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基于CBi-SAN网络的中心动脉压力波形从辐射压力波形的重建
Hanguang Xiao1, Wangwang Song1, Chang Liu1
1College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China.
一个新的CBi-SAN模型从辐射动脉压力 (RAP) 信号准确地重建了中央动脉压力 (CAP) 波形. 这种进步提供了比现有的心血管疾病监测方法更高的性能.
科学领域:
- 心血管生理学心血管生理学
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
背景情况:
- 中心动脉压 (CAP) 是心血管健康的关键指标,但其准确的非侵入性测量仍然具有挑战性.
- 传统的重建方法和一些深度学习模型在 CAP 波形的特征提取和准确性方面存在局限性.
研究的目的:
- 开发和验证一种新的深度学习模型,CBi-SAN,用于从辐射动脉压力 (RAP) 波形准确地从端到端重建CAP波形.
- 与现有的传统和深度学习方法相比,提高CAP波形重建的性能.
主要方法:
- 提出了一个新的CBi-SAN模型,整合了卷积神经网络 (CNN),双向长短期记忆 (BiLSTM) 和自我注意力机制.
- 该模型经过训练和验证,使用药物治疗前后62名患者的侵入性CAP和RAP波形数据进行了训练和验证.
- 使用平均绝对误差 (MAE),根平均平方误差 (RMSE) 和斯皮尔曼相关系数 (SCC) 评估了性能.
主要成果:
- 该CBi-SAN模型在CAP波形重建中表现出色,MAE为2.23 ± 0.11 mmHg,RMSE为2.21 ± 0.07 mmHg.
- 中枢动脉抽缩压 (CASP) (RMSE: 2.94 ± 0.48 mmHg) 和中枢动脉透缩压 (CADP) (RMSE: 1.96 ± 0.06 mmHg) 的重建精度更高.
- 在准确度指标方面,CBi-SAN模型显著超过了传统方法和其他深度学习模型.
结论:
- 与当前方法相比,CBi-SAN模型为非侵入性CAP波形重建提供了一种优越的方法.
- 该模型在重建CAP,CASP和CADP方面的高准确性为心血管疾病管理中的临床应用提供了显著的潜力.
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