Application of deep learning to distinguish multiple deep brain stimulation parameter configurations for the treatment of Parkinson’s disease

Deep brain stimulation offers the opportunity for patient specific and optimally tuned treatment to ameliorate Parkinson’s disease. Imperative for the efficacy of deep brain stimulation is the quantification of therapeutic response. The implementation of inertial sensor systems has considerably augmented the acuity of quantifying the characteristics of the movement disorder through the respective signal data. For example, these inertial sensors constitute effectively wearable and wireless systems that can be mounted about the dorsum of the hand to objectively quantify tremor. Using machine learning algorithms considerable classification accuracy has been achieved. Conformal wearable and wireless inertial sensor systems have been recently developed that can be conveniently mounted about the dorsum of the hand by an adhesive medium with a profile on the order of a bandage. Additionally, these conformal wearable and wireless inertial sensor systems can have their data wirelessly transmitted to a secure Cloud computing environment. This capability offers the opportunity to ascertain the inertial sensor signal for Parkinson’s disease tremor response in the context of multiple deep brain stimulation parameter configurations, such as an assortment of amplitude settings. The objective of the research was to quantify and distinguish the response of Parkinson’s disease tremor through an assortment of amplitude settings for deep brain stimulation, such as ‘Off’ setting as a baseline, amplitude set to 1.0 mA, amplitude set to 1.75 mA, amplitude set to 2.5 mA, amplitude set to 3.25 mA, and amplitude set to 4.0 mA, using a deep learning convolutional neural network through TensorFlow with the quantification of tremor by a conformal wearable and wireless inertial sensor system. Considerable classification accuracy was attained to distinguish between these specified deep brain stimulation settings for the amelioration of Parkinson’s disease.