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Adapting the listening time for micro-electrode recordings in deep brain stimulation interventions



Deep brain stimulation (DBS) is a common treatment for a variety of neurological disorders which involves the precise placement of electrodes at particular subcortical locations such as the subthalamic nucleus. This placement is often guided by auditory analysis of micro-electrode recordings (MERs) which informs the clinical team as to the anatomic region in which the electrode is currently positioned. Recent automation attempts have lacked flexibility in terms of the amount of signal recorded, not allowing them to collect more signal when higher certainty is needed or less when the anatomy is unambiguous.


We have addressed this problem by evaluating a simple algorithm that allows for MER signal collection to terminate once the underlying model has sufficient confidence. We have parameterized this approach and explored its performance using three underlying models composed of one neural network and two Bayesian extensions of said network.


We have shown that one particular configuration, a Bayesian model of the underlying network’s certainty, outperforms the others and is relatively insensitive to parameterization. Further investigation shows that this model also allows for signals to be classified earlier without increasing the error rate.


We have presented a simple algorithm that records the confidence of an underlying neural network, thus allowing for MER data collection to be terminated early when sufficient confidence is reached. This has the potential to improve the efficiency of DBS electrode implantation by reducing the time required to identify anatomical structures using MERs.

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Data is not available for this study.


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This work was funded by Association France Parkinson.

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Correspondence to John S. H. Baxter.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Thibault Martin is supported through a Doctoral Research Grant from Association France Parkinson. John S.H. Baxter is supported by the Institut des Neurosciences Cliniques de Rennes (INCR) and the Natural Sciences and Engineering Research Council of Canada (NSERC) through the Post-Doctoral Fellowship (PDF) program.

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Martin, T., Gilmore, G., Haegelen, C. et al. Adapting the listening time for micro-electrode recordings in deep brain stimulation interventions. Int J CARS 16, 1371–1379 (2021).

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  • Deep brain stimulation
  • Micro-electrode recordings
  • Deep learning
  • Bayesian models