| Parkinson’s Disease(PD)is a typical long-term neurodegenerative disorder,has debilitating effects on individuals and communities in all parts of the world.A recent study estimated that the economic burden for individuals with PD is nearly $52 billion every year.Therefore,PD is considered as a main health problem.Up to the present time,there is no established cure for PD.Therefore,patients are usually subjected to deep brain stimulation(DBS)surgery to relieve their symptoms and improve their life quality via implanting an electrode to the target in the deep brain.Although it is well demonstrated that precise implantation of the electrode in motor territory inside subthalamic nucleus(STN)region is a key component to achieve optimal therapeutic benefits and avoid counter complexities from stimulation,current employed tools have limitations.Microelectrode recording(MER)can provide rich information about the presence of STN region by capturing the underlying irregularities taking place within each structure along the planned electrode trajectory during implantation.However,MER analysis suffers from anatomic challenges,several types of artifacts and non-stationary complex nature.Besides,visual inspection of MER signals is time consuming and error-prone.Moreover,the critical reliance on human judgment.Thus,it is of importance to develop a computer-aided detection system with the goal of overcoming these limitations.This thesis addresses novel automatic methods to detect the anatomical borders of STN with high accuracy.MER signals are important source for studying extracellular neuronal activity and DBS biomarkers.However,MER is prone to several artifacts derived from various sources,which reduce the signal-to-noise ratio of MER signals.Therefore,a novel architecture based on long short-term memory(LSTM)network for automatic artifact detection in MER signals is proposed.Frequency and time-domain features were extracted from the raw MER signals and fed to the deep LSTM network.The proposed model achieved high accuracy in artifacts detection.Application of higher-order statistics and spectra(HOS)for an automated delineation of the neurophysiological borders of STN using MER signals is explored.Two HOS methods were exploited to probe non-Gaussian properties of STN region.This is followed by utilizing several classifiers,to choose the superior classifier.Comparison of the performance achieved via HOS alongside the state-of-the-art techniques shows that the proposed features are better suited for identifying STN borders and achieve higher results.The proposed model can aid the neurosurgeon in STN detection.Although with traditional machine learning techniques own merits,they might be exposed to some immanent shortcomings related to the model training.Therefore,a new deep learning model based on convolutional neural network(CNN)for automatic delineation of the STN borders is developed.The proposed model does not involve any conventional standardization,feature extraction or selection steps.Machine learning algorithms are often cumbersome and suffer from subjective evaluation.Though,the developed tenlayered CNN model has the capability of extracting substantial features at the convolution stage.Promising results for subject based testing were achieved using the proposed CNN model and outperformed the state-of-the-art feature extraction techniques.Although the utility of MER is currently recommended as a part of the standard STN borders detection practice and analysis of MER by an experienced neurophysiologist maintains good clinical outcomes,preoperative stereotactic planning does not ideally associate with track selection by intraoperative electrophysiology.In addition,the procedure requires long duration and jeopardizes the safety of the surgery due to risks inherent in MER such as intracranial hemorrhage.Lately,local field potentials(LFP)investigation has inspired the emergence of adaptive DBS and revealed beneficial perception of PD mechanisms.Therefore,considering an alternative intraoperative fine-tuning tool based on LFP is paramount.Accordingly,a novel feature extraction strategy to exhibit valuable insight into the neural rhythms of STN using LFP is proposed.A multi-tier system is presented,where local features are extracted from LFP signals via CNN,followed by genetic algorithm technique for feature selection and k-Nearest Neighbor for classification.The proposed model betters five end-to-end deep learning approaches,yielding improvements in accuracy up to 7.47%,8.48%,7.06%,6.93% and 6.86% in comparison to LSTM-CNN,GRU-CNN,Bi LSTM-CNN,VGG16 and Res Net18,respectively.This is the first study on the analysis of LFP signals to localize STN using deep learning.The developed model has the potential to extract high level biomarkers regarding STN region.LFP is a robust guidance tool and could be an alternative solution to the current scenario using MER. |
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