1
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Cui B, Qi Z, Liu W, Zhang G, Lin D. ZBP1-mediated PANoptosis: A possible novel mechanism underlying the therapeutic effects of penehyclidine hydrochloride on myocardial ischemia-reperfusion injury. Int Immunopharmacol 2024; 137:112373. [PMID: 38852523 DOI: 10.1016/j.intimp.2024.112373] [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: 12/06/2023] [Revised: 05/21/2024] [Accepted: 05/28/2024] [Indexed: 06/11/2024]
Abstract
Although penehyclidine hydrochloride (PHC) has been identified to alleviate myocardial injury induced by ischemia/reperfusion (I/R), the regulatory molecules and related mechanisms are unknown. In this study, bioinformatics, molecular biology, and biochemistry methods were used to explore the molecular mechanisms and targets of PHC. In the myocardial ischemia-reperfusion injury (MIRI)-induced rat model, PHC pretreatment significantly improved cardiac function (p < 0.01). Multiple differentially expressed genes, including Z-DNA binding protein 1 (ZBP1), were identified through mRNA sequencing analysis of myocardial ischemic penumbra tissue in MIRI rats. The transduction of the ZBP1 adenovirus vector (Ad-Zbp1) in PHC-pretreated rats exhibited a reversible augmentation in myocardial infarct size (p < 0.01), pronounced pathological damage to the myocardial tissue, as well as a significant elevation of serum myocardial enzymes (p < 0.05). The interaction among ZBP1, fas-associating via death domain (FADD), and receptor-interacting serine/threonine-protein kinase 3 (RIPK3) leads to a remarkable up-regulation of cleaved-Caspase-1 (Cl-Casp-1), N-terminal gasdermin D (N-GSDMD), phospho-mixed lineage kinase domain-like Ser358 (p-MLKLS358), and other regulatory proteins, thereby triggering pyroptosis, apoptosis, and necroptosis (PANoptosis) in cardiomyocytes of MIRI rats. Moreover, the transduction of Ad-Zbp1 in the oxygen-glucose deprivation/re-oxygenation (OGD/R)-induced H9c2 cell model also dramatically augmented the number of cell deaths. However, the intervention of PHC considerably enhanced cell viability (p < 0.01), effectively mitigated the release of myocardial enzymes (p < 0.05), and markedly attenuated the expression levels of PANoptosis regulatory proteins through restraint of ZBP1 expression. Therefore, the therapeutic efficacy of PHC in improving MIRI might be attributed to targeting ZBP1-mediated PANoptosis.
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Affiliation(s)
- Boqun Cui
- Center for Anesthesiology, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, People's Republic of China
| | - Zeyou Qi
- Center for Anesthesiology, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, People's Republic of China
| | - Wenjun Liu
- Center for Anesthesiology, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, People's Republic of China
| | - Guanzheng Zhang
- Center for Anesthesiology, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, People's Republic of China
| | - Duomao Lin
- Center for Anesthesiology, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, People's Republic of China.
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2
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Chinni BK, Manlhiot C. Emerging Analytical Approaches for Personalized Medicine Using Machine Learning In Pediatric and Congenital Heart Disease. Can J Cardiol 2024:S0828-282X(24)00585-3. [PMID: 39097187 DOI: 10.1016/j.cjca.2024.07.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 07/25/2024] [Accepted: 07/26/2024] [Indexed: 08/05/2024] Open
Abstract
Precision and personalized medicine, the process by which patient management is tailored to individual circumstances, are now terms that are familiar to cardiologists, despite it still being an emerging field. Although precision medicine relies most often on the underlying biology and pathophysiology of a patient's condition, personalized medicine relies on digital biomarkers generated through algorithms. Given the complexity of the underlying data, these digital biomarkers are most often generated through machine-learning algorithms. There are a number of analytic considerations regarding the creation of digital biomarkers that are discussed in this review, including data preprocessing, time dependency and gating, dimensionality reduction, and novel methods, both in the realm of supervised and unsupervised machine learning. Some of these considerations, such as sample size requirements and measurements of model performance, are particularly challenging in small and heterogeneous populations with rare outcomes such as children with congenital heart disease. Finally, we review analytic considerations for the deployment of digital biomarkers in clinical settings, including the emerging field of clinical artificial intelligence (AI) operations, computational needs for deployment, efforts to increase the explainability of AI, algorithmic drift, and the needs for distributed surveillance and federated learning. We conclude this review by discussing a recent simulation study that shows that, despite these analytic challenges and complications, the use of digital biomarkers in managing clinical care might have substantial benefits regarding individual patient outcomes.
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Affiliation(s)
- Bhargava K Chinni
- The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Cedric Manlhiot
- The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA; Research Institute, SickKids Hospital, Department of Pediatrics, University of Toronto, Toronto, Ontario, Canada.
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3
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Ma L, Zhang F. A Novel Real-Time Detection and Classification Method for ECG Signal Images Based on Deep Learning. SENSORS (BASEL, SWITZERLAND) 2024; 24:5087. [PMID: 39204785 PMCID: PMC11360666 DOI: 10.3390/s24165087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2024] [Revised: 07/28/2024] [Accepted: 08/02/2024] [Indexed: 09/04/2024]
Abstract
In this paper, a novel deep learning method Mamba-RAYOLO is presented, which can improve detection and classification in the processing and analysis of ECG images in real time by integrating three advanced modules. The feature extraction module in our work with a multi-branch structure during training can capture a wide range of features to ensure efficient inference and rich feature extraction. The attention mechanism module utilized in our proposed network can dynamically focus on the most relevant spatial and channel-wise features to improve detection accuracy and computational efficiency. Then, the extracted features can be refined for efficient spatial feature processing and robust feature fusion. Several sets of experiments have been carried out to test the validity of the proposed Mamba-RAYOLO and these indicate that our method has made significant improvements in the detection and classification of ECG images. The research offers a promising framework for more accurate and efficient medical ECG diagnostics.
