Adaptive control of deep brain stimulator for essential tremor: entropy-based tremor prediction using surface-EMG.

Entropy, as a measure of randomness in time-varying signals, is widely used in areas such as thermodynamics, statistical mechanics and information theory. This paper investigates the use of two commonly employed entropy measures, namely Wavelet Entropy and Approximate Entropy, as a predictor of tremor reappearance in Essential Tremor patients; the predictor input is a raw surface-electromyographic (sEMG) signal measured from tremor affected muscles of patients implanted with a Deep Brain Stimulator (DBS). A combination of both types of entropy measure is shown to successfully predict the occurrence of tremor few seconds before its visual manifestation. This result can potentially lead to a novel sEMG-based adaptive on-off DBS controller that can be added on to existing open-loop DBS systems with minimal changes; an adaptive DBS system provides stimulation only when needed thereby reducing the risk of brain over stimulation, delaying DBS intolerance and prolonging DBS battery life.