1
|
Lee SH, Hong WP, Kim J, Cho Y, Lee E. Smartphone AI vs. Medical Experts: A Comparative Study in Prehospital STEMI Diagnosis. Yonsei Med J 2024; 65:174-180. [PMID: 38373837 PMCID: PMC10896668 DOI: 10.3349/ymj.2023.0341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/23/2023] [Accepted: 11/03/2023] [Indexed: 02/21/2024] Open
Abstract
PURPOSE Prehospital telecardiology facilitates early ST-elevation myocardial infarction (STEMI) detection, yet its widespread implementation remains challenging. Extracting digital STEMI biomarkers from printed electrocardiograms (ECGs) using phone cameras could offer an affordable and scalable solution. This study assessed the feasibility of this approach with real-world prehospital ECGs. MATERIALS AND METHODS Patients suspected of having STEMI by emergency medical technicians (EMTs) were identified from a policy research dataset. A deep learning-based ECG analyzer (QCG™ analyzer) extracted a STEMI biomarker (qSTEMI) from prehospital ECGs. The biomarker was compared to a group of human experts, including five emergency medical service directors (board-certified emergency physicians) and three interventional cardiologists based on their consensus score (number of participants answering "yes" for STEMI). Non-inferiority of the biomarker was tested using a 0.100 margin of difference in sensitivity and specificity. RESULTS Among 53 analyzed patients (24 STEMI, 45.3%), the area under the receiver operating characteristic curve of qSTEMI and consensus score were 0.815 (0.691-0.938) and 0.736 (0.594-0.879), respectively (p=0.081). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of qSTEMI were 0.750 (0.583-0.917), 0.862 (0.690-0.966), 0.826 (0.679-0.955), and 0.813 (0.714-0.929), respectively. For the consensus score, sensitivity, specificity, PPV, and NPV were 0.708 (0.500-0.875), 0.793 (0.655-0.966), 0.750 (0.600-0.941), and 0.760 (0.655-0.880), respectively. The 95% confidence interval of sensitivity and specificity differences between qSTEMI and consensus score were 0.042 (-0.099-0.182) and 0.103 (-0.043-0.250), respectively, confirming qSTEMI's non-inferiority. CONCLUSION The digital STEMI biomarker, derived from printed prehospital ECGs, demonstrated non-inferiority to expert consensus, indicating a promising approach for enhancing prehospital telecardiology.
Collapse
Affiliation(s)
- Seung Hyo Lee
- National Fire Agency Pre-hospital Emergency Medical Research TF, Sejong, Korea
| | - Won Pyo Hong
- National Emergency Medical Center, National Medical Center, Seoul, Korea
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, Korea.
| | - Joonghee Kim
- Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
- ARPI Inc., Seongnam, Korea.
| | - Youngjin Cho
- ARPI Inc., Seongnam, Korea
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Eunkyoung Lee
- Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
- ARPI Inc., Seongnam, Korea
| |
Collapse
|
2
|
Bekheet M, Sallah M, Alghamdi NS, Rusu-Both R, Elgarayhi A, Elmogy M. Cardiac Fibrosis Automated Diagnosis Based on FibrosisNet Network Using CMR Ischemic Cardiomyopathy. Diagnostics (Basel) 2024; 14:255. [PMID: 38337771 PMCID: PMC10855193 DOI: 10.3390/diagnostics14030255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/18/2024] [Accepted: 01/19/2024] [Indexed: 02/12/2024] Open
Abstract
Ischemic heart condition is one of the most prevalent causes of death that can be treated more effectively and lead to fewer fatalities if identified early. Heart muscle fibrosis affects the diastolic and systolic function of the heart and is linked to unfavorable cardiovascular outcomes. Cardiac magnetic resonance (CMR) scarring, a risk factor for ischemic heart disease, may be accurately identified by magnetic resonance imaging (MRI) to recognize fibrosis. In the past few decades, numerous methods based on MRI have been employed to identify and categorize cardiac fibrosis. Because they increase the therapeutic advantages and the likelihood that patients will survive, developing these approaches is essential and has significant medical benefits. A brand-new method that uses MRI has been suggested to help with diagnosing. Advances in deep learning (DL) networks contribute to the early and accurate diagnosis of heart muscle fibrosis. This study introduces a new deep network known as FibrosisNet, which detects and classifies fibrosis if it is present. It includes some of 17 various series layers to achieve the fibrosis detection target. The introduced classification system is trained and evaluated for the best performance results. In addition, deep transfer-learning models are applied to the different famous convolution neural networks to find fibrosis detection architectures. The FibrosisNet architecture achieves an accuracy of 96.05%, a sensitivity of 97.56%, and an F1-Score of 96.54%. The experimental results show that FibrosisNet has numerous benefits and produces higher results than current state-of-the-art methods and other advanced CNN approaches.
