Abstract
The identification of a new generation of adaptive strategies for deep brain stimulation (DBS) will require the development of mixed hardware–software systems for testing and implementing such controllers clinically. Towards this aim, introducing an operating system (OS) that provides high-level features (multitasking, hardware abstraction, and dynamic operation) as the core element of adaptive deep brain stimulation (aDBS) controllers could expand the capabilities and development speed of new control strategies. However, such software frameworks also introduce substantial power consumption overhead that could render this solution unfeasible for implantable devices. To address this, in this work four techniques to reduce this overhead are proposed and evaluated: a tick-less idle operation mode, reduced and dynamic sampling, buffered read mode, and duty cycling. A dual threshold adaptive deep brain stimulation algorithm for suppressing pathological oscillatory neural activity was implemented along with the proposed energy saving techniques on an energy-efficient OS, YetiOS, running on a STM32L476RE microcontroller. The system was then tested using an emulation environment coupled to a mean field model of the parkinsonian basal ganglia to simulate local field potential (LFPs) which acted as a biomarker for the controller. The OS-based controller alone introduced a power consumption overhead of 10.03 mW for a sampling rate of 1 kHz. This was reduced to 12 μW by applying the proposed tick-less idle mode, dynamic sampling, buffered read and duty cycling techniques. The OS-based controller using the proposed methods can facilitate rapid and flexible testing and implementation of new control methods. Furthermore, the approach has the potential to become a central element in future implantable devices to enable energy-efficient implementation of a wide range of control algorithms across different neurological conditions and hardware platforms.
Highlights
Over the past two decades, Deep Brain Stimulation (DBS) has become established as an effective surgical therapy to reduce the symptoms of several neurological conditions including Parkinson’s Disease (PD), essential tremor, dystonia, epilepsy, and severe obsessive-compulsive disorder [1,2,3]
In this work we present four different techniques to reduce the power consumption in a device running an Adaptive Deep Brain Stimulation (aDBS) algorithm on top of an operating system (OS)
An OS-based controller provides a wide range of programming flexibility for the development of aDBS and neuromodulation techniques that could enable advances in this field to be rapidly implemented
Summary
Over the past two decades, Deep Brain Stimulation (DBS) has become established as an effective surgical therapy to reduce the symptoms of several neurological conditions including Parkinson’s Disease (PD), essential tremor, dystonia, epilepsy, and severe obsessive-compulsive disorder [1,2,3]. A gradual increase in understanding of the mechanisms by which DBS exerts its therapeutic effects has led to the emergence of closed-loop neuromodulation or adaptive DBS (aDBS) techniques [4,5,6,7] Using this approach, instead of delivering a constant stimulation signal, the stimulation parameters are adjusted in response to biomarkers indicative of patient symptoms [8,9,10,11]. Instead of delivering a constant stimulation signal, the stimulation parameters are adjusted in response to biomarkers indicative of patient symptoms [8,9,10,11] These new techniques present potential benefits both in the effectiveness of the therapies, by delivering the stimulation required to control symptoms while minimizing stimulation-induced side effects, and in reducing power consumption of the stimulation devices [5,12,13].
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