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Paper details
Number 3 - September 2020
Volume 30 - 2020
Mathematical methods of signal analysis applied in medical diagnostic
Konrad A. Ciecierski
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.
Keywords
classification, decision support system, signal filtering, data fusion, temporal analysis