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            <title xml:lang="en">Time and frequency analysis of the potentials induced by the switching of the gradients during MRI</title>
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                <forename type="first">Karim</forename>
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            <idno type="halRefHtml">&lt;i&gt;2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)&lt;/i&gt;, Apr 2020, Meknes, France. pp.1-4, &lt;a target="_blank" href="https://dx.doi.org/10.1109/IRASET48871.2020.9092252"&gt;&amp;#x27E8;10.1109/IRASET48871.2020.9092252&amp;#x27E9;&lt;/a&gt;</idno>
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                <title xml:lang="en">Time and frequency analysis of the potentials induced by the switching of the gradients during MRI</title>
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                <term xml:lang="en">Magnetic resonance imaging</term>
                <term xml:lang="en">Support vector machines</term>
                <term xml:lang="en">Electric potential</term>
                <term xml:lang="en">Electrocardiography</term>
                <term xml:lang="en">Spectral analysis</term>
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              <p>During Magnetic Resonance Imaging (MRI), the switching of the magnetic field gradients useful for obtaining images generate in parallel induced voltages which strongly disturb the electrophysiological signals collected simultaneously. In a previous article we presented a bench developed in the laboratory which allows to collect in vitro these “induced potentials” [1], on the other hand, in vivo signals are stored in a database without specifying their categories. This work is devoted to the research of the temporal, statistical and frequency properties of these induced potentials. A stationary study is proposed according to two methods (KPSS and LMC) supplemented by an estimation of the spectral density according to the Welch method. The results show stationary in the weak sense. Significant changes in frequency characteristics appear depending on the imaging sequences and the orientation of the sections. This type of information is to be considered on the one hand in the classification of signals in vivo, and on the other hand, in the design of denoising algorithms in order to improve its robustness.</p>
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