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Prats-Boluda G, Martinez-de-Juan JL, Nieto-Del-Amor F, Termenon M, Varón C, Ye-Lin Y. Vectorgastrogram: dynamic trajectory and recurrence quantification analysis to assess slow wave vector movement in healthy subjects. Phys Eng Sci Med 2024; 47:663-677. [PMID: 38436885 DOI: 10.1007/s13246-024-01396-y] [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: 04/12/2023] [Accepted: 01/21/2024] [Indexed: 03/05/2024]
Abstract
Functional gastric disorders entail chronic or recurrent symptoms, high prevalence and a significant financial burden. These disorders do not always involve structural abnormalities and since they cannot be diagnosed by routine procedures, electrogastrography (EGG) has been proposed as a diagnostic alternative. However, the method still has not been transferred to clinical practice due to the difficulty of identifying gastric activity because of the low-frequency interference caused by skin-electrode contact potential in obtaining spatiotemporal information by simple procedures. This work attempted to robustly identify the gastric slow wave (SW) main components by applying multivariate variational mode decomposition (MVMD) to the multichannel EGG. Another aim was to obtain the 2D SW vectorgastrogram VGGSW from 4 electrodes perpendicularly arranged in a T-shape and analyse its dynamic trajectory and recurrence quantification (RQA) to assess slow wave vector movement in healthy subjects. The results revealed that MVMD can reliably identify the gastric SW, with detection rates over 91% in fasting postprandial subjects and a frequency instability of less than 5.3%, statistically increasing its amplitude and frequency after ingestion. The VGGSW dynamic trajectory showed a statistically higher predominance of vertical displacement after ingestion. RQA metrics (recurrence ratio, average length, entropy, and trapping time) showed a postprandial statistical increase, suggesting that gastric SW became more intense and coordinated with a less complex VGGSW and higher periodicity. The results support the VGGSW as a simple technique that can provide relevant information on the "global" spatial pattern of gastric slow wave propagation that could help diagnose gastric pathologies.
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Affiliation(s)
- Gema Prats-Boluda
- Centro de Investigación e Innovación en Bioingeniería (CI2B), Universitat Politècnica de València (UPV), Valencia, Spain.
| | - Jose L Martinez-de-Juan
- Centro de Investigación e Innovación en Bioingeniería (CI2B), Universitat Politècnica de València (UPV), Valencia, Spain
| | - Felix Nieto-Del-Amor
- Centro de Investigación e Innovación en Bioingeniería (CI2B), Universitat Politècnica de València (UPV), Valencia, Spain
| | - María Termenon
- Centro de Investigación e Innovación en Bioingeniería (CI2B), Universitat Politècnica de València (UPV), Valencia, Spain
| | - Cristina Varón
- Centro de Investigación e Innovación en Bioingeniería (CI2B), Universitat Politècnica de València (UPV), Valencia, Spain
| | - Yiyao Ye-Lin
- Centro de Investigación e Innovación en Bioingeniería (CI2B), Universitat Politècnica de València (UPV), Valencia, Spain
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Dahlberg P, Axelsson KJ, Rydberg A, Lundahl G, Gransberg L, Bergfeldt L. Spatiotemporal repolarization dispersion before and after exercise in patients with long QT syndrome type 1 versus controls: probing into the arrhythmia substrate. Am J Physiol Heart Circ Physiol 2023; 325:H1279-H1289. [PMID: 37773058 DOI: 10.1152/ajpheart.00335.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 09/21/2023] [Accepted: 09/22/2023] [Indexed: 09/30/2023]
Abstract
Congenital long QT syndrome (LQTS) carries an increased risk for syncope and sudden death. QT prolongation promotes ventricular extrasystoles, which, in the presence of an arrhythmia substrate, might trigger ventricular tachycardia degenerating into fibrillation. Increased electrical heterogeneity (dispersion) is the suggested arrhythmia substrate in LQTS. In the most common subtype LQT1, physical exercise predisposes for arrhythmia and spatiotemporal dispersion was therefore studied in this context. Thirty-seven patients (57% on β-blockers) and 37 healthy controls (mean age, 31 vs. 35; range, 6-68 vs. 6-72 yr) performed an exercise test. Frank vectorcardiography was used to assess spatiotemporal dispersion as Tampl, Tarea, the ventricular gradient (VG), and the Tpeak-end interval from 10-s signal averages before and 7 ± 2 min after exercise; during exercise too much signal disturbance excluded analysis. Baseline and maximum heart rates as well as estimated exercise intensity were similar, but heart rate recovery was slower in patients. At baseline, QT and heart rate-corrected QT (QTcB) were significantly longer in patients (as expected), whereas dispersion parameters