Collapse
Affiliation(s)
- Mohamed Bekheet
- Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura 35516, Egypt
- Radiography and Medical Imaging Department, Faculty of Applied Health Sciences Technology, Sphinx University, New Assiut 71515, Egypt
| | - Mohammed Sallah
- Department of Physics, College of Sciences, University of Bisha, P.O. Box 344, Bisha 61922, Saudi Arabia
| | - Norah S. Alghamdi
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Roxana Rusu-Both
- Automation Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400027 Cluj-Napoca, Romania
| | - Ahmed Elgarayhi
- Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura 35516, Egypt
| | - Mohammed Elmogy
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
| |
Collapse
|
3
|
Qi M, Shao H, Shi N, Wang G, Lv Y. Arrhythmia classification detection based on multiple electrocardiograms databases. PLoS One 2023; 18:e0290995. [PMID: 37756278 PMCID: PMC10529562 DOI: 10.1371/journal.pone.0290995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 08/20/2023] [Indexed: 09/29/2023] Open
Abstract
According to the World Health Organization, cardiovascular diseases are the leading cause of deaths globally. Electrocardiogram (ECG) is a non-invasive approach for detecting heart diseases and reducing the risk of heart disease-related death. However, there are limited numbers of ECG samples and imbalance distribution for existing ECG databases. It is difficult to train practical and efficient neural networks. Based on the analysis and research of many existing ECG databases, this paper conduct an in-depth study on three fine-labeled ECG databases, to extract heartbeats, unify the sampling frequency, and propose a self-processing method of heartbeats, and finally form a unified ECG arrhythmia classification database, noted as Hercules-3. It is separated into training sets (80%) and testing sets (the remaining 20%). In order to verify its capabilities, we have trained a 16-classification fully connected neural network based on Hercules-3 and it achieves an accuracy rate of up to 98.67%. Compared with other data processing, our proposed method improves classification recall by at least 6%, classification accuracy by at least 4%, and F1-score by at least 7%.
Collapse
Affiliation(s)
- Meng Qi
- Computer and Information Engineering Department, Luoyang Institute of Science and Technology, Luoyang, China
- Henan Province Engineering Research Center of Industrial Intelligent Vision, Luoyang, China
| | - Hongxiang Shao
- Computer and Information Engineering Department, Luoyang Institute of Science and Technology, Luoyang, China
- Henan Province Engineering Research Center of Industrial Intelligent Vision, Luoyang, China
| | - Nianfeng Shi
- Computer and Information Engineering Department, Luoyang Institute of Science and Technology, Luoyang, China
- Henan Province Engineering Research Center of Industrial Intelligent Vision, Luoyang, China
| | - Guoqiang Wang
- Computer and Information Engineering Department, Luoyang Institute of Science and Technology, Luoyang, China
- Henan Province Engineering Research Center of Industrial Intelligent Vision, Luoyang, China
| | - Yifei Lv
- School of Computer Science and Engineering Department, Tianjin University of Technology, Tianjin, China
| |
Collapse
|
4
|
Shumba AT, Montanaro T, Sergi I, Bramanti A, Ciccarelli M, Rispoli A, Carrizzo A, De Vittorio M, Patrono L. Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects. SENSORS (BASEL, SWITZERLAND) 2023; 23:6896. [PMID: 37571678 PMCID: PMC10422393 DOI: 10.3390/s23156896] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 07/31/2023] [Accepted: 08/01/2023] [Indexed: 08/13/2023]
Abstract
Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors.
Collapse
Affiliation(s)
- Angela-Tafadzwa Shumba
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; (A.-T.S.); (T.M.); (I.S.); (M.D.V.)
