Deep learning for spike detection in deep brain stimulation surgery
This work addresses the need for automated spike detection in neurosurgery to improve treatment for conditions like Parkinson's disease, but it appears incremental as it applies an existing deep learning method to a specific medical domain.
The paper tackled the problem of detecting neuronal spikes in deep brain stimulation surgery recordings using a convolutional neural network, achieving a maximum accuracy of 98.98% and an AUC of 0.9898 without data preprocessing.
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.