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Detection of subthalamic nucleus using novel higher-order spectra features in microelectrode recordings signals

Hosny Mohamed, Zhu Minwei, Gao Wenpeng, Fu Yili

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

704--716

EN

Successful deep brain stimulation surgery for Parkinson’s disease (PD) patients hinges on accurate clustering of the functional regions along the electrode insertion trajectory. Microelectrode recording (MER) is employed as a substantial tool for neuro-navigation and localizing the optimal target, such as the subthalamic nucleus (STN), intraoperatively. MER signals deliver a framework to reveal the underlying characteristics of STN. The motivation behind this work is to explore the application of Higher-order statistics and spectra (HOS) for an automated delineation of the neurophysiological borders of STN using MER signals. Database collected from 21 PD patients were used. Two HOS methods (Bispectrum and cumulant) were exploited to probe non-Gaussian properties of STN region. This is followed by utilizing classifiers, namely K-nearest neighbor, decision tree, Boosting and support vector machine (SVM), 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. Average classification accuracy, sensitivity, specificity, area under the curve and Youden’s J statistics of 94.81%, 96.73%, 92.15%, 0.9444% and 0.8888, respectively, were yielded using SVM with 8 bispectrum and 241 cumulants features. The proposed model can aid the neurosurgeon in STN detection.

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A novel deep recurrent convolutional neural network for subthalamic nucleus localization using local field potential signals

Hosny Mohamed, Zhu Minwei, Su Yixian, Gao Wenpeng, Fu Yili

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 4

1561--1574

EN

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a well-established interventional treatment for improving motor symptoms of patients suffering from Parkinson’s disease (PD). While STN is originally localized using imaging modalities, additional intraoperative guidance such as microelectrode recording (MER) is crucial to refine the final electrode trajectory. Analysis of MER by an experienced neurophysiologist maintains good clinical outcomes, although the procedure requires long duration and jeopardizes the safety of the surgery. Lately, local field potentials (LFP) investigation has inspired the emergence of adaptive DBS and revealed beneficial perception of PD mechanisms. Several studies confronting LFP analysis to detect the anatomical borders of STN, have focused on handcrafted feature engineering, which does not certainly capture delicate differences in LFP. This study gauges the ability of deep learning to exhibit valuable insight into the electrophysiological neural rhythms of STN using LFP. A recurrent convolutional neural network (CNN) strategy is presented, where local features are extracted from LFP signals via CNN, followed by recurrent layers to aggregate the best features for classification. The proposed model outperformed the state-of-the-art techniques, yielding highest average accuracy of 96.79%. This is the first study on the analysis of LFP signals to localize STN using deep recurrent CNN. The developed model has the potential to extract high level biomarkers regarding STN region, which would boost the neurosurgeon’s confidence in adjusting the trajectory intraoperatively for optimal lead implantation. LFP is a robust guidance tool and could be an alternative solution to the current scenario using MER.

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A novel deep LSTM network for artifacts detection in microelectrode recordings

Hosny Mohamed, Zhu Minwei, Gao Wenpeng, Fu Yili

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 3

1052--1063

EN

Microelectrode recording (MER) signals are world-widely used for validating the planned trajectories in the procedure of deep brain stimulation (DBS) surgery to obtain accurate position of electrodes inside the brain structure. Besides, MER signals are important source for studying extracellular neuronal activity and DBS biomarkers, such as, spike clustering and sorting. However, MER signals are prone to several artifacts derived from electrical equipment in the operating room, electrode movement and patient activities, etc., which reduce the signal-to-noise ratio of the MER signals. Therefore, in this paper, we propose a novel deep learning architecture based on long short-term memory (LSTM) network for automatic artifact detection in MER signals. Frequency and time-domain features were extracted from the raw MER signals and fed to the deep LSTM network. A manually annotated MER database obtained from 17 Parkinson's disease (PD) patients were used to validate the proposed architecture. The proposed architecture achieved promising results of 97.49% accuracy, 98.21% sensitivity and 96.87% specificity on an unseen test set. To our best knowledge, this is the first study to use LSTM network for artifacts detection in MER signals. The MER data will be available at http://homepage.hit.edu.cn/wpgao.