- Istituto Italiano di Tecnologia, Centre for Biomolecular Nanotechnologies, 73010 Arnesano, Italy
| | - Teodoro Montanaro
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; (A.-T.S.); (T.M.); (I.S.); (M.D.V.)
| | - Ilaria Sergi
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; (A.-T.S.); (T.M.); (I.S.); (M.D.V.)
| | - Alessia Bramanti
- Dipartimento di Medicina, Chirurgia e Odontoiatria “Scuola Medica Salernitana” (DIPMED), University of Salerno, 84081 Baronissi, Italy; (A.B.); (M.C.); (A.R.); (A.C.)
| | - Michele Ciccarelli
- Dipartimento di Medicina, Chirurgia e Odontoiatria “Scuola Medica Salernitana” (DIPMED), University of Salerno, 84081 Baronissi, Italy; (A.B.); (M.C.); (A.R.); (A.C.)
| | - Antonella Rispoli
- Dipartimento di Medicina, Chirurgia e Odontoiatria “Scuola Medica Salernitana” (DIPMED), University of Salerno, 84081 Baronissi, Italy; (A.B.); (M.C.); (A.R.); (A.C.)
| | - Albino Carrizzo
- Dipartimento di Medicina, Chirurgia e Odontoiatria “Scuola Medica Salernitana” (DIPMED), University of Salerno, 84081 Baronissi, Italy; (A.B.); (M.C.); (A.R.); (A.C.)
| | - Massimo De Vittorio
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; (A.-T.S.); (T.M.); (I.S.); (M.D.V.)
- Istituto Italiano di Tecnologia, Centre for Biomolecular Nanotechnologies, 73010 Arnesano, Italy
| | - Luigi Patrono
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; (A.-T.S.); (T.M.); (I.S.); (M.D.V.)
| |
Collapse
|
5
|
Mishra M, Pati UC. A classification framework for Autism Spectrum Disorder detection using sMRI: Optimizer based ensemble of deep convolution neural network with on-the-fly data augmentation. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
|
6
|
Zou C, Djajapermana M, Martens E, Muller A, Ruckert D, Muller P, Steger A, Becker M, Wolfgang U. DWT-CNNTRN: a Convolutional Transformer for ECG Classification with Discrete Wavelet Transform. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-6. [PMID: 38082682 DOI: 10.1109/embc40787.2023.10340561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Cardiovascular diseases are the leading cause of death worldwide. The diagnoses of cardiovascular diseases are usually carried out by cardiologists utilizing Electrocardiograms (ECGs). To assist these physicians in making an accurate diagnosis, there is a growing need for reliable and automatic ECG classifiers.In this study, a new method is proposed to classify 12-lead ECG recordings. The proposed model is composed of four components: the CNN(Convolutional Neural Network) module, the transformer module, the global hybrid pooling layer, and a classification layer. To improve the classification performance, the model takes the discrete wavelet transform of ECG signals as the model inputs and utilizes a hybrid pooling layer to condense the most important features over each period.The proposed model is evaluated using the test set of the China Physiological Signal Challenge 2018 dataset with 12-lead ECGs. It performs with an average accuracy of 0.86 and an average F1-scores of 0.83. The scores are particularly good for the block conditions (LBBB, RBBB, I-AVB). The main advantage of the proposed model is that, it obtains good results with a significantly smaller number of parameters compared to other individual and ensemble models.Clinical relevance- This work establishes a new ECG classifier model with high performance and low model size. It can make automatic ECG analysis more accessible, efficient, and accurate, especially in remote or underserved areas.
Collapse
|
7
|
Lai J, Tan H, Wang J, Ji L, Guo J, Han B, Shi Y, Feng Q, Yang W. Practical intelligent diagnostic algorithm for wearable 12-lead ECG via self-supervised learning on large-scale dataset. Nat Commun 2023; 14:3741. [PMID: 37353501 PMCID: PMC10290151 DOI: 10.1038/s41467-023-39472-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 06/15/2023] [Indexed: 06/25/2023] Open
Abstract
Cardiovascular disease is a major global public health problem, and intelligent diagnostic approaches play an increasingly important role in the analysis of electrocardiograms (ECGs). Convenient wearable ECG devices enable the detection of transient arrhythmias and improve patient health by making it possible to seek intervention during continuous monitoring. We collected 658,486 wearable 12-lead ECGs, among which 164,538 were annotated, and the remaining 493,948 were without diagnostic. We present four data augmentation operations and a self-supervised learning classification framework that can recognize 60 ECG diagnostic terms. Our model achieves an average area under the receiver-operating characteristic curve (AUROC) and average F1 score on the offline test of 0.975 and 0.575. The average sensitivity, specificity and F1-score during the 2-month online test are 0.736, 0.954 and 0.468, respectively. This approach offers real-time intelligent diagnosis, and detects abnormal segments in long-term ECG monitoring in the clinical setting for further diagnosis by cardiologists.
