The role of MER processing pipelines for STN functional identification during DBS surgery: a feature based machine learning approach.

Abstract

<title>Abstract</title> <bold>Background:</bold> Micro-electrode recording (MER) is one of the modalities used to confirm pre-operative planning during Deep Brain Stimulation (DBS) surgery of the subthalamic nucleus (STN) for the symptomatic treatment of Parkinson’s Disease. MER signals have been widely used in combination with machine learning (ML) techniques to improve STN functional localization. However, the impact of data processing and preparation has mostly been overlooked. <bold>Methods:</bold> A total of twenty-four combinations of processing approaches have been implemented with the aim of exploring the impact of data processing pipelines on the performance of feature-based ML classifiers. These comprise four signal artefact treatments, three outlier management procedures, and an option to standardize or not the feature sets. The effects of the implemented pipeline on the classification results were evaluated by training and testing three classifiers, both with and without feature selection. A final fundamental step to explore the feature importance using SHAP approach has also been implemented. <bold>Results:</bold> Improvements in performance metrics have been noticed after implementing approaches to artefact rejection and optimal outlier management, while the preliminary features standardization based on single patient and brain hemisphere data reduce all the performance metrics (accuracy, F1-score, recall, precision and area under the curve (AUC)). Interestingly, feature importance analysis through SHAP approach highlighted a good agreement between features contributing to classification across most of the implemented pipelines. <bold>Conclusions:</bold> Proper identification and rejection of artefacts combined with appropriate outlier management are crucial steps during MER processing pipelines for STN identification, while pre-normalization of features based on data from single patient and brain hemisphere may lead to overall performance degradation. In addition, the SHAP approach may represent an adjunctive useful tool to guide and improve the implementation of future algorithms.

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