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Bispectral Analysis of Parkinsonian Rest Tremor: New Characterization and Classification Insights Pre-/Post-DBS and Medication Treatment

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posted on 19.04.2022, 02:56 by Ioannis ZiogasIoannis Ziogas, Charalampos Lamprou, Leontios J. Hadjileontiadis
Objective: Rest tremor is one of the most common symptoms of Parkinson’s Disease (PD), with diagnosis and severity estimation often being hindered by the subjectivity of clinical methods and limitations of existing screening procedures. Hence, development of methods that can accurately describe properties of PD rest tremor, while accounting for the presence of possible ongoing treatments, such as Deep Brain Stimulation (DBS) and medication, are quite important. Methods: A Higher Order Spectrum (HOS)-based analysis for characterizing and classifying tremor severity and treatment effectiveness, taking into consideration its nonlinear characteristics, is proposed here. Bispectrum- and Bicoherence-based features are extracted from velocity signals recorded from 16 PD patients at their index finger. Two different scenarios are implemented and tested for feature characterization under various conditions of treatment. Moreover, a classification scheme was constructed to evaluate the ability of HOS-based features in accurately predicting the rest tremor level, i.e., Low Amplitude/High Amplitude (LAT/HAT), and the presence of treatment (medication/DBS On-Off). To avoid over-fitting, a leave-one-subject-out cross-validation procedure was adopted. Results: The proposed bispectral analysis resulted in area under the Receiver Operating Characteristics curve (AUC) score of 0.96 (95% Confidence Interval (CI): 0.89-1) with 0.91/0.88 Sensitivity/Specificity, respectively, for the On-Off classification for Medication treatment. For the DBS treatment, the proposed analysis resulted in an AUC score of 0.72 (95% CI: 0.57-0.87), with 0.64/0.65 Sensitivity/Specificity, respectively. For LAT/HAT prediction, the best performing models yield scores of 1/0.93 for AUC/Accuracy metrics, with 1/0.86 Sensitivity/Specificity, respectively. Conclusions: When compared to the existing methods, the proposed methodology outperforms them regarding the prediction of LAT/HAT and treatment effectiveness. Additionally, our HOS-based methodology enables the establishment of new rest tremor classes, based on its nonlinearity and allows for new insights about the dynamic nature of the resting tremor production system. Significance: We propose a method that can effectively recognize rest tremor severity and assess the influence of medication and DBS. This study introduces, for the first time, nonlinearity-based classes for rest tremor and provides an accurate representation of tremor dynamics through HOS.


European Union's Horizon 2020 Research and Innovation Programme under the Grant Agreement No 690494 -- i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions


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Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki

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