一个基于多功能伪颜色映射的轻量级网络,用于识别心律失常
Yijun Ma1, Junyan Li2, Jinbiao Zhang3
1School of Mechanical, Electrical and Information Engineering, Shandong University, Wenhuaxi Road, Weihai, 264209 Shandong China.
Health information science and systems
|September 6, 2024
概括
一个新的多功能伪色绘图 (MfPc绘图) 和FlexShuffleNet模型准确地从心电图 (ECG) 数据中分类心跳. 这种方法可以在基准数据集上提高心律失常的诊断准确度.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 准确的心跳分类对于诊断心律失常至关重要.
- 现有的方法通常需要复杂的特征工程或计算密集型模型.
研究的目的:
- 开发和评估一种使用多功能伪颜色映射 (MfPc映射) 和轻量级神经网络 (FlexShuffleNet) 进行心跳分类的新方法.
- 通过先进的数据可视化和精简的分类模型,提高心律失常诊断的解释性和效率.
主要方法:
- 一维 (1D) 电心电图 (ECG) 记录的预处理,包括消除噪声和移除基线漂移.
- 预处理的心跳转化为二维 (2D) 多功能RGB图,使用MfPc映射进行增强的可视化.
- 使用轻量级,可适应的FlexShuffleNet模型对14个心跳类别进行分类.
主要成果:
- 在MIT-BIH心律失常数据库中实现了14类心跳分类的高精度 (99.77%) 和F1得分 (0.9125).
- 在单独的山东省医院数据库中表现出强的表现,准确率为92.08%,F1得分为0.9315 .
- MfPc Mapping为心电图信号提供了出色的解释性和数据可视化.
结论:
- 拟议的MfPc Mapping与FlexShuffleNet相结合,为心跳分类提供了一个高度准确和高效的方法.
- 这种方法在改善临床环境中心律失常的诊断方面具有显著的潜力.
- 由于FlexShuffleNet的轻量化特性,可以适应不同复杂性的分类,使其适用于各种应用.
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