1
|
Huang Y, Li H, Yu X. A novel time representation input based on deep learning for ECG classification. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
|
2
|
Boulif A, Ananou B, Ouladsine M, Delliaux S. A Literature Review: ECG-Based Models for Arrhythmia Diagnosis Using Artificial Intelligence Techniques. Bioinform Biol Insights 2023; 17:11779322221149600. [PMID: 36798080 PMCID: PMC9926384 DOI: 10.1177/11779322221149600] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 12/18/2022] [Indexed: 02/12/2023] Open
Abstract
In the health care and medical domain, it has been proven challenging to diagnose correctly many diseases with complicated and interferential symptoms, including arrhythmia. However, with the evolution of artificial intelligence (AI) techniques, the diagnosis and prognosis of arrhythmia became easier for the physicians and practitioners using only an electrocardiogram (ECG) examination. This review presents a synthesis of the studies conducted in the last 12 years to predict arrhythmia's occurrence by classifying automatically different heartbeat rhythms. From a variety of research academic databases, 40 studies were selected to analyze, among which 29 of them applied deep learning methods (72.5%), 9 of them addressed the problem with machine learning methods (22.5%), and 2 of them combined both deep learning and machine learning to predict arrhythmia (5%). Indeed, the use of AI for arrhythmia diagnosis is emerging in literature, although there are some challenging issues, such as the explicability of the Deep Learning methods and the computational resources needed to achieve high performance. However, with the continuous development of cloud platforms and quantum calculation for AI, we can achieve a breakthrough in arrhythmia diagnosis.
Collapse
Affiliation(s)
- Abir Boulif
- Aix-Marseille University, CNRS, LIS, Marseille, France,Abir Boulif, Aix-Marseille University, CNRS, LIS, 13397 Marseille, France.
| | | | | | | |
Collapse
|
3
|
Kan C, Ye Z, Zhou H, Cheruku SR. DG-ECG: Multi-stream deep graph learning for the recognition of disease-altered patterns in electrocardiogram. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
4
|
Zhao Y, Ren J, Zhang B, Wu J, Lyu Y. An explainable attention-based TCN heartbeats classification model for arrhythmia detection. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
5
|
Song G, Zhang J, Mao D, Chen G, Pang C. A Multimodel Fusion Method for Cardiovascular Disease Detection Using ECG. Emerg Med Int 2022; 2022:3561147. [PMID: 35615106 PMCID: PMC9126725 DOI: 10.1155/2022/3561147] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/13/2022] [Accepted: 04/25/2022] [Indexed: 11/25/2022] Open
Abstract
Objective. Electrocardiogram (ECG) is an important diagnostic tool that has been the subject of much research in recent years. Owing to a lack of well-labeled ECG record databases, most of this work has focused on heartbeat arrhythmia detection based on ECG signal quality. Approach. A record quality filter was designed to judge ECG signal quality, and a random forest method, a multilayer perceptron, and a residual neural network (RESNET)-based convolutional neural network were implemented to provide baselines for ECG record classification according to three different principles. A new multimodel method was constructed by fusing the random forest and RESNET approaches. Main Results. Owing to its ability to combine discriminative human-crafted features with RESNET deep features, the proposed new method showed over 88% classification accuracy and yielded the best results in comparison with alternative methods. Significance. A new multimodel fusion method was presented for abnormal cardiovascular detection based on ECG data. The experimental results show that separable convolution and multiscale convolution are vital for ECG record classification and are effective for use with one-dimensional ECG sequences.
Collapse
Affiliation(s)
- Guanghui Song
- School of Computer and Data Engineering, Ningbo Tech University, Ningbo 315100, Zhejiang, China
| | - Jiajian Zhang
- School of Computer and Data Engineering, Ningbo Tech University, Ningbo 315100, Zhejiang, China
| | - Dandan Mao
- Department of Electrocardiogram, Ningbo Hospital of Traditional Chinese Medicine, Ningbo 315100, Zhejiang, China
| | - Genlang Chen
- School of Computer and Data Engineering, Ningbo Tech University, Ningbo 315100, Zhejiang, China
| | - Chaoyi Pang
- School of Computer and Data Engineering, Ningbo Tech University, Ningbo 315100, Zhejiang, China
| |
Collapse
|
6
|
Many heads are better than one: A multiscale neural information feature fusion framework for spatial route selections decoding from multichannel neural recordings of pigeons. Brain Res Bull 2022; 184:1-12. [DOI: 10.1016/j.brainresbull.2022.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 02/07/2022] [Accepted: 03/10/2022] [Indexed: 11/22/2022]
|
7
|
Du C, Liu PX, Zheng M. Classification of Imbalanced Electrocardiosignal Data using Convolutional Neural Network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 214:106483. [PMID: 34871837 DOI: 10.1016/j.cmpb.2021.106483] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 10/14/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE In the application of wearable heart-monitors, it is of great significance to analyze electrocardiogram (ECG) signals for anomaly detection. ECG arrhythmia classification remains an open problem in that it cannot easily recognize data from minority classes due to the imbalanced dataset and particular characteristic of the time series signal. In this study, a novel method is presented as a possible solution to imbalanced classification problems. METHODS An improved data augmentation method based on variational auto-encoder (VAE) and auxiliary classifier generative adversarial network (ACGAN) is implemented to address the difficulties resulting from the imbalanced dataset. Based on the augmented dataset, convolutional neural network (CNN) classifiers are employed to automatically recognize arrhythmias using two-dimensional ECG images. RESULTS In experimental studies conducted with the MIT-BIH arrhythmia database, the proposed method achieves 98.45% accuracy and 97.03% sensitivity. The sensitivities of two minority classes achieve 95.83% and 97.37%, respectively. CONCLUSION In imbalanced classification, the sensitivity of minority class is a key evaluation indicator. One of the significant contributions of this study is that the proposed method can obtain higher sensitivity of minority class. The experimental results demonstrate that the proposed method for ECG arrhythmia calssification under imbalanced data has better performance compared with traditional cropping augmentation methods and traditional classifiers.
