The closed-loop deep brain stimulation system demands high-performance seizure detection, especially in terms of ultra-low power consumption and patient specificity. In this paper, we propose a seizure detection approach featuring low-pass filters for feature extraction and a programmable linear support vector machine for classification. This approach effectively reduces power consumption while retaining the signal energy near the cutoff frequencies and preserving the correlation between adjacent frequency bands. To reduce the false alarm rate, a Hidden Markov Model is utilized for post-processing. The proposed processor also employed calculation bit-width optimization and time-division multiplexing to minimize power and area consumption, while maintaining minimal accuracy loss. Implemented in a 65-nm CMOS process, the processor occupies an active area of 0.14 mm2. It achieves an energy classification efficiency of 0.473 μJ/class with 0.7-V supply and 16.384-kHz system clock. The measurement results show a sensitivity of 95.92%, a specificity of 98.11%, and a false alarm rate of 1.78 times/h, as validated by the CHB-MIT dataset.