Towards closed-loop deep brain stimulation: Decision tree-based Essential Tremor patient's state classifier and tremor reappearance predictor

摘要

Deep Brain Stimulation (DBS) is a surgical procedure to treat some progressive neurological movement disorders, such as Essential Tremor (ET), in an advanced stage. Current FDA-approved DBS systems operate open-loop, i.e., their parameters are unchanged over time. This work develops a Decision Tree (DT) based algorithm that, by using non-invasively measured surface EMG and accelerometer signals as inputs during DBS-OFF periods, classifies the ET patient's state and then predicts when tremor is about to reappear, at which point DBS is turned ON again for a fixed amount of time. The proposed algorithm achieves an overall accuracy of 93.3% and sensitivity of 97.4%, along with 2.9% false alarm rate. Also, the ratio between predicted tremor delay and the actual detected tremor delay is about 0.93, indicating that tremor prediction is very close to the instant where tremor actually reappeared.