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Low-power circuit design techniques have enabled the possibility of integrating signal processing and feature extraction algorithms on-board implantable medical devices, eliminating the need for wireless transfer of data outside the patient. Feature extraction algorithms also serve as valuable tools for modern-day artificial prostheses, made possible by implantable brain-computer-interface systems. This paper intends to review the challenges in designing feature extraction blocks for implantable devices, with specific focus on developing efficacious but computationally efficient algorithms to detect seizures. Common seizure detection features used to construct algorithms are evaluated and algorithmic, mathematical as well as circuit-level design techniques are suggested to effectively translate the algorithms into hardware implementations on low-power platforms