非专家使用无限冲动响应 (IIR) 过器的机器学习和深度学习模型对心电图进行心律失常的分类
Mallikarjunamallu K1, Khasim Syed1
1School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India.
PeerJ. Computer science
|March 4, 2024
概括
这项研究引入了一个无限脉冲响应 (IIR) 过器,以减少心电图 (ECG) 信号中的噪声,改善心律失常检测. 机器学习和深度学习模型实现了高精度,微调的DenseNet-121达到99.97%.
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:
- 节律失常会带来严重的心血管风险,需要通过心电图 (ECG) 进行精确的监测.
- 由于信号噪声和复杂性,解释心电图数据,特别是12-lead,即使对于专家来说也是具有挑战性的.
- 现有的便携式心电图监视器在实时解释和将节律数据与患者状况相关联方面遇到了困难.
研究的目的:
- 通过消除噪声和提取特征,通过使用一种新的无限脉冲响应 (IIR) 波器来提高心电图信号质量.
- 为了提高非专家对ECG分析的理解性.
- 使用机器学习 (ML) 和深度学习 (DL) 模型准确分类心律.
主要方法:
- 使用了来自MIT-BIH数据库的ECG数据.
- 应用无限冲动响应 (IIR) 过器来减少噪音和提取特征.
- 用于ML分类器的超参数 (HP) 调整和DL模型的微调 (FT),包括DenseNet-121.
- 评估不同过器来对心律失常进行分类,并评估准确度的变化.
主要成果:
- 拟议的IIR过器有效地提高了ECG信号质量.
- 机器学习和深度学习模型在节奏分类中表现出高性能.
- 在没有微调的情况下,DenseNet-121实现了99%的准确性,在微调时达到99.97%的准确性.
结论:
- IIR过器显著增强了ECG信号处理,以改善心律失常的检测.
- 微调的深度学习模型,特别是DenseNet-121,提供非常准确的心律分类.
- 这项研究有助于更可靠和更容易获得的心电图分析,以获得更好的患者结果.
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