Closed-Loop Implantable Neurostimulators for Individualized Treatment of Intractable Epilepsy: A Review of Recent Developments, Ongoing Challenges, and Future Opportunities

• Published in: IEEE transactions on biomedical circuits and systems 2024-12, Vol.18 (6), p.1268-1295
• Main Authors: Kassiri, Hossein, Muneeb, Abdul, Salahi, Rojin, Dabbaghian, Alireza
• Format: Article
• Language: English
• Subjects: adaptive stimulation; Application specific integrated circuits; ASIC solutions; Closed loop systems; computational models; Decision trees; deep neural networks; Electroencephalography; Energy efficiency; Epilepsy; epilepsy treatment; frequency-domain features; implantable device; Implants; individualized therapy; machine learning classifiers; neural mass models; Neural recording; Neurological diseases; Neurostimulation; online tuning; patient-specific therapy; Recording; responsive neurostimulation; seizure detection; seizure prediction; Spiking neural networks; Support vector machines; time-domain features
• ISSN: 1932-4545; 1940-9990
• DOI: 10.1109/TBCAS.2024.3456825

Driven by its proven therapeutic efficacy in treating movement disorders and psychiatric conditions, neurostimulation has emerged as a promising intervention for intractable epilepsy. Researchers envision an advanced implantable device capable of long-term neuronal monitoring, high spatio-temporal resolution data processing, and timely responsive neurostimulation upon seizure detection. However, the stringent energy constraints of implantable devices and significant inter-patient variability in neural activity pose substantial challenges and opportunities for biomedical circuits and systems researchers. For seizure detection, various ASIC solutions employing both deterministic and data-driven algorithms have been developed. These solutions leverage a subset of numerous signal features (spanning time and frequency domains) and classifiers (such as SVMs, DNNs, SNNs) to achieve notable success in terms of detection accuracy, latency, and energy efficiency. Implementations vary widely in computational approaches (digital, mixed-signal, analog, spike-based), training strategies (online versus offline), and application targets (patient-specific versus cross-patient). In terms of treatment, recent efforts have focused on the personalization of stimulation waveforms to enhance therapeutic efficacy. This personalization faces complex challenges, including a limited understanding of how stimulation parameters influence neuronal activity, the lack of a comprehensive brain model to capture its intricate electrochemical dynamics, and recording neural signals in the presence of stimulation artifacts. This review provides a comprehensive overview of the field, detailing the foundational principles, recent advancements, and ongoing challenges in enhancing the diagnostic accuracy, treatment efficacy, and energy efficiency of implantable patient-optimized neurostimulators. We also discuss potential future directions, emphasizing the need for standardized performance metrics, advanced computational models, and adaptive stimulation protocols to realize the full potential of this transformative technology.