混合机器学习模型用于通过使用自编码器和卷积特征从心电图信号中增强律乱检测
Subir Biswas1, Prabodh Kumar Sahoo2, Brajesh Kumar1
1Department of Computer Science and Engineering, C.V. Raman Global University, Bidya Nagar, Bhubaneswar, Odisha, India.
使用机器学习模型的自动心律失常检测显示出高准确度. 具有神经网络的自编码器特征在早期心脏病诊断中优于卷积特征.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 心脏病学 心脏病学
背景情况:
- 早期发现心脏病 (CD) 对于及时治疗至关重要.
- 电心电图 (ECG) 信号对于识别心律失常至关重要.
- 自动化心律失常检测系统可以帮助大规模的医疗保健查.
研究的目的:
- 开发和评估机器学习 (ML) 模型,以改善心电图心律失常检测.
- 为了比较自编码器和卷积特征提取方案的性能.
- 为了确定实时心律失常检测的表现最好的ML模型.
主要方法:
- 开发了八个ML模型,使用两个特征提取方案:自编码器和卷积式.
- 在MIT-BIH心律失常和ECG 5000数据集上训练和测试模型.
- 利用TOPSIS和mRMR对ML模型进行排名,并确定表现最佳的模型.
主要成果:
- 使用自动编码器特征的模型在卷积特征上表现出优越的性能.
- 混合Autoencoder特征与神经网络 (AEFNN) 模型在MIT-BIH数据集上实现了97.96%的准确性.
- 在ECG 5000数据集上,AEFNN模型实现了99.20%的准确性.
结论:
- 拟议的AEFNN模型对于准确和早期检测心律失常是有效的.
- 基于自动编码器的功能提高了用于心电图分析的ML模型性能.
- 这种方法可以支持及时诊断和介入心脏病管理.
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