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Islam T, Washington P. Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review. BIOSENSORS 2024; 14:183. [PMID: 38667177 PMCID: PMC11048540 DOI: 10.3390/bios14040183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 03/27/2024] [Accepted: 04/01/2024] [Indexed: 04/28/2024]
Abstract
The rapid development of biosensing technologies together with the advent of deep learning has marked an era in healthcare and biomedical research where widespread devices like smartphones, smartwatches, and health-specific technologies have the potential to facilitate remote and accessible diagnosis, monitoring, and adaptive therapy in a naturalistic environment. This systematic review focuses on the impact of combining multiple biosensing techniques with deep learning algorithms and the application of these models to healthcare. We explore the key areas that researchers and engineers must consider when developing a deep learning model for biosensing: the data modality, the model architecture, and the real-world use case for the model. We also discuss key ongoing challenges and potential future directions for research in this field. We aim to provide useful insights for researchers who seek to use intelligent biosensing to advance precision healthcare.
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Gupta U, Paluru N, Nankani D, Kulkarni K, Awasthi N. A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms. Heliyon 2024; 10:e26787. [PMID: 38562492 PMCID: PMC10982903 DOI: 10.1016/j.heliyon.2024.e26787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 02/20/2024] [Indexed: 04/04/2024] Open
Abstract
Deep learning has made many advances in data classification using electrocardiogram (ECG) waveforms. Over the past decade, data science research has focused on developing artificial intelligence (AI) based models that can analyze ECG waveforms to identify and classify abnormal cardiac rhythms accurately. However, the primary drawback of the current AI models is that most of these models are heavy, computationally intensive, and inefficient in terms of cost for real-time implementation. In this review, we first discuss the current state-of-the-art AI models utilized for ECG-based cardiac rhythm classification. Next, we present some of the upcoming modeling methodologies which have the potential to perform real-time implementation of AI-based heart rhythm diagnosis. These models hold significant promise in being lightweight and computationally efficient without compromising the accuracy. Contemporary models predominantly utilize 12-lead ECG for cardiac rhythm classification and cardiovascular status prediction, increasing the computational burden and making real-time implementation challenging. We also summarize research studies evaluating the potential of efficient data setups to reduce the number of ECG leads without affecting classification accuracy. Lastly, we present future perspectives on AI's utility in precision medicine by providing opportunities for accurate prediction and diagnostics of cardiovascular status in patients.
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Affiliation(s)
- Utkarsh Gupta
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, 560012, India
| | - Naveen Paluru
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, 560012, India
| | - Deepankar Nankani
- Department of Computer Science and Engineering, Indian Institute of Technology, Guwahati, Assam, 781039, India
| | - Kanchan Kulkarni
- IHU-LIRYC, Heart Rhythm Disease Institute, Fondation Bordeaux Université, Pessac, Bordeaux, F-33000, France
- University of Bordeaux, INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, F-33000, France
| | - Navchetan Awasthi
- Faculty of Science, Mathematics and Computer Science, Informatics Institute, University of Amsterdam, Amsterdam, 1090 GH, the Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam UMC, Amsterdam, 1081 HV, the Netherlands
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Chaitanya MK, Sharma LD, Rahul J, Sharma D, Roy A. Artificial intelligence based approach for categorization of COVID-19 ECG images in presence of other cardiovascular disorders. Biomed Phys Eng Express 2023; 9. [PMID: 36805304 DOI: 10.1088/2057-1976/acbd53] [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: 11/24/2022] [Accepted: 02/20/2023] [Indexed: 02/22/2023]
Abstract
Coronavirus disease (COVID-19) is a class of SARS-CoV-2 virus which is initially identified in the later half of the year 2019 and then evolved as a pandemic. If it is not identified in the early stage then the infection and mortality rates increase with time. A timely and reliable approach for COVID-19 identification has become important in order to prevent the disease from spreading rapidly. In recent times, many methods have been suggested for the detection of COVID-19 disease have various flaws, to increase diagnosis performance, fresh investigations are required. In this article, automatically diagnosing COVID-19 using ECG images and deep learning approaches like as Visual Geometry Group (VGG) and AlexNet architectures have been proposed. The proposed method is able to classify between COVID-19, myocardial infarction, normal sinus rhythm, and other abnormal heart beats using Lead-II ECG image only. The efficacy of the technique proposed is validated by using a publicly available ECG image database. We have achieved an accuracy of 77.42% using Alexnet model and 75% accuracy with the help of VGG19 model.
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Affiliation(s)
| | | | - Jagdeep Rahul
- Department of Electronics & Communication Engineering, Rajiv Gandhi University, India
| | - Diksha Sharma
- Department of Nanoscience & Technology, Central University of Jharkhand, India
| | - Amarjit Roy
- Department of Electrical Engineering, Ghani Khan Choudhury Institute of Engineering and Technology, India
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