were numerically larger in controls. After exercise, QTpeakcB and Tpeak-endcB increased significantly more in patients (18 ± 23 vs. 7 ± 10 ms and 12 ± 17 vs. 2 ± 6 ms; P < 0.001 and P < 0.01). There was, however, no difference in the change in Tampl, Tarea, and VG between groups. In conclusion, although temporal dispersion of repolarization increased significantly more after exercise in patients with LQT1, there were no signs of exercise-induced increase in global dispersion of action potential duration and morphology. The arrhythmia substrate/mechanism in LQT1 warrants further study.NEW & NOTEWORTHY Physical activity increases the risk for life-threatening arrhythmias in LQTS type 1 (LQT1). The arrhythmia substrate is presumably altered electrical heterogeneity (a.k.a. dispersion). Spatiotemporal dispersion parameters were therefore compared before and after exercise in patients versus healthy controls using Frank vectorcardiography, a novelty. Physical exercise prolonged the time between the earliest and latest complete repolarization in patients versus controls, but did not increase parameters reflecting global dispersion of action potential duration and morphology, another novelty.
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Affiliation(s)
- Pia Dahlberg
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Region Vaestra Goetaland, Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Karl-Jonas Axelsson
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Region Vaestra Goetaland, Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Annika Rydberg
- Division of Pediatrics, Department of Clinical Sciences, Umeå University, Umeå, Sweden
| | - Gunilla Lundahl
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Lennart Gransberg
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Lennart Bergfeldt
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Region Vaestra Goetaland, Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden
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Martinek R, Ladrova M, Sidikova M, Jaros R, Behbehani K, Kahankova R, Kawala-Sterniuk A. Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part I: Cardiac Signals. SENSORS 2021; 21:s21155186. [PMID: 34372424 PMCID: PMC8346990 DOI: 10.3390/s21155186] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 07/23/2021] [Accepted: 07/26/2021] [Indexed: 11/30/2022]
Abstract
Advanced signal processing methods are one of the fastest developing scientific and technical areas of biomedical engineering with increasing usage in current clinical practice. This paper presents an extensive literature review of the methods for the digital signal processing of cardiac bioelectrical signals that are commonly applied in today’s clinical practice. This work covers the definition of bioelectrical signals. It also covers to the extreme extent of classical and advanced approaches to the alleviation of noise contamination such as digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation and wavelet transform.
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Affiliation(s)
- Radek Martinek
- FEECS, Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava, 708 00 Ostrava, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
- Correspondence: (R.M.); (A.K.-S.)
| | - Martina Ladrova
- FEECS, Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava, 708 00 Ostrava, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Michaela Sidikova
- FEECS, Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava, 708 00 Ostrava, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Rene Jaros
- FEECS, Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava, 708 00 Ostrava, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Khosrow Behbehani
- College of Engineering, The University of Texas in Arlington, Arlington, TX 76019, USA;
| | - Radana Kahankova
- FEECS, Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava, 708 00 Ostrava, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
- Correspondence: (R.M.); (A.K.-S.)
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Ladrova M, Martinek R, Nedoma J, Hanzlikova P, Nelson MD, Kahankova R, Brablik J, Kolarik J. Monitoring and Synchronization of Cardiac and Respiratory Traces in Magnetic Resonance Imaging: A Review. IEEE Rev Biomed Eng 2021; 15:200-221. [PMID: 33513108 DOI: 10.1109/rbme.2021.3055550] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Synchronization of human vital signs, namely the cardiac cycle and respiratory excursions, is necessary during magnetic resonance imaging of the cardiovascular system and the abdominal cavity to achieve optimal image quality with minimized artifacts. This review summarizes techniques currently available in clinical practice, as well as methods under development, outlines the benefits and disadvantages of each approach, and offers some unique solutions for consideration.