Collapse
Affiliation(s)
- Chaofan Du
- School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, 100044, P. R. China.
| | - Peter Xiaoping Liu
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada.
| | - Minhua Zheng
- School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, 100044, P. R. China; Key Laboratory of Vehicle Advanced Manufacturing, Measuring and Control Technology (Beijing Jiaotong University), Ministry of Education, P. R. China.
| |
Collapse
|
8
|
Rahul J, Sharma LD. Artificial intelligence-based approach for atrial fibrillation detection using normalised and short-duration time-frequency ECG. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103270] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
|
9
|
Tadesse GA, Javed H, Weldemariam K, Liu Y, Liu J, Chen J, Zhu T. DeepMI: Deep multi-lead ECG fusion for identifying myocardial infarction and its occurrence-time. Artif Intell Med 2021; 121:102192. [PMID: 34763807 DOI: 10.1016/j.artmed.2021.102192] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 07/07/2021] [Accepted: 10/05/2021] [Indexed: 11/16/2022]
Abstract
Myocardial Infarction (MI) has the highest mortality of all cardiovascular diseases (CVDs). Detection of MI and information regarding its occurrence-time in particular, would enable timely interventions that may improve patient outcomes, thereby reducing the global rise in CVD deaths. Electrocardiogram (ECG) recordings are currently used to screen MI patients. However, manual inspection of ECGs is time-consuming and prone to subjective bias. Machine learning methods have been adopted for automated ECG diagnosis, but most approaches require extraction of ECG beats or consider leads independently of one another. We propose an end-to-end deep learning approach, DeepMI, to classify MI from Normal cases as well as identifying the time-occurrence of MI (defined as Acute, Recent and Old), using a collection of fusion strategies on 12 ECG leads at data-, feature-, and decision-level. In order to minimise computational overhead, we employ transfer learning using existing computer vision networks. Moreover, we use recurrent neural networks to encode the longitudinal information inherent in ECGs. We validated DeepMI on a dataset collected from 17,381 patients, in which over 323,000 samples were extracted per ECG lead. We were able to classify Normal cases as well as Acute, Recent and Old onset cases of MI, with AUROCs of 96.7%, 82.9%, 68.6% and 73.8%, respectively. We have demonstrated a multi-lead fusion approach to detect the presence and occurrence-time of MI. Our end-to-end framework provides flexibility for different levels of multi-lead ECG fusion and performs feature extraction via transfer learning.
Collapse
Affiliation(s)
- Girmaw Abebe Tadesse
- Department of Engineering, University of Oxford, Oxford, United Kingdom; IBM Research, Kenya.
| | - Hamza Javed
- Department of Engineering, University of Oxford, Oxford, United Kingdom
| | | | - Yong Liu
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Coronary Disease, Guangdong Provincial People's Hospital, University of Technology, Guangzhou, China
| | - Jin Liu
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Coronary Disease, Guangdong Provincial People's Hospital, University of Technology, Guangzhou, China
| | - Jiyan Chen
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Coronary Disease, Guangdong Provincial People's Hospital, University of Technology, Guangzhou, China
| | - Tingting Zhu
- Department of Engineering, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
10
|
Murat F, Yildirim O, Talo M, Demir Y, Tan RS, Ciaccio EJ, Acharya UR. Exploring deep features and ECG attributes to detect cardiac rhythm classes. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107473] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
|
11
|
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.
Collapse
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
| |
Collapse
|
12
|
A New Multichannel Parallel Network Framework for the Special Structure of Multilead ECG. JOURNAL OF HEALTHCARE ENGINEERING 2020; 2020:8889483. [PMID: 33343853 PMCID: PMC7728482 DOI: 10.1155/2020/8889483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 10/26/2020] [Accepted: 11/24/2020] [Indexed: 11/18/2022]
Abstract
Electrocardiogram (ECG) contains the rhythmic features of continuous heartbeat and morphological features of ECG waveforms and varies among different diseases. Based on ECG signal features, we propose a combination of multiple neural networks, the multichannel parallel neural network (MLCNN-BiLSTM), to explore feature information contained in ECG. The MLCNN channel is used in extracting the morphological features of ECG waveforms. Compared with traditional convolutional neural network (CNN), the MLCNN can accurately extract strong relevant information on multilead ECG while ignoring irrelevant information. It is suitable for the special structures of multilead ECG. The Bidirectional Long Short-Term Memory (BiLSTM) channel is used in extracting the rhythmic features of ECG continuous heartbeat. Finally, by initializing the core threshold parameters and using the backpropagation algorithm to update automatically, the weighted fusion of the temporal-spatial features extracted from multiple channels in parallel is used in exploring the sensitivity of different cardiovascular diseases to morphological and rhythmic features. Experimental results show that the accuracy rate of multiple cardiovascular diseases is 87.81%, sensitivity is 86.00%, and specificity is 87.76%. We proposed the MLCNN-BiLSTM neural network that can be used as the first-round screening tool for clinical diagnosis of ECG.
Collapse
|
13
|
Noh YH, Seo JY, Jeong DU. Development of a Knowledge Discovery Computing based wearable ECG monitoring system. INFORMATION TECHNOLOGY & MANAGEMENT 2020. [DOI: 10.1007/s10799-020-00318-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|