Ojanen Petri, Kertész Csaba, Peltola Jukka
Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
Neuro Event Labs, Tampere, Finland.
Epileptic Disord. 2024 Dec;26(6):804-813. doi: 10.1002/epd2.20284. Epub 2024 Sep 16.
In this study, characteristics of signal profiles formed by motion, oscillation, and sound signals were analyzed to evaluate generalizability and variability in a single patient setting (intra-patient variability) and between patients (inter-patient variability). As a secondary objective, the effect of brivaracetam intervention on signal profiles was explored.
Patient data included 13 hyperkinetic seizures, 65 tonic seizures, 13 tonic-clonic seizures, and 138 motor seizures from 11 patients. All patients underwent an 8-week monitoring, and after a 3-week baseline, brivaracetam was initiated. Motion, oscillation, and sound features extracted from the video were used to form signal profiles. Variance of signals was calculated, and combined median and quartile visualizations were used to visualize the results. Similarly, the effect of intervention was visualized.
Hyperkinetic motion signals showed a rapid increase in motion and sound signals without oscillations and achieved low intra-patient variance. Tonic component created a recognizable peak in motion signal typical for tonic and tonic-clonic seizures. For tonic seizures, inter-patient variance was low. Motor signal profiles were varying, and they did not form a generalizable signal profile. Visually recognizable changes were observed in the signal profiles of two patients.
Video-based motion signal analysis enabled the extraction of motion features characteristic for different motor seizure types which might be useful in further development of this system. Tonic component formed a recognizable seizure signature in the motion signal. Hyperkinetic and motor seizures may have not only significantly different motion signal amplitude but also overlapping signal profile characteristics which might hamper their automatic differentiation. Motion signals might be useful in the assessment of movement intensity changes to evaluate the treatment effect. Further research is needed to test generalizability and to increase reliability of the results.
在本研究中,分析了由运动、振荡和声音信号形成的信号特征,以评估单一患者环境中的可推广性和变异性(患者内变异性)以及患者之间的变异性(患者间变异性)。作为次要目的,探讨了布瓦西坦干预对信号特征的影响。
患者数据包括来自11名患者的13次运动性癫痫发作、65次强直性癫痫发作、13次强直阵挛性癫痫发作和138次运动性癫痫发作。所有患者均接受了为期8周的监测,在3周的基线期后开始使用布瓦西坦。从视频中提取的运动、振荡和声音特征用于形成信号特征。计算信号的方差,并使用合并的中位数和四分位数可视化来呈现结果。同样,对干预效果进行了可视化。
运动性癫痫的运动信号显示运动和声音信号迅速增加,无振荡,且患者内方差较低。强直成分在强直性和强直阵挛性癫痫典型的运动信号中产生了一个可识别的峰值。对于强直性癫痫发作,患者间方差较低。运动信号特征各不相同,未形成可推广的信号特征。在两名患者的信号特征中观察到了视觉上可识别的变化。
基于视频的运动信号分析能够提取不同运动性癫痫发作类型的特征运动特征,这可能有助于该系统的进一步开发。强直成分在运动信号中形成了可识别的癫痫特征。运动性癫痫和运动性癫痫发作可能不仅具有显著不同的运动信号幅度,而且具有重叠的信号特征,这可能会妨碍它们的自动区分。运动信号可能有助于评估运动强度变化以评估治疗效果。需要进一步研究以测试可推广性并提高结果的可靠性。