实用性与精确性相遇:带有集成多通道PCG传感器的可穿戴背心,用于有效的冠状动脉疾病预先查
Matthew Fynn1, Kayapanda Mandana2, Javed Rashid2
1School of Electrical Engineering, Computing and Mathematical Sciences (EECMS), Faculty of Science and Engineering, Curtin University, Bentley, 6102, WA, Australia; Department of Electronics & Electrical Communication Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India.
Computers in biology and medicine
|March 7, 2025
概括
一个新的可穿戴背心系统简化了使用心电图 (PCG) 信号进行心血管疾病 (CVD) 查. 这种非侵入性方法为早期检测和监测冠状动脉疾病 (CAD) 提供了一种方便,快速和临床意义上的方法.
科学领域:
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
- 信号处理 信号处理
背景情况:
- 心血管疾病 (CVD),特别是冠状动脉疾病 (CAD),是全球主要的死亡原因.
- 早期发现CAD至关重要,因为心肌梗塞或中风可能是第一个表现.
- 目前对CAD的查方法缺乏成本效益,非侵入性和用于大规模查的易用性.
研究的目的:
- 开发和验证一种新的,方便的,非侵入性的数据采集系统 (DAQS),用于冠状动脉疾病 (CAD) 的预先查.
- 评估使用通过可穿戴背心获得的多通道心电图 (PCG) 信号用于CAD分类的可行性.
- 评估一种机器学习方法,使用PCG特征来区分正常心跳和受CAD影响的心跳.
主要方法:
- 开发了一款新型的可穿戴背心,其中包含多通道PCG传感器,用于快速 (不到两分钟) 和简单的信号采集.
- 获取了七个PCG信号,其中六个来自胸部,一个来自背部.
- 线性频率切斯特拉系数 (LFCC) 被提取为特征,并使用支向量机 (SVM) 来进行分类,利用多个通道的特征级融合.
主要成果:
- 拟议的系统使用多个PCG通道的最佳功能级融合实现了主体级准确度为80.44%和F1得分为81.00%.
- 这一表现比表现最好的单一频道提高了7%.
- 该系统展示了临床上显著的性能指标,表明它适合实际应用和术后监测.
结论:
- 新的DAQS提供了一种简单,方便和快速的方法,用于对心血管疾病 (CVD) 的大规模查.
- 基于PCG的分类使用LFCC特征和具有特征融合的SVM显示了早期CAD检测的巨大潜力.
- 该系统对初始查和干预后监测都很有希望,包括在PTCA或CABG等程序后检测复原症.
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