Adaptive neurostimulation: A real-time optimisation algorithm to enhance neuromodulation therapies
Algorithm fine-tunes brain stimulation therapies in real-time, adapting to changes like disease progression and body rhythms. This personalised approach can enhance treatment effectiveness for conditions such as Parkinson’s and chronic pain.
Applications: Invasive and non-invasive neuromodulation therapies, such as Deep Brain Stimulation and Transcranial Magnetic Stimulation
| Features | Benefits |
|---|---|
| Algorithm provides dynamic adaptation to patient-specific changes | Provides personalised, real-time optimisation of therapy settings |
| Time-varying Bayesian optimisation (TV-BayesOpt) considers the time-varying nature of symptoms | Continuously adjusts to align with daily biological rhythms and disease progression |
| Inputs can be driven by either a peripheral or central disease biomarker | Reduces reliance on trial-and-error from manual parameter setting based on the limited snapshot provided in daytime clinical assessments |
| Direct measure of symptom severity and stimulation efficacy | Makes adjustments informed by current data, minimising the impact of outdated information |
| Prioritisation of recent patient data | Refines therapy delivery over time, leading to superior symptom control compared to static or manually adjusted methods |
| Continuous performance enhancement | Can enhance treatment effectiveness for conditions such as Parkinson’s and tremor, as well as emerging areas where neurostimulation is relevant |
| Relevant for all conditions currently considered for brain stimulation or peripheral stimulation |
Awaiting patent and Available For
- Co-development
- Consulting
- Licensing