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Affiliation(s)
- Linjuan Ma
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China;
| | - Fuquan Zhang
- College of Computer and Control Engineering, Minjiang University, Fuzhou 350108, China
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4
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Islam MS, Kalmady SV, Hindle A, Sandhu R, Sun W, Sepehrvand N, Greiner R, Kaul P. Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications. Can J Cardiol 2024:S0828-282X(24)00523-3. [PMID: 38992812 DOI: 10.1016/j.cjca.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 07/04/2024] [Accepted: 07/05/2024] [Indexed: 07/13/2024] Open
Abstract
Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECGs) has the potential to transform diagnosis and estimate the prognosis of not only cardiac but, increasingly, noncardiac conditions. In this review, we summarize clinical studies and AI-enhanced ECG-based clinical applications in the early detection, diagnosis, and estimating prognosis of cardiovascular diseases in the past 5 years (2019-2023). With advancements in deep learning and the rapid increased use of ECG technologies, a large number of clinical studies have been published. However, most of these studies are single-centre, retrospective, proof-of-concept studies that lack external validation. Prospective studies that progress from development toward deployment in clinical settings account for < 15% of the studies. Successful implementations of ECG-based AI applications that have received approval from the Food and Drug Administration have been developed through commercial collaborations, with approximately half of them being for mobile or wearable devices. The field is in its early stages, and overcoming several obstacles is essential, such as prospective validation in multicentre large data sets, addressing technical issues, bias, privacy, data security, model generalizability, and global scalability. This review concludes with a discussion of these challenges and potential solutions. By providing a holistic view of the state of AI in ECG analysis, this review aims to set a foundation for future research directions, emphasizing the need for comprehensive, clinically integrated, and globally deployable AI solutions in cardiovascular disease management.
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Affiliation(s)
- Md Saiful Islam
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Sunil Vasu Kalmady
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Abram Hindle
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Roopinder Sandhu
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Smidt Heart Institute, Cedars-Sinai Medical Center Hospital System, Los Angeles, California, USA
| | - Weijie Sun
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Nariman Sepehrvand
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Russell Greiner
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada; Alberta Machine Intelligence Institute, Edmonton, Alberta, Canada
| | - Padma Kaul
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.
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5
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Petmezas G, Papageorgiou VE, Vassilikos V, Pagourelias E, Tsaklidis G, Katsaggelos AK, Maglaveras N. Recent advancements and applications of deep learning in heart failure: Α systematic review. Comput Biol Med 2024; 176:108557. [PMID: 38728995 DOI: 10.1016/j.compbiomed.2024.108557] [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: 03/15/2024] [Revised: 04/12/2024] [Accepted: 05/05/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND Heart failure (HF), a global health challenge, requires innovative diagnostic and management approaches. The rapid evolution of deep learning (DL) in healthcare necessitates a comprehensive review to evaluate these developments and their potential to enhance HF evaluation, aligning clinical practices with technological advancements. OBJECTIVE This review aims to systematically explore the contributions of DL technologies in the assessment of HF, focusing on their potential to improve diagnostic accuracy, personalize treatment strategies, and address the impact of comorbidities. METHODS A thorough literature search was conducted across four major electronic databases: PubMed, Scopus, Web of Science and IEEE Xplore, yielding 137 articles that were subsequently categorized into five primary application areas: cardiovascular disease (CVD) classification, HF detection, image analysis, risk assessment, and other clinical analyses. The selection criteria focused on studies utilizing DL algorithms for HF assessment, not limited to HF detection but extending to any attempt in analyzing and interpreting HF-related data. RESULTS The analysis revealed a notable emphasis on CVD classification and HF detection, with DL algorithms showing significant promise in distinguishing between affected individuals and healthy subjects. Furthermore, the review highlights DL's capacity to identify underlying cardiomyopathies and other comorbidities, underscoring its utility in refining diagnostic processes and tailoring treatment plans to individual patient needs. CONCLUSIONS This review establishes DL as a key innovation in HF management, highlighting its role in advancing diagnostic accuracy and personalized care. The insights provided advocate for the integration of DL in clinical settings and suggest directions for future research to enhance patient outcomes in HF care.
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Affiliation(s)
- Georgios Petmezas
- 2nd Department of Obstetrics and Gynecology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece; Centre for Research and Technology Hellas, Thessaloniki, Greece.
| | | | - Vasileios Vassilikos
- 3rd Department of Cardiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Efstathios Pagourelias
- 3rd Department of Cardiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - George Tsaklidis
- Department of Mathematics, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA
| | - Nicos Maglaveras
- 2nd Department of Obstetrics and Gynecology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
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6
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Moreno-Sánchez PA, García-Isla G, Corino VDA, Vehkaoja A, Brukamp K, van Gils M, Mainardi L. ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review. Comput Biol Med 2024; 172:108235. [PMID: 38460311 DOI: 10.1016/j.compbiomed.2024.108235] [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: 08/11/2023] [Revised: 02/07/2024] [Accepted: 02/25/2024] [Indexed: 03/11/2024]
Abstract
Cardiovascular diseases (CVD) are a leading cause of death globally, and result in significant morbidity and reduced quality of life. The electrocardiogram (ECG) plays a crucial role in CVD diagnosis, prognosis, and prevention; however, different challenges still remain, such as an increasing unmet demand for skilled cardiologists capable of accurately interpreting ECG. This leads to higher workload and potential diagnostic inaccuracies. Data-driven approaches, such as machine learning (ML) and deep learning (DL) have emerged to improve existing computer-assisted solutions and enhance physicians' ECG interpretation of the complex mechanisms underlying CVD. However, many ML and DL models used to detect ECG-based CVD suffer from a lack of explainability, bias, as well as ethical, legal, and societal implications (ELSI). Despite the critical importance of these Trustworthy Artificial Intelligence (AI) aspects, there is a lack of comprehensive literature reviews that examine the current trends in ECG-based solutions for CVD diagnosis or prognosis that use ML and DL models and address the Trustworthy AI requirements. This review aims to bridge this knowledge gap by providing a systematic review to undertake a holistic analysis across multiple dimensions of these data-driven models such as type of CVD addressed, dataset characteristics, data input modalities, ML and DL algorithms (with a focus on DL), and aspects of Trustworthy AI like explainability, bias and ethical considerations. Additionally, within the analyzed dimensions, various challenges are identified. To these, we provide concrete recommendations, equipping other researchers with valuable insights to understand the current state of the field comprehensively.
