HH model based smart deep brain stimulator to detect, predict and control epilepsy using machine learning algorithm.

Area of Science:

• Computational neuroscience
• Biomedical engineering
• Machine learning in healthcare

Background:

• Epilepsy is a prevalent neurological disorder managed with deep brain stimulation (DBS).
• Conventional DBS lacks patient-specific parameter optimization, leading to adverse effects.
• Understanding neural network dynamics is crucial for personalized epilepsy treatment.

Purpose of the Study:

• To design a Smart Deep Brain Stimulator (SDBS) for patient-specific epilepsy management.
• To utilize the Hodgkin-Huxley (HH) model for simulating and regulating epileptic activity.
• To develop an adaptive system for detecting, predicting, and controlling seizures.

Main Methods:

• Employed the Hodgkin-Huxley (HH) conductance-based model of brain neurons.
• Integrated ensemble machine learning (bagging and boosting) for seizure detection.
• Utilized Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN) for channel conductance prediction.
• Implemented Nonlinear Autoregressive Moving Average-L2 (NARMA-L2) and Nonlinear Model Predictive Controller (NMPC) for seizure regulation.

Main Results:

• Ensemble bagging achieved 92.7% accuracy in epilepsy detection.
• LSTM-RNN predicted sodium and potassium channel conductance variations with low RMSE (0.00568 and 0.009081).
• NMPC demonstrated superior stability and efficiency in closed-loop control compared to NARMA-L2, with minimal energy consumption.

Conclusions:

• The proposed SDBS offers patient-specific epilepsy treatment by adapting to neuronal activity and channel conductance variations.
• This adaptive approach improves deep brain stimulation efficacy and potentially reduces mortality.
• Future work includes extending the model to larger neuronal populations for enhanced clinical applicability.