没有监督的心脏声音的细分从突然变化的检测检测
Eric Tatulli1, Remi Souriau1, Julie Fontecave-Jallon1
1Univ. Grenoble Alpes, CNRS, CHU Grenoble Alpes, Grenoble INP, TIMC-IMAG, La Tronche, France.
Computers in biology and medicine
|January 26, 2025
概括
这项研究引入了一种无监督的方法,通过检测突发信号变化来细分心脏图中的心脏声音和沉默. 该算法可靠地识别心脏状态,有助于诊断心血管疾病.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 心脏病学 心脏病学
背景情况:
- 心血管疾病的早期诊断依赖于心脏听觉.
- 将心脏图片自动细分成心脏状态,有助于医生进行分析.
- 对心脏声音和沉默的无监督细分对于有效的解释至关重要.
研究的目的:
- 提出一种无监督的方法,在心脏图表中对心脏声音和静音进行细分.
- 检测突然的信号变化,以识别心脏声音的开始和结束.
- 为了改善心脏声音记录的生理解释.
主要方法:
- 定位与心声边界相对应的突然信号变化.
- 在变化点定义的间隔内计算信号功率,以识别声音和静音.
- 调整算法参数仅基于估计的心率.
主要成果:
- 该方法在三个独立的数据库 (PhysioNet 2016,CirCor DigiScope,PASCAL) 上进行了评估.
- 在数据库中获得高平均F1分数:91.2%,94.3%和96.3%.
- 在检测性能方面表现优于大多数竞争的无监督方法.
结论:
- 拟议的算法可靠,强大,易于实施.
- 在各种心声数据库中展示了顶级的检测性能.
- 为心脏声音细分提供了一个简单而有效的替代方案.
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