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Jaros R, Martinek R, Danys L. Comparison of Different Electrocardiography with Vectorcardiography Transformations. SENSORS (BASEL, SWITZERLAND) 2019; 19:E3072. [PMID: 31336798 PMCID: PMC6678609 DOI: 10.3390/s19143072] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 07/04/2019] [Accepted: 07/09/2019] [Indexed: 12/01/2022]
Abstract
This paper deals with transformations from electrocardiographic (ECG) to vectorcardiographic (VCG) leads. VCG provides better sensitivity, for example for the detection of myocardial infarction, ischemia, and hypertrophy. However, in clinical practice, measurement of VCG is not usually used because it requires additional electrodes placed on the patient's body. Instead, mathematical transformations are used for deriving VCG from 12-leads ECG. In this work, Kors quasi-orthogonal transformation, inverse Dower transformation, Kors regression transformation, and linear regression-based transformations for deriving P wave (PLSV) and QRS complex (QLSV) are implemented and compared. These transformation methods were not yet compared before, so we have selected them for this paper. Transformation methods were compared for the data from the Physikalisch-Technische Bundesanstalt (PTB) database and their accuracy was evaluated using a mean squared error (MSE) and a correlation coefficient (R) between the derived and directly measured Frank's leads. Based on the statistical analysis, Kors regression transformation was significantly more accurate for the derivation of the X and Y leads than the others. For the Z lead, there were no statistically significant differences in the medians between Kors regression transformation and the PLSV and QLSV methods. This paper thoroughly compared multiple VCG transformation methods to conventional VCG Frank's orthogonal lead system, used in clinical practice.
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Affiliation(s)
- Rene Jaros
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu 15, 708 33 Ostrava, Czech Republic.
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu 15, 708 33 Ostrava, Czech Republic.
| | - Lukas Danys
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu 15, 708 33 Ostrava, Czech Republic
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Prolonged Tpeak-Tend interval is associated with ventricular fibrillation during reperfusion in ST-elevation myocardial infarction. Int J Cardiol 2019; 280:80-83. [DOI: 10.1016/j.ijcard.2019.01.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 12/04/2018] [Accepted: 01/02/2019] [Indexed: 11/22/2022]
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Lingman M, Hartford M, Karlsson T, Herlitz J, Rubulis A, Caidahl K, Bergfeldt L. Value of the QRS-T area angle in improving the prediction of sudden cardiac death after acute coronary syndromes. Int J Cardiol 2016; 218:1-11. [DOI: 10.1016/j.ijcard.2016.05.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2016] [Revised: 04/27/2016] [Accepted: 05/01/2016] [Indexed: 10/21/2022]
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Hasan MA, Abbott D. A review of beat-to-beat vectorcardiographic (VCG) parameters for analyzing repolarization variability in ECG signals. ACTA ACUST UNITED AC 2016; 61:3-17. [DOI: 10.1515/bmt-2015-0005] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2014] [Accepted: 04/17/2015] [Indexed: 11/15/2022]
Abstract
AbstractElevated ventricular repolarization lability is believed to be linked to the risk of ventricular tachycardia/ventricular fibrillation. However, ventricular repolarization is a complex electrical phenomenon, and abnormalities in ventricular repolarization are not completely understood. To evaluate repolarization lability, vectorcardiography (VCG) is an alternative approach where the electrocardiographic (ECG) signal can be considered as possessing both magnitude and direction. Recent research has shown that VCG is advantageous over ECG signal analysis for identification of repolarization abnormality. One of the key reasons is that the VCG approach does not rely on exact identification of the T-wave offset, which improves the reproducibility of the VCG technique. However, beat-to-beat variability in VCG is an emerging area for the investigation of repolarization abnormality though not yet fully realized. Therefore, the purpose of this review is to explore the techniques, findings, and efficacy of beat-to-beat VCG parameters for analyzing repolarization lability, which may have potential utility for further study.
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Vozda M, Cerny M. Methods for derivation of orthogonal leads from 12-lead electrocardiogram: A review. Biomed Signal Process Control 2015. [DOI: 10.1016/j.bspc.2015.03.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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10
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Pahlm O, Wagner GS. QRS, ST and T changes of acute transmural myocardial ischemia: Overview editorial. J Electrocardiol 2014; 47:397-401. [DOI: 10.1016/j.jelectrocard.2014.05.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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