Deep learning for spike detection in deep brain stimulation surgery
Arkadiusz Nowacki Ewelina Kołpa Mateusz Szychiewicz Konrad Ciecierski
Deep brain stimulation (DBS) is a neurosurgical procedure successfully used
to treat conditions such as Parkinson's disease. Electrostimulation, carried
out by implanting electrodes into an identified focus in the brain, makes it
possible to reduce the symptoms of the disease significantly. In this paper, a
method for analyzing recordings of neuronal activity acquired during DBS
neurosurgery using deep learning is presented. We tested using a convolutional
neural network (CNN) for this purpose. Based on the time window, the classifier
assesses whether neuronal activity (spike) is present. The maximum accuracy
value for the classifier was 98.98%, and the area under the receiver operating
characteristic curve (AUC) was 0.9898. The method made it possible to obtain a
classification without using data preprocessing.