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Milad Jabbari |
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People
Director Researchers Ph.D. Candidate Students Alumni
 Milad Jabbari was born in Urmia, Iran in 1989. He received the B.Sc. degree in Electrical Engineering-Bioelectric from the Sahand University of Technology (SUT), Tabriz, Iran in 2013 and the M.Sc. degree in Biomedical Engineering-Bioelectric from the Iran University of Science and Technology (IUST), Tehran, Iran, in 2019. He was a Research Assistant at the Iran Neural Technology Centre (INTC) from 2015 to 2019 and a guest reviewer of the journal of Medical Engineering and Physics in 2020. His research interests include biomedical signal processing, computational neuroscience, machine learning, deep neural networks, and intra-spinal cord recording.
Research Activity
Neurological disease or spinal cord injury (SCI) can disrupt the normal function of the bladder, resulting in urinary incontinence or retention. Electrical stimulation is a common effective approach for treatment of such disorders and can be applied continuously or conditionally. Continuous stimulation has some disadvantages such as increasing the risk of tissue damage, habituation of the spinal reflexes, and the possibility of electrode corrosion. To overcome these challenges, conditional stimulation can be utilized in such a way that stimulation pulses are delivered when an impeding bladder contraction is set to be occur or the bladder requires voiding. To inhibit impeding contraction by the conditional stimulation, it is necessary to detect the onset of nascent hyperreflexive contractions. Moreover, estimation of bladder pressure or volume can be used in a close loop control strategy to improve bladder functions in people suffering from neurological disease or SCI. The major aim of this study is to decode the pressure and volume of the bladder from the neural activity in the dorsal horn of the spinal cord.
Publications:
M. Jabbari, and, A. Erfanian, “Estimation of bladder pressure and volume from the neural activity of lumbosacral dorsal horn using a long-short-term-memory-based deep neural network,” Scientific reports, vol. 9(1), pp.1-15, 2019, https://doi.org/10.1038/s41598-019-54144-8
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