Collapse
Affiliation(s)
- Jiewei Lai
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Guangzhou, China
| | - Huixin Tan
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Guangzhou, China
| | - Jinliang Wang
- CardioCloud Medical Technology (Beijing) Co., Ltd., Beijing, China
| | - Lei Ji
- IT Department, Chinese PLA General Hospital, Beijing, China
| | - Jun Guo
- Department of Cardiology, Chinese PLA General Hospital, Beijing, China
| | - Baoshi Han
- Department of Cardiology, Chinese PLA General Hospital, Beijing, China
| | - Yajun Shi
- Department of Cardiology, Chinese PLA General Hospital, Beijing, China
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China.
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Guangzhou, China.
| | - Wei Yang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China.
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Guangzhou, China.
| |
Collapse
|
8
|
Jafari M, Shoeibi A, Khodatars M, Ghassemi N, Moridian P, Alizadehsani R, Khosravi A, Ling SH, Delfan N, Zhang YD, Wang SH, Gorriz JM, Alinejad-Rokny H, Acharya UR. Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review. Comput Biol Med 2023; 160:106998. [PMID: 37182422 DOI: 10.1016/j.compbiomed.2023.106998] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 03/01/2023] [Accepted: 04/28/2023] [Indexed: 05/16/2023]
Abstract
In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. At early stages, CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMRI) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians as each scan has many slices of data, and the contrast of it might be low. To address these issues, deep learning (DL) techniques have been employed in the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. The following presents research to detect CVDs using CMR images and the most significant DL methods. Another section discussed the challenges in diagnosing CVDs from CMRI data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. Finally, the most important findings of this study are presented in the conclusion section.
Collapse
Affiliation(s)
- Mahboobeh Jafari
- Internship in BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia
| | - Afshin Shoeibi
- Internship in BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia; Data Science and Computational Intelligence Institute, University of Granada, Spain.
| | - Marjane Khodatars
- Data Science and Computational Intelligence Institute, University of Granada, Spain
| | - Navid Ghassemi
- Internship in BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia
| | - Parisa Moridian
- Data Science and Computational Intelligence Institute, University of Granada, Spain
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Australia
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Australia
| | - Sai Ho Ling
- Faculty of Engineering and IT, University of Technology Sydney (UTS), Australia
| | - Niloufar Delfan
- Faculty of Computer Engineering, Dept. of Artificial Intelligence Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Yu-Dong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK
| | - Shui-Hua Wang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK
| | - Juan M Gorriz
- Data Science and Computational Intelligence Institute, University of Granada, Spain; Department of Psychiatry, University of Cambridge, UK
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia; UNSW Data Science Hub, The University of New South Wales, Sydney, NSW, 2052, Australia; Health Data Analytics Program, Centre for Applied Artificial Intelligence, Macquarie University, Sydney, 2109, Australia
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia; Dept. of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
| |
Collapse
|
9
|
Petmezas G, Stefanopoulos L, Kilintzis V, Tzavelis A, Rogers JA, Katsaggelos AK, Maglaveras N. State-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint). JMIR Med Inform 2022; 10:e38454. [PMID: 35969441 PMCID: PMC9425174 DOI: 10.2196/38454] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/03/2022] [Accepted: 07/03/2022] [Indexed: 11/13/2022] Open
Abstract
Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. Objective This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. Methods The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. Results We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. Conclusions We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.
Collapse
Affiliation(s)
- Georgios Petmezas
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Leandros Stefanopoulos
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vassilis Kilintzis
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Andreas Tzavelis
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States
| | - John A Rogers
- Department of Material Science, Northwestern University, Evanston, IL, United States
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | - Nicos Maglaveras
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| |
Collapse
|