An online movement and tremor identification algorithm for evaluation during deep brain stimulation

Abstract INTRODUCTION: Deep brain stimulation (DBS) is widely used to alleviate symptoms of movement disorders. During intraoperative the influence active or passive movements on neuronal activity often evaluated but evaluation remains mostly subjective. The objective this paper investigate potential a previously developed Weighted-frequency Fourier Linear combiner and Kalman filter-based recursive algorithm identify tremor phases types. METHODS: Ten accelerometer recordings from eight patients were acquired during DBS which 186 manually annotated into: rest, postural kinetic phase without tremor, with tremor. method first estimates instantaneous frequency then decomposes motion signal into voluntary tremorous parts. part quantify differentiate phases. RESULTS: Instantaneous amplitude are successfully tracked online. overall accuracy for only 89.1% 76.3% when also non-tremorous considered. Two main misclassification cases identified further discussed. CONCLUSION: results demonstrate as an online classifier. It would benefit more advanced detector nevertheless obtained digital biomarkers offer evidence-based analysis could optimize efficacy treatment.