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Borisov AV, Syrkina AG, Kuzmin DA, Ryabov VV, Boyko AA, Zaharova O, Zasedatel VS, Kistenev YV. Application of machine learning and laser optical-acoustic spectroscopy to study the profile of exhaled air volatile markers of acute myocardial infarction. J Breath Res 2021; 15. [PMID: 33657535 DOI: 10.1088/1752-7163/abebd4] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 03/03/2021] [Indexed: 11/12/2022]
Abstract
Conventional acute myocardial infarction (AMI) diagnosis is quite accurate and has proved its effectiveness. However, despite this, discovering more operative methods of this disease detection is underway. From this point of view, the application of exhaled air analysis for a similar diagnosis is valuable. The aim of the paper is to research effective machine learning algorithms for the predictive model for AMI diagnosis constructing, using exhaled air spectral data. The target group included 30 patients with primary myocardial infarction. The control group included 42 healthy volunteers. The 'LaserBreeze' laser gas analyzer (Special Technologies Ltd, Russia), based on the dual-channel resonant photoacoustic detector cell and optical parametric oscillator as the laser source, had been used. The pattern recognition approach was applied in the same manner for the set of extracted concentrations of AMI volatile markers and the set of absorption coefficients in a most informative spectral range 2.900 ± 0.125µm. The created predictive model based on the set of absorption coefficients provided 0.86 of the mean values of both the sensitivity and specificity when linear support vector machine (SVM) combined with principal component analysis was used. The created predictive model based on using six volatile AMI markers (C5H12, N2O, NO2, C2H4, CO, CO2) provided 0.82 and 0.93 of the mean values of the sensitivity and specificity, respectively, when linear SVM was used.
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Affiliation(s)
- Alexey V Borisov
- Biophotonics Laboratory, National Research Tomsk State University, Tomsk, Russia
| | - Anna G Syrkina
- Department of Emergency Cardiology, Cardiology Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, Russia
| | - Dmitry A Kuzmin
- Biophysics and Functional Diagnostics Division, Siberian State Medical University, Tomsk,Russia
| | - Vyacheslav V Ryabov
- Department of Emergency Cardiology, Cardiology Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, Russia.,Cardiology Division, Siberian State Medical University, Tomsk, Russia.,Laboratory for Translational Cellular and Molecular Biomedicine, National Research Tomsk State University, Tomsk, Russia
| | - Andrey A Boyko
- Biophotonics Laboratory, National Research Tomsk State University, Tomsk, Russia
| | - Olga Zaharova
- Biophotonics Laboratory, National Research Tomsk State University, Tomsk, Russia
| | | | - Yury V Kistenev
- Biophotonics Laboratory, National Research Tomsk State University, Tomsk, Russia.,Central Research Laboratory, Siberian State Medical University, Tomsk, Russia
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Manappallil RG, Nambiar J. Hyperkalemic Cardiac Arrest in a Patient with Diabetic Ketoacidosis. Indian J Crit Care Med 2020; 24:737-738. [PMID: 33024389 PMCID: PMC7519603 DOI: 10.5005/jp-journals-10071-23526] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Affiliation(s)
- Robin G Manappallil
- Department of Internal Medicine, Baby Memorial Hospital, Calicut, Kerala, India
- Robin G Manappallil, Department of Internal Medicine, Baby Memorial Hospital, Calicut, Kerala, India, Phone: +91 8547753396, e-mail:
| | - Jayasree Nambiar
- Department of Cardiology, Baby Memorial Hospital, Calicut, Kerala, India
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