Epilepsy is a neurological disorder that affects millions of people worldwide and is characterized by recurrent seizures. Managing epilepsy effectively remains a challenge, particularly for patients who do not respond to medication. Closed-loop neuromodulation systems have emerged as a promising approach for real-time seizure detection and intervention, offering the potential to improve the quality of life for individuals with epilepsy. These systems monitor brain activity and deliver therapeutic stimuli when abnormal patterns, indicative of an impending seizure, are detected. However, the effectiveness of these systems depends heavily on the accuracy and efficiency of the embedded seizure detection algorithms.

Implementing brain-computer interfaces (BCIs) for neuromodulation presents significant challenges, particularly in terms of power consumption, area constraints, and computational complexity. The use of machine learning (ML), especially lightweight classifiers, is critical for overcoming these challenges. ML models can be trained to identify complex seizure patterns with high sensitivity and specificity, but their deployment in implantable devices requires careful consideration of hardware limitations.

This thesis addresses these challenges by employing an oblique tree estimator to develop a low-power, low-area seizure detection classifier that relies solely on temporal features to minimize hardware costs. Specifically, the Sparse Projection Oblique Randomer Forest (SPORF) variant is utilized, to enable multiplier-less classifications, along with a multi-path inference approach thereby reducing memory requirements. The proposed model achieves state-of-the-art performance in both patient-specific (98.78%/ 99.89% and 98.52%/99.91% sensitivity/specificity in the CHB-MIT and ETHZ databases, respectively) and patient-independent (92.26%/98.29% in the CHB-MIT database) seizure detection frameworks. Additionally, post-layout simulations of the synthesized design reveal a power consumption of 1.22 µW and an area of 0.0076 mm², with an energy consumption of 7.58 nJ per classification using 40 nm CMOS technology.