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SepaConvNet for Localizing the Subthalamic Nucleus using One Second Micro-Electrode Recordings

Abstract : Micro-electrode recording (MER) is a powerful way of localizing target structures during neurosur-gical procedures such as the implantation of deep brain stimulation electrodes, which is a common treatment for Parkinson's disease and other neurological disorders. While Micro-electrode Recording (MER) provides adjunctive information to guidance assisted by pre-operative imaging, it is not unanimously used in the operating room. The lack of standard use of MER may be in part due to its long duration, which can lead to complications during the operation, or due to high degree of expertise required for their interpretation. Over the past decade, various approaches addressing automating MER analysis for target localization have been proposed, which have mainly focused on feature engineering. While the accuracies obtained are acceptable in certain configurations, one issue with handcrafted MER features is that they do not necessarily capture more subtle differences in MER that could be detected auditorily by an expert neurophysiologist. In this paper, we propose and validate a deep learning-based pipeline for subthalamic nucleus (STN) localization with micro-electrode recordings motivated by the human auditory system. Our proposed Convolutional Neural Network (CNN), referred as SepaConvNet, shows improved accuracy over two comparative networks for locating the STN from one second MER samples.
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Contributor : Maxime Peralta <>
Submitted on : Monday, May 18, 2020 - 12:14:31 PM
Last modification on : Wednesday, June 17, 2020 - 4:24:54 PM


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  • HAL Id : hal-02571558, version 2


Maxime Peralta, Anh Quoc, Antoine Ackaouy, Thibault Martin, Greydon Gilmore, et al.. SepaConvNet for Localizing the Subthalamic Nucleus using One Second Micro-Electrode Recordings. 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society in conjunction with the 43rd, Annual Conference of the Canadian Medical and Biological Engineering Society, Jul 2020, Montreal, Canada. ⟨hal-02571558v2⟩



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