New Methodology for Attack Patterns Classification in Deep Brain Stimulation

Deep brain stimulation is a surgical treatment using implantable devices to generate electrical impulses and alleviate motor symptoms in Parkinson’s disease, primarily the resting tremor. Recently, deep learning (DL)–based approaches have provided incredible benefits for a wide range of problems, including classification, segmentation, and feature engineering. Although DL-based techniques performed very well in the classification tasks, they remain challenged in handling time series data (i.e., rest tremor velocity signals recorded from patients suffering from PD). Multiple issues persevere with time-series data, including difficulties in extracting relevant features, heavily weighted data, etc. This research aims to develop a hybrid model for classifying different attack types in deep brain implants using a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). Our model autonomously extracts features from raw data with minimal preprocessing. Because of the advantages of CNN and BiLSTM, they can learn both high-level features and long-term dependencies in sequential data. Various model architectures were explored to construct the optimal structure. A comprehensive experimental study has been made in this research, which shows that the proposed approach offers the most efficient tool for accurate classification and ranks at the top of the list of recently published algorithms on the Physionet dataset. Ten-fold cross-validation is carried out. The proposed model achieved accuracy, precision, recall, and an F1-score of 97.69 \(\%\) , 99,62 \(\%\) , 99,62 \(\%\) , 99.5 \(\%\) , and 97.63 \(\%\) , respectively. The proposed model provides a robust tool for the classification of different attack types for deep brain implants.