Patient Specific Parkinson's Disease Detection for Adaptive Deep Brain Stimulation

Continuous deep brain stimulation for Parkinson's disease (PD) patients results in side effects and shortening of the pacemaker battery life. This can be remedied using adaptive stimulation. To achieve adaptive DBS, patient customized PD detection is required due to the inconsistency associated with biomarkers across patients and time. This paper proposes the use of patient specific feature extraction together with adaptive support vector machine (SVM) classifiers to create a patient customized detector for PD. The patient specific feature extraction is obtained using the extrema of the ratio between the PD and non-PD spectra bands of each patient as features, while the adaptive SVM classifier adjusts its decision boundary until a suitable model is obtained. This yields individualised features and classifier pairs for each patient. Datasets containing local field potentials of PD patients were used to validate the method. Six of the nine patient datasets tested achieved a classification accuracy greater than 98%. The adaptive detector is suitable for realization on chip.