Design of a hybrid AI network circuit for epilepsy detection with 97.5% accuracy and low cost-latency

Epilepsy detection using artificial intelligence (AI) networks has gained significant attention. However, existing methods face challenges in accuracy, computational cost, and speed. CNN excel in feature extraction but suffer from high computational latency and power consumption, while SVM rely heavily on feature quality and expensive kernel computations, limiting real-time performance. Additionally, most CNN-SVM hybrid model lack hardware optimization, leading to inefficient implementations with poor accuracy-latency trade-offs. To address these issues, this paper designs a hybrid AI network-based method for epilepsy detection using electroencephalography (EEG) signals. First, a hybrid AI network was constructed using three convolutional layers, three pooling layers, and a Gaussian kernel SVM to achieve EEG epilepsy detection. Then, the design of the multiply-accumulate circuit was completed using a parallel-style row computation method, and a pipelined convolutional computation circuit was used to accelerate the convolutional computation and reduce the computational overhead and delay. Finally, a single-precision floating-point exponential and logarithmic computation circuit was designed to improve the speed and accuracy of data computation. The digital back-end of the hardware circuit was realized under the TSMC 65 nm process. Experimental results show that the circuit occupies an area of 3.20 mm², consumes 4.28 mW of power, operates at a frequency of 10 MHz, and has an epilepsy detection latency of 0.008 s, which represents a 32% reduction in latency compared to those reported in the relevant literature. The database test results showed an epilepsy detection accuracy of 97.5%, a sensitivity of 97.6%, and a specificity of 97.2%.