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多模态智能癫痫发作采集(MISA)系统——一种基于全身运动测量的癫痫发作检测新方法。

Multi-modal intelligent seizure acquisition (MISA) system--a new approach towards seizure detection based on full body motion measures.

作者信息

Conradsen Isa, Beniczky Sandor, Wolf Peter, Terney Daniella, Sams Thomas, Sorensen Helge B D

机构信息

Department of Electrical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:2591-5. doi: 10.1109/IEMBS.2009.5335334.

Abstract

Many epilepsy patients cannot call for help during a seizure, because they are unconscious or because of the affection of their motor system or speech function. This can lead to injuries, medical complications and at worst death. An alarm system setting off at seizure onset could help to avoid hazards. Today no reliable alarm systems are available. A Multi-modal Intelligent Seizure Acquisition (MISA) system based on full body motion data seems as a good approach towards detection of epileptic seizures. The system is the first to provide a full body description for epilepsy applications. Three test subjects were used for this pilot project. Each subject simulated 15 seizures and in addition performed some predefined normal activities, during a 4-hour monitoring with electromyography (EMG), accelerometer, magnetometer and gyroscope (AMG), electrocardiography (ECG), electroencephalography (EEG) and audio and video recording. The results showed that a non-subject specific MISA system developed on data from the modalities: accelerometer (ACM), gyroscope and EMG is able to detect 98% of the simulated seizures and at the same time mistakes only 4 of the normal movements for seizures. If the system is individualized (subject specific) it is able to detect all simulated seizures with a maximum of 1 false positive. Based on the results from the simulated seizures and normal movements the MISA system seems to be a promising approach to seizure detection.

摘要

许多癫痫患者在发作期间无法呼救,原因是他们失去意识,或是运动系统或语言功能受到影响。这可能导致受伤、医疗并发症,最严重的是死亡。发作开始时触发的警报系统有助于避免危险。目前尚无可靠的警报系统。基于全身运动数据的多模态智能癫痫发作采集(MISA)系统似乎是检测癫痫发作的一种好方法。该系统是首个为癫痫应用提供全身描述的系统。本试点项目使用了三名测试对象。在为期4小时的监测过程中,每个对象模拟了15次发作,此外还进行了一些预先定义的正常活动,监测手段包括肌电图(EMG)、加速度计、磁力计和陀螺仪(AMG)、心电图(ECG)、脑电图(EEG)以及音频和视频记录。结果表明,基于加速度计(ACM)、陀螺仪和肌电图等模态数据开发的非特定对象MISA系统能够检测出98%的模拟发作,同时仅将4次正常运动误判为发作。如果该系统实现个性化(特定对象),则能够检测出所有模拟发作,最多出现1例假阳性。基于模拟发作和正常运动的结果,MISA系统似乎是一种很有前景的癫痫发作检测方法。

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