卷积长期短期记忆神经网络与分类器集成,用于对机械呼吸患者的异步呼吸类型进行分类
Nur Sa'adah Muhamad Sauki1, Nor Salwa Damanhuri1, Nor Azlan Othman1
1Electrical Engineering Studies, Universiti Teknologi MARA Cawangan Pulau Pinang, Permatang Pauh Campus, Pulau Pinang 13500, Malaysia.
Computer methods and programs in biomedicine
|February 23, 2025
概括
一个新的CNN-LSTM模型准确地对机械呼吸患者的异步呼吸 (AB) 进行了分类. 这种自动检测通过区分正常呼吸和关键AB类型来改善呼吸器管理和患者的结果.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 呼吸系统生理学 呼吸系统生理学
背景情况:
- 异步呼吸 (AB) 发生在患者的呼吸和机械呼吸 (MV) 不一致时.
- AB可以对患者的康复,结果和MV管理产生负面影响.
- 准确的AB类型分类对于自动检测和改善临床护理至关重要.
研究的目的:
- 开发和验证一种基于模型的方法,用于准确分类异步呼吸 (AB) 类型.
- 在机械呼吸患者中自动检测AB和正常呼吸 (NB).
- 通过精确的AB分类来改善机械通风器管理.
主要方法:
- 使用了一个具有长短期记忆 (CNN-LSTM) 架构的1维卷积神经网络.
- 输入数据包括7名ICU患者 (4500次呼吸) 的气道压力.
- 将分类器 (随机森林,SVM,后勤回归) 与CNN-LSTM集成,将呼吸分类为反向触发 (RT),过早循环 (PC) 和正常呼吸 (NB).
- 模型性能使用k-fold交叉验证和混矩阵进行评估.
主要成果:
- 带有分类器的1D CNN-LSTM模型通过5倍交叉验证实现了100%的准确性.
- 结合CNN-LSTM与分类器的模型显示出卓越的性能,精度,灵敏度,特异性和F1得分超过83.5%.
- 没有分类器的模型表现较差,平均F1分数低于71.8%.
结论:
- 带有分类器的CNN-LSTM神经网络模型有效地检测和分类AB和NB.
- 这种基于模型的方法提供了AB类型的精确分类和与正常呼吸的区别.
- 经过验证的模型有可能提高床边机械呼吸机的管理,并保证进一步的临床测试.
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