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Bohorquez Vargas DF, Leon Ariza HH, Agudelo-Otalora LM, Botero Rosas DA, Moscoso Barrera WD. Portable system for the acquisition of the cardiac electrical signal and the calculation of heart rate variability metrics in real time: Statistical validation (Preprint). JMIR BIOMEDICAL ENGINEERING 2022. [DOI: 10.2196/37453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Grün D, Rudolph F, Gumpfer N, Hannig J, Elsner LK, von Jeinsen B, Hamm CW, Rieth A, Guckert M, Keller T. Identifying Heart Failure in ECG Data With Artificial Intelligence-A Meta-Analysis. Front Digit Health 2021; 2:584555. [PMID: 34713056 PMCID: PMC8521986 DOI: 10.3389/fdgth.2020.584555] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 12/29/2020] [Indexed: 12/21/2022] Open
Abstract
Introduction: Electrocardiography (ECG) is a quick and easily accessible method for diagnosis and screening of cardiovascular diseases including heart failure (HF). Artificial intelligence (AI) can be used for semi-automated ECG analysis. The aim of this evaluation was to provide an overview of AI use in HF detection from ECG signals and to perform a meta-analysis of available studies. Methods and Results: An independent comprehensive search of the PubMed and Google Scholar database was conducted for articles dealing with the ability of AI to predict HF based on ECG signals. Only original articles published in peer-reviewed journals were considered. A total of five reports including 57,027 patients and 579,134 ECG datasets were identified including two sets of patient-level data and three with ECG-based datasets. The AI-processed ECG data yielded areas under the receiver operator characteristics curves between 0.92 and 0.99 to identify HF with higher values in ECG-based datasets. Applying a random-effects model, an sROC of 0.987 was calculated. Using the contingency tables led to diagnostic odds ratios ranging from 3.44 [95% confidence interval (CI) = 3.12–3.76] to 13.61 (95% CI = 13.14–14.08) also with lower values in patient-level datasets. The meta-analysis diagnostic odds ratio was 7.59 (95% CI = 5.85–9.34). Conclusions: The present meta-analysis confirms the ability of AI to predict HF from standard 12-lead ECG signals underlining the potential of such an approach. The observed overestimation of the diagnostic ability in artificial ECG databases compared to patient-level data stipulate the need for robust prospective studies.
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Affiliation(s)
- Dimitri Grün
- Department of Internal Medicine I, Cardiology, Justus-Liebig University Giessen, Giessen, Germany
| | - Felix Rudolph
- Department of Internal Medicine I, Cardiology, Justus-Liebig University Giessen, Giessen, Germany
| | - Nils Gumpfer
- Cognitive Information Systems, KITE - Kompetenzzentrum für Informationstechnologie, Technische Hochschule Mittelhessen - University of Applied Sciences, Friedberg, Germany
| | - Jennifer Hannig
- Cognitive Information Systems, KITE - Kompetenzzentrum für Informationstechnologie, Technische Hochschule Mittelhessen - University of Applied Sciences, Friedberg, Germany
| | - Laura K Elsner
- Department of Internal Medicine I, Cardiology, Justus-Liebig University Giessen, Giessen, Germany
| | - Beatrice von Jeinsen
- Department of Cardiology, Kerckhoff Heart and Thorax Center, Bad Nauheim, Germany
| | - Christian W Hamm
- Department of Internal Medicine I, Cardiology, Justus-Liebig University Giessen, Giessen, Germany.,Department of Cardiology, Kerckhoff Heart and Thorax Center, Bad Nauheim, Germany
| | - Andreas Rieth
- Department of Cardiology, Kerckhoff Heart and Thorax Center, Bad Nauheim, Germany
| | - Michael Guckert
- Cognitive Information Systems, KITE - Kompetenzzentrum für Informationstechnologie, Technische Hochschule Mittelhessen - University of Applied Sciences, Friedberg, Germany.,Department of MND - Mathematik, Naturwissenschaften und Datenverarbeitung, Technische Hochschule Mittelhessen - University of Applied Sciences, Friedberg, Germany
| | - Till Keller
- Department of Internal Medicine I, Cardiology, Justus-Liebig University Giessen, Giessen, Germany.,Department of Cardiology, Kerckhoff Heart and Thorax Center, Bad Nauheim, Germany
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Accurate automated detection of congestive heart failure using eigenvalue decomposition based features extracted from HRV signals. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2018.10.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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Schires E, Georgiou P, Lande TS. Vital Sign Monitoring Through the Back Using an UWB Impulse Radar With Body Coupled Antennas. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2018; 12:292-302. [PMID: 29570057 DOI: 10.1109/tbcas.2018.2799322] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Radar devices can be used in nonintrusive situations to monitor vital sign, through clothes or behind walls. By detecting and extracting body motion linked to physiological activity, accurate simultaneous estimations of both heart rate (HR) and respiration rate (RR) is possible. However, most research to date has focused on front monitoring of superficial motion of the chest. In this paper, body penetration of electromagnetic (EM) wave is investigated to perform back monitoring of human subjects. Using body-coupled antennas and an ultra-wideband (UWB) pulsed radar, in-body monitoring of lungs and heart motion was achieved. An optimised location of measurement in the back of a subject is presented, to enhance signal-to-noise ratio and limit attenuation of reflected radar signals. Phase-based detection techniques are then investigated for back measurements of vital sign, in conjunction with frequency estimation methods that reduce the impact of parasite signals. Finally, an algorithm combining these techniques is presented to allow robust and real-time estimation of both HR and RR. Static and dynamic tests were conducted, and demonstrated the possibility of using this sensor in future health monitoring systems, especially in the form of a smart car seat for driver monitoring.
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A Novel ECG Eigenvalue Detection Algorithm Based on Wavelet Transform. BIOMED RESEARCH INTERNATIONAL 2017; 2017:5168346. [PMID: 28596962 PMCID: PMC5450177 DOI: 10.1155/2017/5168346] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Revised: 02/18/2017] [Accepted: 04/02/2017] [Indexed: 11/18/2022]
Abstract
This study investigated an electrocardiogram (ECG) eigenvalue automatic analysis and detection method; ECG eigenvalues were used to reverse the myocardial action potential in order to achieve automatic detection and diagnosis of heart disease. Firstly, the frequency component of the feature signal was extracted based on the wavelet transform, which could be used to locate the signal feature after the energy integral processing. Secondly, this study established a simultaneous equations model of action potentials of the myocardial membrane, using ECG eigenvalues for regression fitting, in order to accurately obtain the eigenvalue vector of myocardial membrane potential. The experimental results show that the accuracy of ECG eigenvalue recognition is more than 99.27%, and the accuracy rate of detection of heart disease such as myocardial ischemia and heart failure is more than 86.7%.
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