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Safdar MF, Nowak RM, Pałka P. Pre-Processing techniques and artificial intelligence algorithms for electrocardiogram (ECG) signals analysis: A comprehensive review. Comput Biol Med 2024; 170:107908. [PMID: 38217973 DOI: 10.1016/j.compbiomed.2023.107908] [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: 10/10/2023] [Revised: 12/19/2023] [Accepted: 12/24/2023] [Indexed: 01/15/2024]
Abstract
Electrocardiogram (ECG) are the physiological signals and a standard test to measure the heart's electrical activity that depicts the movement of cardiac muscles. A review study has been conducted on ECG signals analysis with the help of artificial intelligence (AI) methods over the last ten years i.e., 2012-22. Primarily, the method of ECG analysis by software systems was divided into classical signal processing (e.g. spectrograms or filters), machine learning (ML) and deep learning (DL), including recursive models, transformers and hybrid. Secondly, the data sources and benchmark datasets were depicted. Authors grouped resources by ECG acquisition methods into hospital-based portable machines and wearable devices. Authors also included new trends like advanced pre-processing, data augmentation, simulations and agent-based modeling. The study found improvement in ECG examination perfection made each year through ML, DL, hybrid models, and transformers. Convolutional neural networks and hybrid models were more targeted and proved efficient. The transformer model extended the accuracy from 90% to 98%. The Physio-Net library helps acquire ECG signals, including the popular benchmark databases such as MIT-BIH, PTB, and challenging datasets. Similarly, wearable devices have been established as a appropriate option for monitoring patient health without the time and place limitations and are also helpful for AI model calibration with so far accuracy of 82%-83% on Samsung smartwatch. In the pre-processing signals, spectrogram generation through Fourier and wavelet transformations are erected leading approaches promoting on average accuracy of 90%-95%. Likewise, data enhancement using geometrical techniques is well-considered; however, extraction and concatenation-based methods need attention. As the what-if analysis in healthcare or cardiac issues can be performed using a complex simulation, the study reviews agent-based modeling and simulation approaches for cardiovascular risk event assessment.
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Affiliation(s)
- Muhammad Farhan Safdar
- Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland.
| | - Robert Marek Nowak
- Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland
| | - Piotr Pałka
- Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland
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Yoon GW, Joo S. Classification feasibility test on multi-lead electrocardiography signals generated from single-lead electrocardiography signals. Sci Rep 2024; 14:1888. [PMID: 38253719 PMCID: PMC10803292 DOI: 10.1038/s41598-024-52216-y] [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: 09/06/2023] [Accepted: 01/16/2024] [Indexed: 01/24/2024] Open
Abstract
Nowadays, Electrocardiogram (ECG) signals can be measured using wearable devices, such as smart watches. Most wearable devices provide only a few details; however, they have the advantage of recording data in real time. In this study, 12-lead ECG signals were generated from lead I and their feasibility was tested to obtain more details. The 12-lead ECG signals were generated using a U-net-based generative adversarial network (GAN) that was trained on ECG data obtained from the Asan Medical Center. Subsequently, unseen PTB-XL PhysioNet data were used to produce real 12-lead ECG signals for classification. The generated and real 12-lead ECG signals were then compared using a ResNet classification model; and the normal, atrial fibrillation (A-fib), left bundle branch block (LBBB), right bundle branch block (RBBB), left ventricular hypertrophy (LVH), and right ventricular hypertrophy (RVH) were classified. The mean precision, recall, and f1-score for the real 12-lead ECG signals are 0.70, 0.72, and 0.70, and that for the generated 12-lead ECG signals are 0.82, 0.80, and 0.81, respectively. In our study, according to the result generated 12-lead ECG signals performed better than real 12-lead ECG.
