Bandarabadi Mojtaba, Teixeira Cesar A, Netoff Theoden I, Parhi Keshab K, Dourado Antonio
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:4447-50. doi: 10.1109/EMBC.2014.6944611.
This paper presents two low complexity and yet robust methods for automated seizure detection using a set of 2 intracranial Electroencephalogram (iEEG) recordings. Most current seizure detection methods suffer from high number of false alarms, even when designed to be subject-specific. In this study, the ratios of power between pairs of frequency bands are used as features to detect epileptic seizures. For comparison, these features are calculated from monopolar and bipolar iEEG recordings. Optimal thresholds are individually determined and used for each feature. Alarms are generated when the measure passes the threshold. The detector was applied to long-term continuous invasive recordings from 5 patients with refractory partial epilepsy, containing 54 seizures in 780 hours. On average, the results revealed 88.9% sensitivity, a very low false detection rate of 0.041 per hour (h(-1)) and detection latency of 9.4 seconds.
本文提出了两种低复杂度但稳健的方法，用于使用一组2个颅内脑电图（iEEG）记录进行自动癫痫发作检测。即使设计为针对特定个体，大多数当前的癫痫发作检测方法仍存在大量误报。在本研究中，频带对之间的功率比用作检测癫痫发作的特征。为了进行比较，这些特征是从单极和双极iEEG记录中计算得出的。针对每个特征分别确定并使用最佳阈值。当测量值超过阈值时会生成警报。该检测器应用于5例难治性部分性癫痫患者的长期连续侵入性记录，在780小时内包含54次癫痫发作。平均而言，结果显示灵敏度为88.9%，每小时非常低的误检率为0.041（h(-1)），检测潜伏期为9.4秒。
