Toward a closed-loop deep brain stimulation in Parkinson’s disease using local field potential in parkinsonian rat model

Sana Amoozegar | Mohammad Pooyan | Mehrdad Roghani

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URL :   http://research.shahed.ac.ir/WSR/WebPages/Report/PaperView.aspx?PaperID=116871
Date :  2019/09/24
Publish in :    Medical Hypotheses
DOI :  https://doi.org/10.1016/j.mehy.2019.109360
Link :  http://dx.doi.org/10.1016/j.mehy.2019.109360
Keywords :Parkinson’s disease Closed-loop deep brain stimulation Local field potential High frequency features Genetic algorithm Support Vector Machine

Abstract :

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Deep brain stimulation (DBS) is an invasive method used for treating Parkinson’s disease in its advanced stages. Nowadays, the initial adjustment of DBS parameters and their automatic matching proportion to the progression of the disease is viewed as one of the research areas discussed by the researchers, which is called closed-loop DBS. Various studies were conducted regarding finding the signal(s) which reflects different symptoms of the disease. Local Field Potential (LFP) is one of the signals that is suitable for using as feedback, because it can be recorded by the same implemented electrodes for stimulation. The present study aimed to identify the distinguishing features of patients from healthy individuals using LFP signals. Methods: In the present study, LFP was recorded from the rats in sham and parkinsonian model groups. After evaluating the signals in the frequency domain, sixty-six features were extracted from power spectral density of LFPs. The features were classified by Support Vector Machine (SVM) to determine the ability of features for separating parkinsonian rats from healthy ones. Finally, the most effective features were selected for distinguishing between the sham and parkinsonian model groups using a genetic algorithm. Results: The results indicated that the frequency domain features of LFP signals from rats have capacity of using them as a feedback for closed-loop DBS. The accuracy of the Support Vector Machine classification using all 66 features was 80.42 which increased to 84.41 using 38 features selected by genetic algorithm. The proposed method not only increase the accuracy, but it also reduce computation by decreasing the number of the effective features. The results indicate the significant capacity of the proposed method for identifying the effective highfrequency features to control the closed-loop DBS. Conclusions: The ability of using LFP signals as feedback in closed-loop DBS was shown by extracting useful information in frequency bands below and above 100 Hz regarding LFP signals of parkinsonian rats and sham ones. Based on the results, features at frequencies above 100 Hz were more powerful and robust than below 100 Hz. The genetic algorithm was used for optimizing the classification problem.