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Automatic Computer-Based Detection of Epileptic Seizures

机译:基于计算机的癫痫发作自动检测

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摘要

Automatic computer-based seizure detection and warning devices are important for objective seizure documentation, for SUDEP prevention, to avoid seizure related injuries and social embarrassments as a consequence of seizures, and to develop on demand epilepsy therapies. Automatic seizure detection systems can be based on direct analysis of epileptiform discharges on scalp-EEG or intracranial EEG, on the detection of motor manifestations of epileptic seizures using surface electromyography (sEMG), accelerometry (ACM), video detection systems and mattress sensors and finally on the assessment of changes of physiologic parameters accompanying epileptic seizures measured by electrocardiography (ECG), respiratory monitors, pulse oximetry, surface temperature sensors, and electrodermal activity. Here we review automatic seizure detection based on scalp-EEG, ECG, and sEMG. Different seizure types affect preferentially different measurement parameters. While EEG changes accompany all types of seizures, sEMG and ACM are suitable mainly for detection of seizures with major motor manifestations. Therefore, seizure detection can be optimized by multimodal systems combining several measurement parameters. While most systems provide sensitivities over 70%, specificity expressed as false alarm rates still needs to be improved. Patients' acceptance and comfort of a specific device are of critical importance for its long-term application in a meaningful clinical way.
机译:基于计算机的自动癫痫发作检测和警告设备对于客观的癫痫发作记录,预防SUDEP,避免癫痫发作引起的癫痫发作相关的伤害和社会尴尬以及开发按需癫痫治疗至关重要。自动癫痫发作检测系统可以基于对头皮脑电图或颅内脑电图上癫痫样放电的直接分析,基于使用表面肌电图(sEMG),加速计(ACM),视频检测系统和床垫传感器的癫痫发作的运动表现检测评估通过心电图(ECG),呼吸监测仪,脉搏血氧饱和度,表面温度传感器和皮肤电活动测量的伴随癫痫发作的生理参数的变化。在这里,我们回顾基于头皮EEG,ECG和sEMG的自动癫痫发作检测。不同的癫痫发作类型会优先影响不同的测量参数。尽管脑电图变化伴随着所有类型的癫痫发作,但sEMG和ACM主要适用于检测具有主要运动表现的癫痫发作。因此,可以通过结合多种测量参数的多峰系统优化癫痫发作检测。尽管大多数系统提供的灵敏度超过70%,但表示为误报率的特异性仍需要提高。患者对于特定设备的接受程度和舒适性对于以有意义的临床方式长期应用至关重要。

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