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Affiliation(s)
| | - Guadalupe García-Isla
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Italy
| | - Valentina D A Corino
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Italy
| | - Antti Vehkaoja
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | | | - Mark van Gils
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Luca Mainardi
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Italy
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7
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Kim Y, Jo H, Jang TG, Park SY, Park HY, Cho SP, Park J, Kim SH, Urtnasan E. SleepMI: An AI-based screening algorithm for myocardial infarction using nocturnal electrocardiography. Heliyon 2024; 10:e26548. [PMID: 38444951 PMCID: PMC10912038 DOI: 10.1016/j.heliyon.2024.e26548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 02/07/2024] [Accepted: 02/15/2024] [Indexed: 03/07/2024] Open
Abstract
Myocardial infarction (MI) is a common cardiovascular disease, the early diagnosis of which is essential for effective treatment and reduced mortality. Therefore, novel methods are required for automatic screening or early diagnosis of MI, and many studies have proposed diverse conventional methods for its detection. In this study, we aimed to develop a sleep-myocardial infarction (sleepMI) algorithm for automatic screening of MI based on nocturnal electrocardiography (ECG) findings from diagnostic polysomnography (PSG) data using artificial intelligence (AI) models. The proposed sleepMI algorithm was designed using representation and ensemble learning methods and optimized via dropout and batch normalization. In the sleepMI algorithm, a deep convolutional neural network and light gradient boost machine (LightGBM) models were mixed to obtain robust and stable performance for screening MI from nocturnal ECG findings. The nocturnal ECG signal was extracted from 2,691 participants (2,331 healthy individuals and 360 patients with MI) from the PSG data of the second follow-up stage of the Sleep Heart Health Study. The nocturnal ECG signal was extracted 3 h after sleep onset and segmented at 30-s intervals for each participant. All ECG datasets were divided into training, validation, and test sets consisting of 574,729, 143,683, and 718,412 segments, respectively. The proposed sleepMI model exhibited very high performance with precision, recall, and F1-score of 99.38%, 99.38%, and 99.38%, respectively. The total mean accuracy for automatic screening of MI using a nocturnal single-lead ECG was 99.387%. MI events can be detected using conventional 12-lead ECG signals and polysomnographic ECG recordings using our model.
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Affiliation(s)
- Youngtae Kim
- Medical Intelligence Lab, Wonju College of Medicine, Yonsei University, Wonju-si, 26426, Republic of Korea
| | - Hoon Jo
- Artificial Intelligence Big Data Medical Center, Wonju College of Medicine, Yonsei University, Wonju-si, 26426, Republic of Korea
| | - Tae Gwan Jang
- Medical Intelligence Lab, Wonju College of Medicine, Yonsei University, Wonju-si, 26426, Republic of Korea
| | - So Yeon Park
- Medical Intelligence Lab, Wonju College of Medicine, Yonsei University, Wonju-si, 26426, Republic of Korea
| | - Ha Young Park
- Medical Intelligence Lab, Wonju College of Medicine, Yonsei University, Wonju-si, 26426, Republic of Korea
| | - Sung Pil Cho
- MEZOO Co., Ltd., 668 Namwon-ro, Wonju-si, 26442, Republic of Korea
| | - Junghwan Park
- MEZOO Co., Ltd., 668 Namwon-ro, Wonju-si, 26442, Republic of Korea
| | - Sang-Ha Kim
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Internal Medicine, Wonju Severance Christian Hospital, Wonju-si, 26426, Republic of Korea
| | - Erdenebayar Urtnasan
- Medical Intelligence Lab, Wonju College of Medicine, Yonsei University, Wonju-si, 26426, Republic of Korea
- Artificial Intelligence Big Data Medical Center, Wonju College of Medicine, Yonsei University, Wonju-si, 26426, Republic of Korea
- Yonsei Institute of AI Data Convergence Science, Yonsei University Mirae Campus, Wonju-si, 26493, Republic of Korea
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8
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Raileanu G, de Jong JSSG. Electrocardiogram Interpretation Using Artificial Intelligence: Diagnosis of Cardiac and Extracardiac Pathologic Conditions. How Far Has Machine Learning Reached? Curr Probl Cardiol 2024; 49:102097. [PMID: 37739276 DOI: 10.1016/j.cpcardiol.2023.102097] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 09/18/2023] [Indexed: 09/24/2023]
Abstract
Artificial intelligence (AI) is already widely used in different fields of medicine, making possible the integration of the paraclinical exams with the clinical findings in patients, for a more accurate and rapid diagnosis and treatment decision. The electrocardiogram remains one of the most important, fastest, cheapest, and noninvasive methods of diagnosis in cardiology, despite the rapid development and progression of the technology. Even if studied a long time ago, it still has a lot of less understood features that, with a better understanding, can give more clues to a correct and prompt diagnosis in a short time. The use of AI in the interpretation of the ECG improved the accuracy and the time to diagnosis in different cardiovascular diseases, and more than this, explaining the decision to make AI diagnosis improved the human understanding of the different features of the ECG that might be considered for a more accurate diagnosis. The purpose of this article is to provide an overview of the most recently published articles about the use of AI in ECG interpretation.
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Affiliation(s)
- Gabriela Raileanu
- Department of Cardiology, Onze Lieve Vrouwe Gasthuis, Amsterdam, The Netherlands.