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Affiliation(s)
- Gi-Won Yoon
- Department of Biomedical Engineering, Asan Medical Center, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Segyeong Joo
- Department of Biomedical Engineering, Asan Medical Center, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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Monachino G, Zanchi B, Fiorillo L, Conte G, Auricchio A, Tzovara A, Faraci FD. Deep Generative Models: The winning key for large and easily accessible ECG datasets? Comput Biol Med 2023; 167:107655. [PMID: 37976830 DOI: 10.1016/j.compbiomed.2023.107655] [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: 07/24/2023] [Revised: 10/04/2023] [Accepted: 10/31/2023] [Indexed: 11/19/2023]
Abstract
Large high-quality datasets are essential for building powerful artificial intelligence (AI) algorithms capable of supporting advancement in cardiac clinical research. However, researchers working with electrocardiogram (ECG) signals struggle to get access and/or to build one. The aim of the present work is to shed light on a potential solution to address the lack of large and easily accessible ECG datasets. Firstly, the main causes of such a lack are identified and examined. Afterward, the potentials and limitations of cardiac data generation via deep generative models (DGMs) are deeply analyzed. These very promising algorithms have been found capable not only of generating large quantities of ECG signals but also of supporting data anonymization processes, to simplify data sharing while respecting patients' privacy. Their application could help research progress and cooperation in the name of open science. However several aspects, such as a standardized synthetic data quality evaluation and algorithm stability, need to be further explored.
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Affiliation(s)
- Giuliana Monachino
- Institute of Digital Technologies for Personalized Healthcare - MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Via la Santa 1, Lugano 6900, Switzerland; Institute of Informatics, University of Bern, Neubrückstrasse 10, Bern 3012, Switzerland.
| | - Beatrice Zanchi
- Institute of Digital Technologies for Personalized Healthcare - MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Via la Santa 1, Lugano 6900, Switzerland; Department of Quantitative Biomedicine, University of Zurich, Schmelzbergstrasse 26, Zurich 8091, Switzerland
| | - Luigi Fiorillo
- Institute of Digital Technologies for Personalized Healthcare - MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Via la Santa 1, Lugano 6900, Switzerland
| | - Giulio Conte
- Division of Cardiology, Fondazione Cardiocentro Ticino, Via Tesserete 48, Lugano 6900, Switzerland; Centre for Computational Medicine in Cardiology, Faculty of Informatics, Università della Svizzera Italiana, Via la Santa 1, Lugano 6900, Switzerland
| | - Angelo Auricchio
- Division of Cardiology, Fondazione Cardiocentro Ticino, Via Tesserete 48, Lugano 6900, Switzerland; Centre for Computational Medicine in Cardiology, Faculty of Informatics, Università della Svizzera Italiana, Via la Santa 1, Lugano 6900, Switzerland
| | - Athina Tzovara
- Institute of Informatics, University of Bern, Neubrückstrasse 10, Bern 3012, Switzerland; Sleep Wake Epilepsy Center | NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16, Bern 3010, Switzerland
| | - Francesca Dalia Faraci
- Institute of Digital Technologies for Personalized Healthcare - MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Via la Santa 1, Lugano 6900, Switzerland
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Rahman MM, Rivolta MW, Badilini F, Sassi R. A Systematic Survey of Data Augmentation of ECG Signals for AI Applications. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115237. [PMID: 37299964 DOI: 10.3390/s23115237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 05/23/2023] [Accepted: 05/27/2023] [Indexed: 06/12/2023]
Abstract
AI techniques have recently been put under the spotlight for analyzing electrocardiograms (ECGs). However, the performance of AI-based models relies on the accumulation of large-scale labeled datasets, which is challenging. To increase the performance of AI-based models, data augmentation (DA) strategies have been developed recently. The study presented a comprehensive systematic literature review of DA for ECG signals. We conducted a systematic search and categorized the selected documents by AI application, number of leads involved, DA method, classifier, performance improvements after DA, and datasets employed. With such information, this study provided a better understanding of the potential of ECG augmentation in enhancing the performance of AI-based ECG applications. This study adhered to the rigorous PRISMA guidelines for systematic reviews. To ensure comprehensive coverage, publications between 2013 and 2023 were searched across multiple databases, including IEEE Explore, PubMed, and Web of Science. The records were meticulously reviewed to determine their relevance to the study's objective, and those that met the inclusion criteria were selected for further analysis. Consequently, 119 papers were deemed relevant for further review. Overall, this study shed light on the potential of DA to advance the field of ECG diagnosis and monitoring.
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Affiliation(s)
- Md Moklesur Rahman
- Dipartimento di Informatica, Università degli Studi di Milano, 20133 Milan, Italy
| | | | - Fabio Badilini
- School of Nursing, University of California, San Francisco, CA 94143, USA
- AMPS-LLC, New York, NY 10025, USA
| | - Roberto Sassi
- Dipartimento di Informatica, Università degli Studi di Milano, 20133 Milan, Italy
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