在心血管疾病诊断中平衡能源消耗和检测准确性:使用心电图和PCG信号的基于尖端神经网络的方法
Guihao Ran1, Yijing Wang1, Han Zhang1
1School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
这项研究引入了一个新的尖端神经网络 (SNN) 框架,用于检测心血管疾病 (CVD) 使用结合心电图 (ECG) 和心电图 (PCG) 信号. 该方法平衡了医疗器械的诊断准确性和低能耗.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 心脏病学 心脏病学
背景情况:
- 心电图 (ECG) 和心电图 (PCG) 信号对于心血管疾病 (CVD) 诊断至关重要.
- 现有的多式联运方式往往忽视了诊断准确性和能源效率之间的平衡.
研究的目的:
- 开发一种低功耗的多式联络框架,用于使用尖端神经网络 (SNN) 进行心血管疾病检测.
- 评估多式联网诊断系统中分类性能和能源消耗之间的权衡.
主要方法:
- 一个差异信号级融合策略从ECG和PCG数据中创建了一个融合的EPCG信号.
- 时间频率特征是使用自适应超级子变换 (ASLT) 提取的.
- 训练了两个尖端卷积神经网络 (SCNN) 模型,并进行了基于信任的动态决策级 (CDD) 融合,以获得最终分类.
主要成果:
- 拟议的多式联运框架在PhysioNet/CinC Challenge 2016数据集上实现了89.74%的准确性和89.08%的AUC.
- 该系统的低能耗为209.6μJ.
- 该方法在性能平衡方面表现优于单模信号方法.
结论:
- 开发的基于SNN的框架有效地平衡了诊断性能和CVD检测的能源效率.
- 这种方法为创建低功耗多式联运医疗诊断系统提供了一个可行的策略.
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