Shoeb Ali, Carlson Dave, Panken Eric, Denison Timothy
Massachusetts Institute of Technology, Boston, MA 02139, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:4202-5. doi: 10.1109/IEMBS.2009.5333790.
Implantable neurostimulators for the treatment of epilepsy that are capable of sensing seizures can enable novel therapeutic applications. However, detecting seizures is challenging due to significant intracranial EEG signal variability across patients. In this paper, we illustrate how a machine-learning based, patient-specific seizure detector provides better performance and lower power consumption than a patient non-specific detector using the same seizure library. The machine-learning based architecture was fully implemented in the micropower domain, demonstrating feasibility for an embedded detector in implantable systems.
能够感知癫痫发作的用于治疗癫痫的植入式神经刺激器可实现新的治疗应用。然而，由于患者之间颅内脑电图信号存在显著差异，检测癫痫发作具有挑战性。在本文中，我们展示了基于机器学习的、针对特定患者的癫痫发作检测器如何比使用相同癫痫发作库的非特定患者检测器具有更好的性能和更低的功耗。基于机器学习的架构在微功耗领域得到了全面实现，证明了其在植入式系统中作为嵌入式检测器的可行性。