| | - Jonas S S G de Jong
- Department of Cardiology, Onze Lieve Vrouwe Gasthuis, Amsterdam, The Netherlands
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9
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Nagaraj J, Leema A. Light weight multi-branch network-based extraction and classification of myocardial infarction from 12 lead electrocardiogram images. THE IMAGING SCIENCE JOURNAL 2023. [DOI: 10.1080/13682199.2023.2178608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Affiliation(s)
- Jothiaruna Nagaraj
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Anny Leema
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
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10
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Automatic diagnosis of cardiovascular diseases using wavelet feature extraction and convolutional capsule network. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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11
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Wang Z, Stavrakis S, Yao B. Hierarchical deep learning with Generative Adversarial Network for automatic cardiac diagnosis from ECG signals. Comput Biol Med 2023; 155:106641. [PMID: 36773553 DOI: 10.1016/j.compbiomed.2023.106641] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 01/11/2023] [Accepted: 02/05/2023] [Indexed: 02/10/2023]
Abstract
Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is critical to timely medical treatment to save patients' lives. Routine use of the electrocardiogram (ECG) is the most common method for physicians to assess the cardiac electrical activities and detect possible abnormal conditions. Fully utilizing the ECG data for reliable heart disease detection depends on developing effective analytical models. In this paper, we propose a two-level hierarchical deep learning framework with Generative Adversarial Network (GAN) for ECG signal analysis. The first-level model is composed of a Memory-Augmented Deep AutoEncoder with GAN (MadeGAN), which aims to differentiate abnormal signals from normal ECGs for anomaly detection. The second-level learning aims at robust multi-class classification for different arrhythmia identification, which is achieved by integrating the transfer learning technique to transfer knowledge from the first-level learning with the multi-branching architecture to handle the data-lacking and imbalanced data issues. We evaluate the performance of the proposed framework using real-world ECG data from the MIT-BIH arrhythmia database. Experimental results show that our proposed model outperforms existing methods that are commonly used in current practice.
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Affiliation(s)
- Zekai Wang
- Department of Industrial & Systems Engineering, The University of Tennessee, Knoxville, TN, 37996, USA
| | - Stavros Stavrakis
- University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Bing Yao
- Department of Industrial & Systems Engineering, The University of Tennessee, Knoxville, TN, 37996, USA.
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12
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Chadaga K, Prabhu S, Sampathila N, Nireshwalya S, Katta SS, Tan RS, Acharya UR. Application of Artificial Intelligence Techniques for Monkeypox: A Systematic Review. Diagnostics (Basel) 2023; 13:824. [PMID: 36899968 PMCID: PMC10000611 DOI: 10.3390/diagnostics13050824] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 02/24/2023] Open
Abstract
Monkeypox or Mpox is an infectious virus predominantly found in Africa. It has spread to many countries since its latest outbreak. Symptoms such as headaches, chills, and fever are observed in humans. Lumps and rashes also appear on the skin (similar to smallpox, measles, and chickenpox). Many artificial intelligence (AI) models have been developed for accurate and early diagnosis. In this work, we systematically reviewed recent studies that used AI for mpox-related research. After a literature search, 34 studies fulfilling prespecified criteria were selected with the following subject categories: diagnostic testing of mpox, epidemiological modeling of mpox infection spread, drug and vaccine discovery, and media risk management. In the beginning, mpox detection using AI and various modalities was described. Other applications of ML and DL in mitigating mpox were categorized later. The various machine and deep learning algorithms used in the studies and their performance were discussed. We believe that a state-of-the-art review will be a valuable resource for researchers and data scientists in developing measures to counter the mpox virus and its spread.
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Affiliation(s)
- Krishnaraj Chadaga
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - Srikanth Prabhu
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - Niranjana Sampathila
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - Sumith Nireshwalya
- Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - Swathi S. Katta
- Manipal Institute of Management, Manipal Academy of Higher Education, Manipal 576104, India
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore 168752, Singapore
- Duke-NUS Medical School, Singapore 169857, Singapore
| | - U. Rajendra Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, Singapore 599489, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 599494, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 40444, Taiwan
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13
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Auto-MyIn: Automatic diagnosis of myocardial infarction via multiple GLCMs, CNNs, and SVMs. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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14
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Denysyuk HV, Pinto RJ, Silva PM, Duarte RP, Marinho FA, Pimenta L, Gouveia AJ, Gonçalves NJ, Coelho PJ, Zdravevski E, Lameski P, Leithardt V, Garcia NM, Pires IM. Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review. Heliyon 2023; 9:e13601. [PMID: 36852052 PMCID: PMC9958295 DOI: 10.1016/j.heliyon.2023.e13601] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 01/31/2023] [Accepted: 02/05/2023] [Indexed: 02/12/2023] Open
Abstract
The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient's autonomy.
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Key Words
- AI, Artificial Intelligence
- BNN, Binarized Neural Network
- CNN, Concolutional Neural Networks
- Cardiovascular diseases
- DL, Deep Learning
- DNN, Deep Neural Networks
- Diagnosis
- ECG sensors
- ECG, Electrocardiography
- GAN, Generative Adversarial Networks
- GMM, Gaussian Mixture Model
- GNB, Gaussian Naive bayes
- GRU, Gated Recurrent Unit
- LASSO, Least Absolute Shrinkage and Selection Operator
- LDA, Linear Discriminant Analysis
- LR, Linear Regression
- LSTM, Long Short-Term Memory
- ML, Machine Learning
- MLP, Multiplayer Perceptron
- MLR, Multiple Linear Regression
- NLP, Natural Language Processing
- POAF, Postoperative Atrial Fibrillation
- RF, Random Forest
- RNN, Recurrent Neural Network
- SHAP, SHapley Additive exPlanations
- SVM, Support Vector Machine
- Systematic review
- WHO, World Health Organization
- kNN, k-nearest neighbors
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Affiliation(s)
| | - Rui João Pinto
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Pedro Miguel Silva
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Rui Pedro Duarte
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Francisco Alexandre Marinho
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Luís Pimenta
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - António Jorge Gouveia
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Norberto Jorge Gonçalves
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Paulo Jorge Coelho
- Polytechnic of Leiria, Leiria, Portugal
- Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra), Coimbra, Portugal
