<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Digital signal processing, such as filtering, information extraction, and fusion of various results, is currently an integral part of advanced medical therapies. It is especially important in neurosurgery during deep-brain stimulation procedures. In such procedures, the surgical target is accessed using special electrodes while not being directly visible. This requires very precise identification of brain structures in 3D space throughout the surgery. In the case of deep-brain stimulation surgery for Parkinson’s disease (PD), the target area—the subthalamic nucleus (STN)—is located deep within the brain. It is also very small (just a few millimetres across), which makes this procedure even more difficult. For this reason, various signals are acquired, filtered, and finally fused, to provide the neurosurgeon with the exact location of the target. These signals come from preoperative medical imaging (such as MRI and CT), and from recordings of brain activity carried out during surgery using special brain-implanted electrodes. Using the method described in this paper, it is possible to construct a decision-support system that, during surgery, analyses signals recorded within the patient’s brain and classifies them as recorded within the STN or not. The constructed classifier discriminates signals with a sensitivity of 0.97 and a specificity of 0.96. The described algorithm is currently used for deep-brain stimulation surgeries among PD patients.</p></abstract>

Digital signal processing, such as filtering, information extraction, and fusion of various results, is currently an integral part of advanced medical therapies. It is especially important in neurosurgery during deep-brain stimulation procedures. In such procedures, the surgical target is accessed using special electrodes while not being directly visible. This requires very precise identification of brain structures in 3D space throughout the surgery. In the case of deep-brain stimulation surgery for Parkinson’s disease (PD), the target area—the subthalamic nucleus (STN)—is located deep within the brain. It is also very small (just a few millimetres across), which makes this procedure even more difficult. For this reason, various signals are acquired, filtered, and finally fused, to provide the neurosurgeon with the exact location of the target. These signals come from preoperative medical imaging (such as MRI and CT), and from recordings of brain activity carried out during surgery using special brain-implanted electrodes. Using the method described in this paper, it is possible to construct a decision-support system that, during surgery, analyses signals recorded within the patient’s brain and classifies them as recorded within the STN or not. The constructed classifier discriminates signals with a sensitivity of 0.97 and a specificity of 0.96. The described algorithm is currently used for deep-brain stimulation surgeries among PD patients.

Mathematical Methods of Signal Analysis Applied in Medical Diagnostic

Bioinformatics and Machine Recognition Department

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Mathematical Methods of Signal Analysis Applied in Medical Diagnostic"}

Digital signal processing, such as filtering, information extraction, and fusion of various results, is currently an integral part of advanced medical...

Mathematical Methods of Signal Analysis Applied in Medical Diagnostic

Publié en ligne: 29 sept. 2020

Pages: 449 - 462

Reçu: 14 nov. 2019

Accepté: 29 mai 2020

DOI: https://doi.org/10.34768/amcs-2020-0033

Mots clés
classification, decision support system, signal filtering, data fusion, temporal analysis

© 2020 Konrad A. Ciecierski, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mathematical Methods of Signal Analysis Applied in Medical Diagnostic

Publié en ligne: 29 sept. 2020

Pages: 449 - 462

Reçu: 14 nov. 2019

Accepté: 29 mai 2020

DOI: https://doi.org/10.34768/amcs-2020-0033

Mots clésclassification, decision support system, signal filtering, data fusion, temporal analysis

© 2020 Konrad A. Ciecierski, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
classification, decision support system, signal filtering, data fusion, temporal analysis