基于可调整的Q因子波波变换,使用心电图信号识别糖尿病患者
Anuja Jain1, Anurag Verma1, Amit Kumar Verma2
1Teerthanker Mahaveer University, Moradabad, UP, India.
Computer methods in biomechanics and biomedical engineering
|April 18, 2024
概括
本研究引入了一种机器学习系统,用于使用心电图 (ECG) 信号自动检测糖尿病. 该方法达到91.5%的准确性,超过了用于识别糖尿病心血管自主功能障碍的现有技术.
科学领域:
- 生物医学工程 生物医学工程
- 计算医学是一种计算医学.
- 心脏病学 心脏病学
背景情况:
- 糖尿病是一种慢性疾病,导致血糖升高,导致广泛的器官损伤.
- 糖尿病心血管自主功能障碍改变心电图 (ECG) 信号形态.
- 通过心电图分析自动检测糖尿病仍然是一个重大挑战.
研究的目的:
- 开发和验证基于机器学习的系统,用于使用心电图信号自动检测糖尿病.
- 调查多分辨率分析和基于的特征在糖尿病鉴定中的有效性.
- 将拟议方法的性能与现有技术进行比较.
主要方法:
- 电脑心电图信号被分解成子频段,使用可调的Q因子波量变换 (TQWT).
- 从每个子频段提取了四个基于的特征,并使用K-W测试进行了选择.
- 一个支持矢量机 (SVM) 分类器与10倍的交叉验证被用于自动检测.
主要成果:
- TQWT的子频段与使用粗高斯核的SVM分类器相结合,实现了91.5%的分类准确性.
- 拟议的方法在同一数据集上的其他现有方法相比,显示出更高的性能.
- 使用K-W测试的特征选择有效地确定了用于糖尿病检测的相关特征.
结论:
- 开发的机器学习系统为自动糖尿病检测提供了一个有希望的,非侵入性的方法.
- 对心电图信号的多分辨率分析,特别是特定的TQWT子频段,对于识别糖尿病并发症具有重大潜力.
- 这些发现表明,基于心电图的分析可以作为早期糖尿病查和管理的宝贵工具.
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