| | - Eftim Zdravevski
- Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, Macedonia
| | - Petre Lameski
- Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, Macedonia
| | - Valderi Leithardt
- VALORIZA, Research Center for Endogenous Resources Valorization, Instituto Politécnico de Portalegre, 7300-555 Portalegre, Portugal
- COPELABS, Universidade Lusófona de Humanidades e Tecnologias, Lisboa, Portugal
| | - Nuno M. Garcia
- Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
| | - Ivan Miguel Pires
- Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
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15
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Endoscopy, video capsule endoscopy, and biopsy for automated celiac disease detection: A review. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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16
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Multilevel hybrid accurate handcrafted model for myocardial infarction classification using ECG signals. INT J MACH LEARN CYB 2022; 14:1651-1668. [PMID: 36467277 PMCID: PMC9702788 DOI: 10.1007/s13042-022-01718-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 11/03/2022] [Indexed: 11/29/2022]
Abstract
Myocardial infarction (MI) is detected using electrocardiography (ECG) signals. Machine learning (ML) models have been used for automated MI detection on ECG signals. Deep learning models generally yield high classification performance but are computationally intensive. We have developed a novel multilevel hybrid feature extraction-based classification model with low time complexity for MI classification. The study dataset comprising 12-lead ECGs belonging to one healthy and 10 MI classes were downloaded from a public ECG signal databank. The model architecture comprised multilevel hybrid feature extraction, iterative feature selection, classification, and iterative majority voting (IMV). In the hybrid handcrafted feature (HHF) generation phase, both textural and statistical feature extraction functions were used to extract features from ECG beats but only at a low level. A new pooling-based multilevel decomposition model was presented to enable them to create features at a high level. This model used average and maximum pooling to create decomposed signals. Using these pooling functions, an unbalanced tree was obtained. Therefore, this model was named multilevel unbalanced pooling tree transformation (MUPTT). On the feature extraction side, two extractors (functions) were used to generate both statistical and textural features. To generate statistical features, 20 commonly used moments were used. A new, improved symmetric binary pattern function was proposed to generate textural features. Both feature extractors were applied to the original MI signal and the decomposed signals generated by the MUPTT. The most valuable features from among the extracted feature vectors were selected using iterative neighborhood component analysis (INCA). In the classification phase, a one-dimensional nearest neighbor classifier with ten-fold cross-validation was used to obtain lead-wise results. The computed lead-wise results derived from all 12 leads of the same beat were input to the IMV algorithm to generate ten voted results. The most representative was chosen using a greedy technique to calculate the overall classification performance of the model. The HHF-MUPTT-based ECG beat classification model attained excellent performance, with the best lead-wise accuracy of 99.85% observed in Lead III and 99.94% classification accuracy using the IMV algorithm. The results confirmed the high MI classification ability of the presented computationally lightweight HHF-MUPTT-based model.
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17
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Huang JD, Wang J, Ramsey E, Leavey G, Chico TJA, Condell J. Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:8002. [PMID: 36298352 PMCID: PMC9610988 DOI: 10.3390/s22208002] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/06/2022] [Accepted: 10/13/2022] [Indexed: 06/06/2023]
Abstract
Cardiovascular disease (CVD) is the world's leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face.
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Affiliation(s)
- Jian-Dong Huang
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Jinling Wang
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Elaine Ramsey
- Department of Global Business & Enterprise, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Gerard Leavey
- School of Psychology, Ulster University at Coleraine, Londonderry BT52 1SA, UK
| | - Timothy J. A. Chico
- Department of Infection, Immunity and Cardiovascular Disease, The Medical School, The University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK
| | - Joan Condell
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
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18
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Chou L, Liu J, Gong S, Chou Y. A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree. Front Physiol 2022; 13:1008111. [PMID: 36311226 PMCID: PMC9614148 DOI: 10.3389/fphys.2022.1008111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 09/23/2022] [Indexed: 01/11/2023] Open
Abstract
Extreme bradycardia (EB), extreme tachycardia (ET), ventricular tachycardia (VT), and ventricular flutter (VF) are the four types of life-threatening arrhythmias, which are symptoms of cardiovascular diseases. Therefore, in this study, a method of life-threatening arrhythmia recognition is proposed based on pulse rate variability (PRV). First, noise and interference are wiped out from the arterial blood pressure (ABP), and the PRV signal is extracted. Then, 19 features are extracted from the PRV signal, and 15 features with highly important and significant variation were selected by random forest (RF). Finally, the back-propagation neural network (BPNN), extreme learning machine (ELM), and decision tree (DT) are used to build, train, and test classifiers to detect life-threatening arrhythmias. The experimental data are obtained from the MIMIC/Fantasia and the 2015 Physiology Net/CinC Challenge databases. The experimental results show that the DT classifier has the best average performance with accuracy and kappa coefficient (kappa) of 98.76 ± 0.08% and 97.59 ± 0.15%, which are higher than those of the BPNN (accuracy = 94.85 ± 1.33% and kappa = 89.95 ± 2.62%) and ELM (accuracy = 95.05 ± 0.14% and kappa = 90.28 ± 0.28%) classifiers. The proposed method shows better performance in identifying four life-threatening arrhythmias compared to existing methods and has potential to be used for home monitoring of patients with life-threatening arrhythmias.
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Affiliation(s)
- Lijuan Chou
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, China,School of Computer and Information Technology, Northeast Petroleum University, Daqing, China
| | - Jicheng Liu
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, China
| | - Shengrong Gong
- School of Computer and Information Technology, Northeast Petroleum University, Daqing, China,School of Computer Science and Engineering, Changshu Institute of Technology, Suzhou, China
| | - Yongxin Chou
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, China,*Correspondence: Yongxin Chou,
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19
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Junaid SB, Imam AA, Balogun AO, De Silva LC, Surakat YA, Kumar G, Abdulkarim M, Shuaibu AN, Garba A, Sahalu Y, Mohammed A, Mohammed TY, Abdulkadir BA, Abba AA, Kakumi NAI, Mahamad S. Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey. Healthcare (Basel) 2022; 10:1940. [PMID: 36292387 PMCID: PMC9601636 DOI: 10.3390/healthcare10101940] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 09/26/2022] [Accepted: 09/28/2022] [Indexed: 11/16/2022] Open
Abstract
In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.
