Metaimage format1/10/2024 To cope with this problem, reference ECGs were acquired outside the scanner assuming stationarity of the ECG signals for healthy subjects without cardiovascular diseases or arrhythmias.ĮCGs were acquired in different MRI scanners at Otto-von-Guericke University of Magdeburg, Germany, with static magnetic field strengths of 1T, 3T and 7T without imaging, i.e. Another difficulty is the lack of a reference ECG while the subject is inside the MRI scanner, which complicates the validation of the developed algorithms. This goal could be achieved by improving existing signal processing techniques aiming to separate ECG and MHD signal components.ĭue to the high correlation between the ECG and MHD signals and overlapping frequency distributions, a separation of both signals still remains a challenging task to solve. One potential goal for using this dataset would be to increase the diagnostic value of an ECG acquired during MRI exams, which would be especially important for critical care patients or during MRI guided interventions. Several dedicated QRS detection algorithms were developed to cope with this issue. Depending on the characteristics of the MHD signal, QRS detection might be hampered. Another challenge is the detection of the QRS complex. of the P wave, ST segment or the T wave, is not possible. The Hall voltages across the blood vessels lead to blood flow dependent body surface potentials which are superimposing the ECG signals.ĭue to the superposition of the ECG and MHD signals, a detailed and reliable morphological analysis of the ECG during MRI exams, e.g. This potential difference or voltage is referred to as the Hall voltage. The ions accumulate near the vessel’s wall leading to a potential difference across the vessel. This force, which is known as Lorentz force, causes the ions to move perpendicular to the direction of the blood flow and perpendicular to the MR scanner’s static magnetic field. Ions (electrolytes) contained in the blood are moving inside the vessels where they experience a force due to the presence of the MR scanner’s static magnetic field. The physical interaction between the static magnetic field and the pulsatile blood flow, which is caused by the rhythmic action of the heart, results in the magnetohydrodynamic (MHD) effect. All signals were manually annotated.ĭuring an MRI exam, a subject or patient is exposed to a strong static magnetic field which typically ranges from 1T to 3T in clinical scanners and up to 10.5T in research scanners. The 12-lead and 3-lead ECG signals were acquired from different subjects in various MRI scanners with magnetic field strengths ranging from 1T up to 7T. The MHD effect might be a useful signal for extracting further physiological information, e.g. of the P wave, ST segment or the T wave) is prevented. As a consequence, a detailed morphological analysis of the ECG (e.g. The MHD effect, which is caused an interaction of the MRI’s strong static magnetic field and the patient’s blood flow, superimposes the ECG signal during MRI exams. This ECG dataset was acquired in different magnetic resonance imaging (MRI) scanners to study the magnetohydrodynamic (MHD) effect. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Goldberger A, Amaral L, Glass L, Hausdorff J, Ivanov PC, Mark R, Mietus JE, Moody GB, Peng CK, Stanley HE. ![]() Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P.C., Mark, R., Mietus, J.E., Moody, G.B., Peng, C.K. "PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P.
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