Whisler J W, ReMine W J, Leppik I E, McLain L W, Gumnit R J
Electroencephalogr Clin Neurophysiol. 1982 Nov;54(5):541-51. doi: 10.1016/0013-4694(82)90039-6.
Machine detection of epileptiform activity in the EEG is useful in seizure monitoring because of its inherent consistency and the rapid data reduction it can provide. Devices based on a few detection criteria have lacked reliability of detection and those with more complex algorithms have sacrificed operating speed and portability. This paper describes a largely analog device which detects irregular as well as classic spike and wave activity. It is portable and it can process the accelerated playback of 24 h tape recorders as well as real-time EEG. It recognizes spikes by their shape and waves by their frequency. It makes inter-channel comparisons to identify trains of bilateral synchronous spikes, generalized waves, and coincidence of spikes and waves and furnishes a limited description of each event in terms of these characteristics. The device was tested against the judgment of 3 experienced and certified electroencephalographers in 18 h of EEG containing 769 bursts of spike-wave activity from 6 patients. It detected 96.5% of the consensus spike and wave activity (i.e., activity identified by all 3 electroencephalographers). Only 0.56% of the machine's detections were false positives (i.e., activity identified by none of the electroencephalographers), though the false positive rate was higher in the presence of chewing artifact. It measured burst duration with an average error of 0.43 sec/burst. While reader-machine agreement varied somewhat by patient, in general, the machine disagreed with the consensus no more than the readers disagreed with each other. In a second reading session after 6 months, the amount of activity identified by the readers changed by an amount ranging from 2.4% to 57% while the machine was consistent within a few tenths of 1%. Hence, this paper demonstrates that by implementing a multi-criteria detection algorithm in special purpose circuitry, a cost-effective solution to the problem of reliable machine detection of spike and wave activity can be obtained.
脑电图中癫痫样活动的机器检测在癫痫发作监测中很有用，因为它具有内在的一致性以及能够提供快速的数据简化。基于少数检测标准的设备缺乏检测可靠性，而具有更复杂算法的设备则牺牲了运行速度和便携性。本文描述了一种主要为模拟式的设备，它能检测不规则以及典型的棘波和慢波活动。它便于携带，能够处理24小时磁带录音机的加速回放以及实时脑电图。它通过形状识别棘波，通过频率识别慢波。它进行通道间比较以识别双侧同步棘波序列、广泛性慢波以及棘波和慢波的同时出现，并根据这些特征对每个事件提供有限的描述。该设备在包含6名患者的769次棘慢波活动爆发的18小时脑电图中，与3名经验丰富且有资质的脑电图专家的判断进行了对比测试。它检测出了96.5%的一致棘波和慢波活动（即所有3名脑电图专家都认定的活动）。该设备的检测中只有0.56%为假阳性（即没有脑电图专家认定的活动），不过在存在咀嚼伪迹的情况下假阳性率更高。它测量爆发持续时间，平均误差为每次爆发0.43秒。虽然读者与机器的一致性在不同患者中有所不同，但总体而言，机器与共识的分歧不超过读者之间的分歧。在6个月后的第二次阅读环节中，读者识别出的活动量变化范围为2.4%至57%，而机器的结果在1%的十分之几范围内保持一致。因此，本文表明通过在专用电路中实施多标准检测算法，可以获得一种经济有效的解决方案，用于可靠地进行机器检测棘波和慢波活动。