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Affiliation(s)
| | - Abdullahi Abubakar Imam
- School of Digital Science, Universiti Brunei Darussalam, Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei
| | - Abdullateef Oluwagbemiga Balogun
- Department of Computer Science, University of Ilorin, Ilorin 1515, Nigeria
- Department of Computer and Information Science, Universiti Teknologi PETRONAS, Sri Iskandar 32610, Malaysia
| | | | | | - Ganesh Kumar
- Department of Computer and Information Science, Universiti Teknologi PETRONAS, Sri Iskandar 32610, Malaysia
| | - Muhammad Abdulkarim
- Department of Computer Science, Ahmadu Bello University, Zaria 810211, Nigeria
| | - Aliyu Nuhu Shuaibu
- Department of Electrical Engineering, University of Jos, Bauchi Road, Jos 930105, Nigeria
| | - Aliyu Garba
- Department of Computer Science, Ahmadu Bello University, Zaria 810211, Nigeria
| | - Yusra Sahalu
- SEHA Abu Dhabi Health Services Co., Abu Dhabi 109090, United Arab Emirates
| | - Abdullahi Mohammed
- Department of Computer Science, Ahmadu Bello University, Zaria 810211, Nigeria
| | | | | | | | - Nana Aliyu Iliyasu Kakumi
- Patient Care Department, General Ward, Saudi German Hospital Cairo, Taha Hussein Rd, Huckstep, El Nozha, Cairo Governorate 4473303, Egypt
| | - Saipunidzam Mahamad
- Department of Computer and Information Science, Universiti Teknologi PETRONAS, Sri Iskandar 32610, Malaysia
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20
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Hammad M, Chelloug SA, Alkanhel R, Prakash AJ, Muthanna A, Elgendy IA, Pławiak P. Automated Detection of Myocardial Infarction and Heart Conduction Disorders Based on Feature Selection and a Deep Learning Model. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22176503. [PMID: 36080960 PMCID: PMC9460171 DOI: 10.3390/s22176503] [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: 07/19/2022] [Revised: 08/23/2022] [Accepted: 08/27/2022] [Indexed: 05/09/2023]
Abstract
An electrocardiogram (ECG) is an essential piece of medical equipment that helps diagnose various heart-related conditions in patients. An automated diagnostic tool is required to detect significant episodes in long-term ECG records. It is a very challenging task for cardiologists to analyze long-term ECG records in a short time. Therefore, a computer-based diagnosis tool is required to identify crucial episodes. Myocardial infarction (MI) and conduction disorders (CDs), sometimes known as heart blocks, are medical diseases that occur when a coronary artery becomes fully or suddenly stopped or when blood flow in these arteries slows dramatically. As a result, several researchers have utilized deep learning methods for MI and CD detection. However, there are one or more of the following challenges when using deep learning algorithms: (i) struggles with real-life data, (ii) the time after the training phase also requires high processing power, (iii) they are very computationally expensive, requiring large amounts of memory and computational resources, and it is not easy to transfer them to other problems, (iv) they are hard to describe and are not completely understood (black box), and (v) most of the literature is based on the MIT-BIH or PTB databases, which do not cover most of the crucial arrhythmias. This paper proposes a new deep learning approach based on machine learning for detecting MI and CDs using large PTB-XL ECG data. First, all challenging issues of these heart signals have been considered, as the signal data are from different datasets and the data are filtered. After that, the MI and CD signals are fed to the deep learning model to extract the deep features. In addition, a new custom activation function is proposed, which has fast convergence to the regular activation functions. Later, these features are fed to an external classifier, such as a support vector machine (SVM), for detection. The efficiency of the proposed method is demonstrated by the experimental findings, which show that it improves satisfactorily with an overall accuracy of 99.20% when using a CNN for extracting the features with an SVM classifier.
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Affiliation(s)
- Mohamed Hammad
- Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shibin El Kom 32511, Egypt or
| | - Samia Allaoua Chelloug
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
- Correspondence: (S.A.C.); (P.P.)
| | - Reem Alkanhel
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Allam Jaya Prakash
- Department of Electronics and Communication, National Institute of Technology Rourkela, Rourkela 769008, India
| | - Ammar Muthanna
- Department of Applied Probability and Informatics, Peoples’ Friendship University of Russia (RUDN University), 117198 Moscow, Russia
| | - Ibrahim A. Elgendy
- Department of Computer Science, Faculty of Computers and Information, Menoufia University, Shibin El Kom 32511, Egypt
| | - Paweł Pławiak
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Baltycka 5, 44-100 Gliwice, Poland
- Correspondence: (S.A.C.); (P.P.)
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21
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Lin W, Jia S, Chen Y, Shi H, Zhao J, Li Z, Wu Y, Jiang H, Zhang Q, Wang W, Chen Y, Feng C, Xia S. Korotkoff sounds dynamically reflect changes in cardiac function based on deep learning methods. Front Cardiovasc Med 2022; 9:940615. [PMID: 36093170 PMCID: PMC9458936 DOI: 10.3389/fcvm.2022.940615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 07/18/2022] [Indexed: 11/24/2022] Open
Abstract
Korotkoff sounds (K-sounds) have been around for over 100 years and are considered the gold standard for blood pressure (BP) measurement. K-sounds are also unique for the diagnosis and treatment of cardiovascular diseases; however, their efficacy is limited. The incidences of heart failure (HF) are increasing, which necessitate the development of a rapid and convenient pre-hospital screening method. In this review, we propose a deep learning (DL) method and the possibility of using K-methods to predict cardiac function changes for the detection of cardiac dysfunctions.
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Affiliation(s)
- Wenting Lin
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Sixiang Jia
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Yiwen Chen
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Hanning Shi
- Department of Anime and Comics, Hangzhou Normal University, Hangzhou, China
| | - Jianqiang Zhao
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Zhe Li
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Yiteng Wu
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Hangpan Jiang
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Qi Zhang
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Wei Wang
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Yayu Chen
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Chao Feng
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Shudong Xia
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
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22
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Hassannataj Joloudari J, Mojrian S, Nodehi I, Mashmool A, Kiani Zadegan Z, Khanjani Shirkharkolaie S, Alizadehsani R, Tamadon T, Khosravi S, Akbari Kohnehshari M, Hassannatajjeloudari E, Sharifrazi D, Mosavi A, Loh HW, Tan RS, Acharya UR. Application of artificial intelligence techniques for automated detection of myocardial infarction: a review. Physiol Meas 2022; 43. [PMID: 35803247 DOI: 10.1088/1361-6579/ac7fd9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 07/08/2022] [Indexed: 11/11/2022]
Abstract
Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals worldwide. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG and some other biophysical signals, including machine learning (ML) and deep learning (DL) models. The performance of traditional ML methods relies on handcrafted features and manual selection of ECG signals, whereas DL models can automate these tasks. The review observed that deep convolutional neural networks (DCNNs) yielded excellent classification performance for MI diagnosis, which explains why they have become prevalent in recent years. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and some other biophysical signals.
