A patient-specific algorithm for the detection of seizure onset in long-term EEG monitoring: Possible use as a warning device
A patient-specific algorithm for the detection of seizure onset in long-term EEG monitoring: Possible use as a warning device
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DOI：
10.1109/10.552241
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发表时间：
1997-02-01
影响因子：
4.6
通讯作者：
Gotman, J
中科院分区：
文献类型：
--
作者：
Qu, H;Gotman, J
在癫痫患者的长期脑电图（EEG）监测期间，癫痫发作警告系统将允许患者和观察者采取适当的预防措施，它还允许观察者在癫痫发作期间早期与患者交互，从而揭示临床有用的信息。我们设计了患者特定的分类器来检测癫痫发作，在记录患者的癫痫发作和一些非癫痫发作数据后，它们用于训练分类器。在随后的监测会话中，EEG模式必须通过该分类器以确定是否发生癫痫发作。如果发生，则触发警报。由于高的误报警率将使系统无效，因此已经采取了非常小心的措施以确保低的误报警率。从时域和频域提取特征，并使用修改的最近邻（NN）分类器。该系统的发病检测率为100%，发病后平均延迟时间为9.35s，平均误报率仅为0.02/h。该方法在12例患者共47次癫痫发作中进行了评估，结果表明，该系统是有效的和合理的可靠性，计算负载一直保持在最低限度，使实时处理是可能的。
During long-term electroencephalogram (EEG) monitoring of epileptic patients, a seizure warning system would allow patients and observers to take appropriate precautions, It would also allow observers to interact with patients early during the seizure, thus revealing clinically useful information, We designed patient-specific classifiers to detect seizure onsets, After a seizure and some nonseizure data are recorded in a patient, they are used to train a classifier. In subsequent monitoring sessions, EEG patterns have to pass this classifier to determine if a seizure onset occurs, If it does, an alarm is triggered, Extreme care has been taken to ensure a low false-alarm rate, since a high false-alarm rate would render the system ineffective, Features were extracted from the time and frequency domains and a modified nearest-neighbor (NN) classifier was used, The system reached an onset detection rate of 100% with an average delay of 9.35 s after onset, The average false-alarm rate was only 0.02/h. The method was evaluated in 12 patients with a total of 47 seizures, Results indicate that the system is effective and reasonably reliable, Computation load has been kept to a minimum so that real-time processing is possible.