在外部心室排水紧时自动识别内压力波形:通过波形分析进行细分
Murad Megjhani1,2, Kalijah Terilli1,2, Soon Bin Kwon1,2
1Department of Neurology, Columbia University, NY, United States of America.
Physiological measurement
|June 16, 2023
概括
这项研究开发了一种自动化方法来识别从外部心室排水 (EVD) 记录中的内压力 (ICP) 波形数据. 该算法准确地分割ICP数据,提高实时分析和研究效率.
科学领域:
- 神经外科 神经外科
- 生物医学工程 生物医学工程
- 信号处理 信号处理
背景情况:
- 内压力 (ICP) 监测对于管理严重脑损伤至关重要.
- 外腔室排水系统 (EVD) 用于监测ICP和排水脑脊液.
- 在EVD系统中间歇性排水和关闭会产生复杂化ICP波形分析的工件.
研究的目的:
- 开发和验证一种自动化算法,用于从外部心室排水 (EVD) 数据中识别有效的内压力 (ICP) 波形段.
- 为了在EVD紧和打开期间区分真正的ICP信号和工件.
- 为了实现实时数据分析和提高ICP波形研究的效率.
主要方法:
- 使用波点进行时间频率分析,以区分ICP波形与EVD系统工件.
- 员工Otsu值和形态操作用于自动化文物移除和细分识别.
- 通过将其输出与两个独立调查人员的手动分类进行比较,验证了算法的性能.
主要成果:
- 该算法在45150小时的分析记录中正确识别ICP波形数据时达到86%的准确性.
- 在8.2%的病例中,部分或完全无法分割ICP波形.
- 在5.4%的数据中出现了错误阳性,即人工物被错误地识别为ICP波形.
结论:
- 开发的算法有效地自动识别EVD数据中有效的ICP波形段.
- 这种自动化可促进实时数据分析,用于临床决策支持.
- 该方法提高了管理ICP研究数据的标准化和效率.
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