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Affiliation(s)
- Javad Hassannataj Joloudari
- Computer Engineering, University of Birjand, South Khorasan Province, Birjand, Iran, Birjand, South Khorasan, 9717434765, Iran (the Islamic Republic of)
| | - Sanaz Mojrian
- Mazandaran University of Science and Technology, Mazandaran Province, Babol, Danesh 5, No. Sheykh Tabarasi, Iran, Babol, 47166-85635, Iran (the Islamic Republic of)
| | - Issa Nodehi
- University of Qom, Qom, shahid khodakaram blvd، Iran, Qom, Qom, 1519-37195, Iran (the Islamic Republic of)
| | - Amir Mashmool
- University of Geneva, Via del Molo, 65, 16128 Genova GE, Italy, Geneva, Geneva, 16121, ITALY
| | - Zeynab Kiani Zadegan
- University of Birjand, South Khorasan Province, Birjand, Iran, Birjand, 9717434765, Iran (the Islamic Republic of)
| | - Sahar Khanjani Shirkharkolaie
- Mazandaran University of Science and Technology, Mazandaran Province, Babol, Danesh 5, No. Sheykh Tabarasi, Iran, Babol, 47166-85635, Iran (the Islamic Republic of)
| | - Roohallah Alizadehsani
- Deakin University - Geelong Waterfront Campus, IISRI, Geelong, Victoria, 3220, AUSTRALIA
| | - Tahereh Tamadon
- University of Birjand, South Khorasan Province, Birjand, Iran, Birjand, 9717434765, Iran (the Islamic Republic of)
| | - Samiyeh Khosravi
- University of Birjand, South Khorasan Province, Birjand, Iran, Birjand, 9717434765, Iran (the Islamic Republic of)
| | - Mitra Akbari Kohnehshari
- Bu Ali Sina University, QFRQ+V8H District 2, Hamedan, Iran, Hamedan, Hamedan, 6516738695, Iran (the Islamic Republic of)
| | - Edris Hassannatajjeloudari
- Maragheh University of Medical Sciences, 87VG+9J6, Maragheh, East Azerbaijan Province, Iran, Maragheh, East Azerbaijan, 55158-78151, Iran (the Islamic Republic of)
| | - Danial Sharifrazi
- Islamic Azad University Shiraz, Shiraz University, Iran, Shiraz, Fars, 74731-71987, Iran (the Islamic Republic of)
| | - Amir Mosavi
- Faculty of Informatics, Obuda University, Faculty of Informatics, Obuda University, Budapest, Hungary, Budapest, 1034, HUNGARY
| | - Hui Wen Loh
- Singapore University of Social Sciences, SG, Clementi Rd, 463, Singapore 599494, Singapore, 599491, SINGAPORE
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, 5 Hospital Dr, Singapore 169609, Singapore, 168752, SINGAPORE
| | - U Rajendra Acharya
- Electronic Computer Engineering Division, Ngee Ann Polytechnic, 535 Clementi Road, Singapore 599489, Singapore, 599489, SINGAPORE
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23
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Jahmunah V, Ng E, Tan RS, Oh SL, Acharya UR. Explainable detection of myocardial infarction using deep learning models with Grad-CAM technique on ECG signals. Comput Biol Med 2022; 146:105550. [DOI: 10.1016/j.compbiomed.2022.105550] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/19/2022] [Accepted: 04/20/2022] [Indexed: 01/31/2023]
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24
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Kannan S. An automated heart disease prediction approach using linearly support vector regression and stacked linear swarm optimization. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In today’s world, mining and learning applications play an essential role in healthcare sectors and intend to transform all the data into an understandable form. However, the healthcare sectors require an automated disease prediction system for better medical analysis and emphasize better prediction accuracy for evaluation purposes. In this paper, a new automated prediction model based on Linearly Support Vector Regression and Stacked Linear Swarm Optimization (LSVR-SLSO) has been proposed to predict heart disease accurately. Primarily, the features are analyzed in a linear and non-linear manner using LSVR feature learning approaches. The extracted features are then fed into the SLSO model in order to extract the global optimal solutions. These global solutions will reduce the data dimensionality and computational complexity during the evaluation phase. Moreover, the optimal solution facilitates the proposed model to predict heart disease appropriately. The simulation can be carried out through the MATLAB environment by utilizing a publicly available benchmark heart disease dataset. The performance results evident that the proposed LSVR-SLSO model can efficiently predict heart disease with superior accuracy of 98%, precision of 98.76%, and recall of 99.7% when compared with conventional approaches. The better performance of the proposed model will pave the way to act as an effective clinical decision support tool for physicians during an emergency.
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Affiliation(s)
- Sridharan Kannan
- Professor, Department of Computer Science and Engineering, J.K.K. Munirajah College of Technology, Erode, India
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25
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Machine Leaning-Based Optimization Algorithm for Myocardial Injury under High-Intensity Exercise in Track and Field Athletes. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7792958. [PMID: 35586102 PMCID: PMC9110131 DOI: 10.1155/2022/7792958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 04/08/2022] [Accepted: 04/19/2022] [Indexed: 11/29/2022]
Abstract
In order to train at high-intensity, athletics can again cause varying degrees of myocardial damage. Evaluating the balance between exercise myocardial injury and exercise intensity should actively prevent myocardial injury caused by high-intensity athletic training. In this paper, an intelligent optimization algorithm is used to investigate the degree of myocardial injury. The basic idea is to define the measured data and the output of the numerical model as an objective function of the structural parameters, to obtain the structural parameters by finding ways to continuously optimize the objective function to be close to the observed values, and to identify the injury based on the changes in these parameters before and after myocardial injury. The objective function can be defined in various ways, and the myocardial injury optimization algorithm can be chosen. In order to obtain the best computational results, numerical simulations of damage identification are performed using the objective function and three machine learning-based optimization algorithms. The computational results show that the combination of the objective function and the machine learning algorithms provides good accuracy and computational speed in identifying myocardial injury.
