Adaptive neurostimulation: A real-time optimisation algorithm to enhance neuromodulation therapies

Applications: Invasive and non-invasive neuromodulation therapies, such as Deep Brain Stimulation and Transcranial Magnetic Stimulation

Algorithm fine-tunes brain stimulation therapies in real-time, adapting to changes like disease progression and body rhythms.

Features	Benefits
Algorithm provides dynamic adaptation to patient-specific changes. Time-varying Bayesian optimisation (TV-BayesOpt) considers the time-varying nature of symptoms.	Provides personalised, real-time optimisation of therapy settings. Continuously adjusts to align with daily biological rhythms and disease progression.
Inputs can be driven by either a peripheral or central disease biomarker. Direct measure of symptom severity and stimulation efficacy.	Reduces reliance on trial-and-error from manual parameter setting based on the limited snapshot provided in daytime clinical assessments.
Prioritisation of recent patient data Continuous performance enhancement	Makes adjustments informed by current data, minimising the impact of outdated information. Refines therapy delivery over time, leading to superior symptom control compared to static or manually adjusted methods.
Relevant for all conditions currently considered for brain stimulation or peripheral stimulation.	Can enhance treatment effectiveness for conditions such as Parkinson’s and tremor, as well as emerging areas where neurostimulation is relevant.

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