Annu Int Conf IEEE Eng Med Biol Soc. 2023 Jul;2023:1-4. doi: 10.1109/EMBC40787.2023.10340588.
Closed-loop brain-implantable neuromodulation devices are a new treatment option for patients with refractory epilepsy. Seizure detection algorithms implemented on such devices are subject to strict power and area constraints. Deep learning methods, though very powerful, tend to have high computational complexity and thus are typically impractical for resource-constrained neuromodulation devices. In this paper, we propose a compact and hardware-efficient one-dimensional convolutional neural network (1D CNN) structure for patient-specific early seizure detection. Feature extraction techniques and a novel initialization method based on the forward-chaining training and testing scheme are used to improve model performance. Our compact model achieves similar accuracy to that of support vector machines, the state-of-the-art method for seizure detection, while consuming over 20x less power.
闭环脑植入式神经调节设备是治疗耐药性癫痫患者的一种新的治疗选择。此类设备上实现的癫痫发作检测算法受到严格的功率和面积限制。深度学习方法虽然非常强大，但往往计算复杂度高，因此对于资源受限的神经调节设备来说通常不切实际。在本文中，我们提出了一种用于患者特定的早期癫痫发作检测的紧凑且高效的一维卷积神经网络 (1D CNN) 结构。基于前向链接训练和测试方案的特征提取技术和新颖的初始化方法用于提高模型性能。我们的紧凑模型实现了与支持向量机（癫痫检测的最先进方法）相似的准确性，同时功耗降低了 20 多倍。
