Zanetti Renato, Aminifar Amir, Atienza David
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:4248-4251. doi: 10.1109/EMBC44109.2020.9175339.
Epilepsy affects more than 50 million people and ranks among the most common neurological diseases worldwide. Despite advances in treatment, one-third of patients still suffer from refractory epilepsy. Wearable devices for real-time patient monitoring can potentially improve the quality of life for such patients and reduce the mortality rate due to seizure-related accidents and sudden death in epilepsy. However, the majority of employed seizure detection techniques and devices suffer from unacceptable false-alarm rate. In this paper, we propose a robust seizure detection methodology for a wearable platform and validate it on the Physionet.org CHB-MIT Scalp EEG database. It reaches sensitivity of 0.966 and specificity of 0.925, and reducing the false-alarm rate by 34.7%. We also evaluate the battery lifetime of the wearable system including our proposed methodology and demonstrate the feasibility of using it in real time for up to 40.87 hours on a single battery charge.
癫痫影响着超过5000万人，是全球最常见的神经疾病之一。尽管治疗方法有所进步，但仍有三分之一的患者患有难治性癫痫。用于实时患者监测的可穿戴设备有可能改善此类患者的生活质量，并降低癫痫发作相关事故和癫痫猝死导致的死亡率。然而，大多数现有的癫痫发作检测技术和设备的误报率令人无法接受。在本文中，我们为可穿戴平台提出了一种强大的癫痫发作检测方法，并在Physionet.org的CHB-MIT头皮脑电图数据库上进行了验证。该方法的灵敏度达到0.966，特异性达到0.925，误报率降低了34.7%。我们还评估了包括我们提出的方法在内的可穿戴系统的电池续航时间，并证明了单次充电后该系统可实时使用长达40.87小时的可行性。
