Current deep brain stimulation paradigms deliver continuous stimulation to deep brain structures to ameliorate the symptoms of Parkinson's disease. This continuous stimulation has undesirable side effects and decreases the lifespan of the unit's battery, necessitating earlier replacement. A closed-loop deep brain stimulator that uses brain signals to determine when to deliver stimulation based on the occurrence of symptoms could potentially address these drawbacks of current technology. Attempts to detect Parkinsonian tremor using brain signals recorded during the implantation procedure have been successful. However, the ability of these methods to accurately detect tremor over extended periods of time is unknown. Here we use local field potentials recorded during a deep brain stimulation clinical follow-up visit 1 month after initial programming to build a tremor detection algorithm and use this algorithm to detect tremor in subsequent visits up to 8 months later. Using this method, we detected the occurrence of tremor with accuracies between 68-93%. These results demonstrate the potential of tremor detection methods for efficacious closed-loop deep brain stimulation over extended periods of time.