使用顺序过渡模式特征工程技术与生理信号开发的新型准确分类系统
Mehmet Ali Gelen1, Prabal Datta Barua2, Irem Tasci3
1Department of Cardiology, Elazig Fethi Sekin City Hospital, Elazig, Turkey.
Scientific reports
|May 1, 2025
概括
本研究介绍了一种可解释特征工程 (XFE) 模型,使用顺序过渡模式 (OTPat) 进行精确的EEG和ECG信号分类. 新的框架达到95%以上的准确性,提供可解释的连接组图.
科学领域:
- 生物医学信号处理
- 医疗保健中的机器学习
- 可解释的人工智能
背景情况:
- 精确的脑电图 (EEG) 和心电图 (ECG) 信号的分类对于诊断神经和心脏疾病至关重要.
- 现有的方法往往缺乏解释性,阻碍了临床采用.
- 特性工程在提高复杂生物医学信号的分类性能方面发挥着至关重要的作用.
研究的目的:
- 开发一种新的可解释特征工程 (XFE) 框架,用于高精度的EEG和ECG信号分类.
- 提高机器学习模型在生物医学信号分析中的可解释性.
- 验证关于不同EEG和ECG数据集的拟议框架.
主要方法:
- 利用顺序过渡模式 (OTPat) 功能提取器来捕获信号中的空间和时间模式.
- 使用累积加权代邻域组件分析 (CWINCA) 进行特征选择.
- 使用t-算法k-近邻 (tkNN) 分类器对特征进行分类.
- 通过使用Directed Lobish (DLob) 和 Cardioish符号语言生成可解释的结果,用于连接组图.
主要成果:
- 在多个EEG和ECG数据集上实现了超过95%的分类准确性.
- 在一个具有挑战性的8类EEG文物数据集上显示了86.07%的准确性.
- 基于OTPat的XFE模型提供了清晰,可解释的连接组图,用于结果可视化.
结论:
- 拟议的基于OTPat的XFE模型为生物医学信号分类提供了强大的,高度准确的解决方案.
- 该框架的可解释性,通过符号语言,增强了对临床应用的信任和理解.
- 这种方法在促进神经学和心脏病学诊断能力方面具有重大潜力.
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