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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.
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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
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Xiong P, Lee SMY, Chan G. Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review. Front Cardiovasc Med 2022; 9:860032. [PMID: 35402563 PMCID: PMC8990170 DOI: 10.3389/fcvm.2022.860032] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 02/18/2022] [Indexed: 12/24/2022] Open
Abstract
Myocardial infarction is a common cardiovascular disorder caused by prolonged ischemia, and early diagnosis of myocardial infarction (MI) is critical for lifesaving. ECG is a simple and non-invasive approach in MI detection, localization, diagnosis, and prognosis. Population-based screening with ECG can detect MI early and help prevent it but this method is too labor-intensive and time-consuming to carry out in practice unless artificial intelligence (AI) would be able to reduce the workload. Recent advances in using deep learning (DL) for ECG screening might rekindle this hope. This review aims to take stock of 59 major DL studies applied to the ECG for MI detection and localization published in recent 5 years, covering convolutional neural network (CNN), long short-term memory (LSTM), convolutional recurrent neural network (CRNN), gated recurrent unit (GRU), residual neural network (ResNet), and autoencoder (AE). In this period, CNN obtained the best popularity in both MI detection and localization, and the highest performance has been obtained from CNN and ResNet model. The reported maximum accuracies of the six different methods are all beyond 97%. Considering the usage of different datasets and ECG leads, the network that trained on 12 leads ECG data of PTB database has obtained higher accuracy than that on smaller number leads data of other datasets. In addition, some limitations and challenges of the DL techniques are also discussed in this review.
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Affiliation(s)
- Ping Xiong
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
| | - Simon Ming-Yuen Lee
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
| | - Ging Chan
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
- Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Taipa, Macau SAR, China
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Penso M, Solbiati S, Moccia S, Caiani EG. Decision Support Systems in HF based on Deep Learning Technologies. Curr Heart Fail Rep 2022; 19:38-51. [PMID: 35142985 PMCID: PMC9023383 DOI: 10.1007/s11897-022-00540-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/20/2022] [Indexed: 11/26/2022]
Abstract
Purpose of Review Application of deep learning (DL) is growing in the last years, especially in the healthcare domain. This review presents the current state of DL techniques applied to electronic health record structured data, physiological signals, and imaging modalities for the management of heart failure (HF), focusing in particular on diagnosis, prognosis, and re-hospitalization risk, to explore the level of maturity of DL in this field. Recent Findings DL allows a better integration of different data sources to distillate more accurate outcomes in HF patients, thus resulting in better performance when compared to conventional evaluation methods. While applications in image and signal processing for HF diagnosis have reached very high performance, the application of DL to electronic health records and its multisource data for prediction could still be improved, despite the already promising results. Summary Embracing the current big data era, DL can improve performance compared to conventional techniques and machine learning approaches. DL algorithms have potential to provide more efficient care and improve outcomes of HF patients, although further investigations are needed to overcome current limitations, including results generalizability and transparency and explicability of the evidences supporting the process.
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Affiliation(s)
- Marco Penso
- Department of Electronics, Information and Biomedical Engineering, Politecnico Di Milano, P.zza L. da Vinci 32, 20133, Milan, Italy
- Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Sarah Solbiati
- Department of Electronics, Information and Biomedical Engineering, Politecnico Di Milano, P.zza L. da Vinci 32, 20133, Milan, Italy
- Institute of Electronics, Information Engineering and Telecommunications (IEIIT), Italian National Research Council (CNR), Milan, Italy
| | - Sara Moccia
- The BioRobotics Institute, Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Enrico G Caiani
- Department of Electronics, Information and Biomedical Engineering, Politecnico Di Milano, P.zza L. da Vinci 32, 20133, Milan, Italy.
- Institute of Electronics, Information Engineering and Telecommunications (IEIIT), Italian National Research Council (CNR), Milan, Italy.
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Estimation of coronary artery movement using a non-rigid registration with global-local structure preservation. Comput Biol Med 2021; 141:105125. [PMID: 34952339 DOI: 10.1016/j.compbiomed.2021.105125] [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: 06/15/2021] [Revised: 12/05/2021] [Accepted: 12/05/2021] [Indexed: 11/20/2022]
Abstract
BACKGROUND At present, coronary artery disease (CAD) is the leading cause of death worldwide. Many studies have shown that CAD is strongly associated with the motion characteristics of the coronary arteries. Although cardiovascular imaging technology has been widely used for the diagnosis of CAD, the motion parameters of the heart and coronary arteries cannot be directly calculated from the images. In this paper, we propose a point set registration method with global and local topology constraints to quantify coronary artery movement. METHODS The global constraint is the motion coherence of the point set which enforces the smoothness of the displacement field. The local linear embedding based topological structure and the local feature descriptor i.e., the 3D shape context, are designed to retain the local structure of the point set. We incorporate these constraints into a maximum likelihood framework and derive an expectation-maximization algorithm to obtain the transformation function between the two point sets. The proposed method was compared with four existing algorithms using simulated data and applied to the real data obtained from 4D CT angiograms. RESULTS For the simulation data, the proposed method achieves a lower registration error than the comparison algorithms. For the real data, the proposed method shows that, in most cases, the right coronary artery achieves a larger velocity than the left anterior descending and left circumflex branches, and there are three well-defined velocity peaks, during the cardiac cycle for these branches. CONCLUSION The proposed approach is feasible and effective in quantifying coronary artery movement and thus adds to the diagnostic power of coronary imaging.
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Abstract
Convolutional neural networks (CNNs) are widely used among the various deep learning techniques available because of their superior performance in the fields of computer vision and natural language processing. CNNs can effectively extract the locality and correlation of input data using structures in which convolutional layers are successively applied to the input data. In general, the performance of neural networks has improved as the depth of CNNs has increased. However, an increase in the depth of a CNN is not always accompanied by an increase in the accuracy of the neural network. This is because the gradient vanishing problem may arise, causing the weights of the weighted layers to fail to converge. Accordingly, the gradient flows of the VGGNet, ResNet, SENet, and DenseNet models were analyzed and compared in this study, and the reasons for the differences in the error rate performances of the models were derived.
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