从心电图信号中识别和分类心律失常的心跳,使用特征诱导的最佳极端梯度增强算法
S Majumder1, S Bhattacharya1, P Debnath2
1Electronics and Communication Engineering Department, Meghnad Saha Institute of Technology, Kolkata, India.
Computer methods in biomechanics and biomedical engineering
|October 9, 2023
概括
这项研究引入了一种计算机辅助诊断 (CAD) 系统,用于从心电图 (ECG) 信号中分类心律失常心跳. 该方法利用优化极端梯度提升 (O-XGBoost) 进行心血管疾病的准确检测.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 心血管疾病是全球主要的死亡原因.
- 早期发现心律失常是减轻风险的关键.
- 自动化系统可以帮助及时诊断和治疗.
研究的目的:
- 开发一种计算机辅助诊断 (CAD) 方法,用于自动识别和分类心律不整的心跳.
- 使用机器学习技术提高心律失常检测的准确性.
- 提高心电图 (ECG) 信号分析的临床实用性.
主要方法:
- 使用MIT-BIH心律失常数据库进行培训和测试.
- 适用于ECG信号预处理的离散波形变换.
- 提取的统计,时间和光谱特征.
- 采用随机森林来选择特征和一个最佳极端梯度增强 (O-XGBoost) 分类器.
- 优化了超参数,并使用了合成少数过量采样技术来平衡数据集.
主要成果:
- 在分类心律不整的心跳方面取得了显著的表现.
- 与现有的最先进的方法相比,证明了更好的结果.
- 使用优化参数的O-XGBoost分类器显示出高精度.
结论:
- 拟议的CAD系统有效地使用心电图信号对心律不整的心跳进行分类.
- 这种方法为心血管疾病检测提供了强大的解决方案.
- 该模型可适应各种计算机辅助诊断系统的实施.
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