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Elfouly T, Alouani A. Harnessing the Heart's Magnetic Field for Advanced Diagnostic Techniques. SENSORS (BASEL, SWITZERLAND) 2024; 24:6017. [PMID: 39338762 PMCID: PMC11435997 DOI: 10.3390/s24186017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2024] [Revised: 09/05/2024] [Accepted: 09/14/2024] [Indexed: 09/30/2024]
Abstract
Heart diseases remain one of the leading causes of morbidity and mortality worldwide, necessitating innovative diagnostic methods for early detection and intervention. An electrocardiogram (ECG) is a well-known technique for the preliminary diagnosis of heart conditions. However, it can not be used for continuous monitoring due to skin irritation. It is well known that every body organ generates a magnetic field, and the heart generates peak amplitudes of about 10 to 100 pT (measured at a distance of about 3 cm above the chest). This poses challenges to capturing such signals. This paper reviews the different techniques used to capture the heart's magnetic signals along with their limitations. In addition, this paper provides a comprehensive review of the different approaches that use the heart-generated magnetic field to diagnose several heart diseases. This research reveals two aspects. First, as a noninvasive tool, the use of the heart's magnetic field signal can lead to more sensitive advanced heart disease diagnosis tools, especially when continuous monitoring is possible and affordable. Second, its current use is limited due to the lack of accurate, affordable, and portable sensing technology.
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Affiliation(s)
- Tarek Elfouly
- Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN 38505, USA
| | - Ali Alouani
- Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN 38505, USA
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Sayad A, Uddin SM, Yao S, Wilson H, Chan J, Zhao H, Donnan G, Davis S, Skafidas E, Yan B, Kwan P. A magnetoimpedance biosensor microfluidic platform for detection of glial fibrillary acidic protein in blood for acute stroke classification. Biosens Bioelectron 2022; 211:114410. [PMID: 35617799 DOI: 10.1016/j.bios.2022.114410] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/15/2022] [Accepted: 05/17/2022] [Indexed: 11/17/2022]
Abstract
Acute stroke is the third leading cause of mortality and disability worldwide. Administration of appropriate therapy for acute stroke is critically dependent on timely classification into either ischemic or hemorrhagic subtypes, which have divergent treatment pathways. The current classification method is based on neuroimaging, which generally requires the transport of the patient to a hospital-based facility unless a mobile stroke unit is available. Plasma glial fibrillary acidic protein (GFAP) level has been identified as a useful blood-based biomarker to differentiate stroke subtypes. However, its conventional immunoassay methods are laboratory-based and time-consuming. Novel approaches for rapid stroke classification near the patients are urgently needed. Here, we report the development and testing of a microfluidic-based magnetoimpedance biosensor platform for measuring GFAP levels. The platform consists of a microfluidic chip for GFAP extraction from a blood sample and a magnetoimpedance (MI) biosensor that employs Dynabeads as a magnetic label to capture the GFAP molecules. We demonstrated the detection of recombinant GFAP protein in phosphate-buffered saline (PBS) and in mouse blood samples (detection limit 0.01 ng/mL) and of physiological GFAP in blood and plasma samples (detection limit 1.0 ng/mL) obtained from acute stroke patients. This detection level is within the range of cut-off levels required for clinical stroke subtype differentiation. This platform has the potential to be incorporated into a small device with further development to assist in the classification of acute stroke patients and clinical decision-making at the point-of-care.
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Affiliation(s)
- Abkar Sayad
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, 3004, Australia.
| | - Shah Mukim Uddin
- Department of Medicine, The University of Melbourne, The Royal Melbourne Hospital, Melbourne, VIC, 3050, Australia.
| | - Scarlett Yao
- Department of Medicine, The University of Melbourne, The Royal Melbourne Hospital, Melbourne, VIC, 3050, Australia.
| | - Harold Wilson
- Department of Medicine, The University of Melbourne, The Royal Melbourne Hospital, Melbourne, VIC, 3050, Australia. https://
| | - Jianxiong Chan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, 3004, Australia; Department of Medicine, The University of Melbourne, The Royal Melbourne Hospital, Melbourne, VIC, 3050, Australia.
| | - Henry Zhao
- Department of Medicine, The University of Melbourne, The Royal Melbourne Hospital, Melbourne, VIC, 3050, Australia; Department of Neurology, Melbourne Brain Centre, Royal Melbourne Hospital, VIC, 3010, Australia.
| | - Geoffrey Donnan
- Department of Medicine, The University of Melbourne, The Royal Melbourne Hospital, Melbourne, VIC, 3050, Australia; Department of Neurology, Melbourne Brain Centre, Royal Melbourne Hospital, VIC, 3010, Australia.
| | - Stephen Davis
- Department of Medicine, The University of Melbourne, The Royal Melbourne Hospital, Melbourne, VIC, 3050, Australia; Department of Neurology, Melbourne Brain Centre, Royal Melbourne Hospital, VIC, 3010, Australia. https://
| | - Efstratios Skafidas
- Department of Medicine, The University of Melbourne, The Royal Melbourne Hospital, Melbourne, VIC, 3050, Australia; Department of Electrical and Electronic Engineering, Melbourne School of Engineering, The University of Melbourne, VIC, 3010, Australia.
| | - Bernard Yan
- Department of Medicine, The University of Melbourne, The Royal Melbourne Hospital, Melbourne, VIC, 3050, Australia; Department of Neurology, Melbourne Brain Centre, Royal Melbourne Hospital, VIC, 3010, Australia.
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, 3004, Australia; Department of Medicine, The University of Melbourne, The Royal Melbourne Hospital, Melbourne, VIC, 3050, Australia; Department of Electrical and Electronic Engineering, Melbourne School of Engineering, The University of Melbourne, VIC, 3010, Australia; Department of Neurology, Melbourne Brain Centre, Royal Melbourne Hospital, VIC, 3010, Australia.
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