使用视觉标记优化高频振荡的自动检测并不能改善SOZ定位
Trisha Mendoza1, Casey L Trevino1, Daniel W Shrey2
1Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA.
概括
使用视觉数据优化自动化高频振荡 (HFO) 检测并没有提高发作发作区 (SOZ) 定位精度. 为了更好的结果,可能需要对患者进行特定的优化.
科学领域:
- 神经科学是一个神经科学.
- 发病学 (Epileptology) 是一个专业的学科.
- 生物医学工程 生物医学工程
背景情况:
- 高频振荡 (HFO) 是确定发作区域 (SOZ) 的关键生物标志物.
- 自动化HFO检测提供了效率,但最大限度地提高SOZ定位精度的最佳参数选择仍然不清楚.
- 目前的方法在优化自动HFO探测器方面缺乏共识.
研究的目的:
- 使用可视识别的HFO优化自动HFO探测器.
- 评估这种优化对SOZ本地化准确性的影响.
- 通过患者特定的参数调整来探索潜在的改善.
主要方法:
- 分析了来自20名患者的内EEG数据.
- 使用三种方法检测HFO:未优化的自动化,视觉识别和视觉优化的自动检测.
- 对每个检测方法的SOZ定位准确性进行了评估.
主要成果:
- 在三个HFO检测方法中,SOZ定位的准确性没有显著差异.
- 优化的探测器设置在患者之间有很大差异,没有任何一种配置被证明是普遍有效的.
- 探索性分析表明,患者特定的设置可能会增强SOZ局部化.
结论:
- 自动HFO探测器的视觉优化并不能提高SOZ定位的准确性.
- 目前对HFO进行视觉标记是劳动密集型的.
- 开发特定于患者的自动检测参数对于改善SOZ定位至关重要.
更多相关视频
10:22Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
Published on: December 6, 2016
20.3K
09:57Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
Published on: September 20, 2024
2.6K
相关概念视频
Difference from Background: Limit of Detection
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
The LOD indicates the presence or absence...
Automatic Processing and Automatic Social Behavior
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
