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Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation
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On the need for adaptive learning in on-demand Deep Brain Stimulation for Movement Disorders.

Nivedita Khobragade, Daniela Tuninetti, Daniel Graupe

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    Summary

    Future on-demand Deep Brain Stimulation (DBS) systems need adaptive learning. Machine learning models predict tremor onset but require continuous updates for long-term efficacy in movement disorder patients.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Machine Learning

    Background:

    • Deep Brain Stimulation (DBS) is a key treatment for movement disorders like Parkinson's disease and essential tremor.
    • On-demand DBS systems aim to improve patient outcomes by delivering stimulation only when needed.
    • Maintaining long-term efficacy requires systems to adapt to changes in patient control signals over time.

    Purpose of the Study:

    • To evaluate the long-term robustness of machine learning algorithms for predicting tremor onset in on-demand DBS.
    • To assess the performance of Decision Tree and LAMSTAR neural network models using surface Electromyography and accelerometry.
    • To investigate the necessity of adaptive learning for sustained symptom control in chronic DBS therapy.

    Main Methods:

    • Two machine learning algorithms (Decision Tree, LAMSTAR) were trained and tested using surface Electromyography and accelerometry data.
    • Models predicted tremor onset after Deep Brain Stimulation (DBS) was switched off in Parkinson's disease and essential tremor patients.
    • Novelty lies in training and testing across sessions at least one week apart to simulate long-term operation.

    Main Results:

    • 100% sensitivity was achieved when training and testing on data from the same session.
    • Sensitivity decreased significantly when models were tested on data from separate sessions (recorded at least one week apart).
    • The ratio of predicted to observed stimulation-off time was lower for separate-session testing, indicating reduced performance.

    Conclusions:

    • Current machine learning approaches for on-demand DBS show reduced efficacy over longer periods.
    • Adaptive learning capabilities are crucial for future on-demand DBS systems to maintain high symptom control.
    • Continuous adaptation is necessary to account for potential changes in patient control signals during chronic use.