98.71%, and 99.02% for overall, N-, R-, L-, V-, P-, and A-type ECG beats, respectively.
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Affiliation(s)
- Ahmet Mert
- Department of Electrical and Electronics Engineering, Piri Reis University, Tuzla, 34940 Istanbul, Turkey
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Kumar SU, Inbarani HH. Neighborhood rough set based ECG signal classification for diagnosis of cardiac diseases. Soft comput 2016. [DOI: 10.1007/s00500-016-2080-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Yeh BC, Lin WP. Using a Calculated Pulse Rate with an Artificial Neural Network to Detect Irregular Interbeats. J Med Syst 2015; 40:48. [PMID: 26643078 DOI: 10.1007/s10916-015-0409-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2015] [Accepted: 11/16/2015] [Indexed: 11/30/2022]
Abstract
Heart rate is an important clinical measure that is often used in pathological diagnosis and prognosis. Valid detection of irregular heartbeats is crucial in the clinical practice. We propose an artificial neural network using the calculated pulse rate to detect irregular interbeats. The proposed system measures the calculated pulse rate to determine an "irregular interbeat on" or "irregular interbeat off" event. If an irregular interbeat is detected, the proposed system produces a danger warning, which is helpful for clinicians. If a non-irregular interbeat is detected, the proposed system displays the calculated pulse rate. We include a flow chart of the proposed software. In an experiment, we measure the calculated pulse rates and achieve an error percentage of < 3% in 20 participants with a wide age range. When we use the calculated pulse rates to detect irregular interbeats, we find such irregular interbeats in eight participants.
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Affiliation(s)
- Bih-Chyun Yeh
- Department of Electrical Engineering, Chang Gung University, Taiwan, Republic of China.
| | - Wen-Piao Lin
- Department of Electrical Engineering, Chang Gung University, Taiwan, Republic of China
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Elmansouri K, Latif R, Nassiri B, Maoulainine FMR. Developing a real time electrocardiogram system using virtual bio-instrumentation. J Med Syst 2014; 38:39. [PMID: 24705799 DOI: 10.1007/s10916-014-0039-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2013] [Accepted: 03/14/2014] [Indexed: 11/24/2022]
Abstract
Today bio-manufacturers propose various electrocardiogram (ECG) instruments that have addressed a wide variety of clinical issues. However, the discovery of new applications in ECG devices that provide doctors with the right information at the right time and in the right way will help them to provide a highest quality care possible. In this paper, we focus on the development of an accurate and robust virtual bio-instrument. The important goals of the described project is to provide online new diagnostic informations, an accurate analysis algorithm applied to the acquired signals, data capture from commercial monitors, fast real time ECG acquisition, real time data display and recording of real ECG signals which results in the improvement of data availability. The virtual bio-instrument is validated and tested on the level of robustness, diagnostic accuracy, diagnostic impact and Human - System Interface (HSI) functioning with collaboration of the cardiologists.
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Affiliation(s)
- Khalifa Elmansouri
- Signals System and Computer Sciences Group (ESSI), National School of Applied Sciences, Ibn Zohr University, Agadir, Morocco,
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Authier S, Pugsley MK, Troncy E, Curtis MJ. Arrhythmogenic liability screening in cardiovascular safety pharmacology: Commonality between non-clinical safety pharmacology and clinical thorough QT (TQT) studies. J Pharmacol Toxicol Methods 2010; 62:83-8. [DOI: 10.1016/j.vascn.2010.06.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2010] [Accepted: 06/11/2010] [Indexed: 01/10/2023]
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