DyNeuMo Mk-1: Design and pilot validation of an investigational motion-adaptive neurostimulator with integrated chronotherapy
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
There is growing interest in using adaptive neuromodulation to provide a more personalized therapy experience that might improve patient outcomes. Current implant technology, however, can be limited in its adaptive algorithm capability. To enable exploration of adaptive algorithms with chronic implants, we designed and validated the ‘Picostim DyNeuMo Mk-1’ (DyNeuMo Mk-1 for short), a fully-implantable, adaptive research stimulator that titrates stimulation based on circadian rhythms (e.g. sleep, wake) and the patient's movement state (e.g. posture, activity, shock, free-fall). The design leverages off-the-shelf consumer technology that provides inertial sensing with low-power, high reliability, and relatively modest cost. The DyNeuMo Mk-1 system was designed, manufactured and verified using ISO 13485 design controls, including ISO 14971 risk management techniques to ensure patient safety, while enabling novel algorithms. The system was validated for an intended use case in movement disorders under an emergency-device authorization from the Medicines and Healthcare Products Regulatory Agency (MHRA). The algorithm configurability and expanded stimulation parameter space allows for a number of applications to be explored in both central and peripheral applications. Intended applications include adaptive stimulation for movement disorders, synchronizing stimulation with circadian patterns, and reacting to transient inertial events such as posture changes, general activity, and walking. With appropriate design controls in place, first-in-human research trials are now being prepared to explore the utility of automated motion-adaptive algorithms.
Date Issued
2022-05-01
Date Acceptance
2022-01-06
Citation
Experimental Neurology, 2022, 351
ISSN
0014-4886
Publisher
Elsevier
Journal / Book Title
Experimental Neurology
Volume
351
Copyright Statement
2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/35016994
PII: S0014-4886(22)00002-4
Subjects
Activity recognition
Adaptive control
Brain stimulation
Closed-loop systems
DEEP BRAIN-STIMULATION
Life Sciences & Biomedicine
Neural implants
NEUROMODULATION DEVICE
Neurosciences
Neurosciences & Neurology
Risk management
Science & Technology
SLEEP
TREMOR
Publication Status
Published
Coverage Spatial
United States
Article Number
ARTN 113977
Date Publish Online
2022-01-10