优化了用于血压分类的深度神经网络模型,使用基于富里埃分析的光电聚光学信号的时间频谱图
Pankaj1, Ashish Kumar2, Manjeet Kumar3
1Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India.
这项研究估计血压 (BP) 来自光电显微镜 (PPG) 信号,即使有运动器件. 使用富里埃分解的深度神经网络方法在分类高血压方面取得了96.5%的准确性,有助于预防心血管疾病.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 心血管疾病 (CVD) 构成重大健康风险,高血压是主要原因.
- 通过持续监测和快速诊断,有效的血压管理对于预防心血管疾病至关重要.
- 光电显微镜 (PPG) 信号为生理监测提供了一种非侵入性方法,但易受运动工件的影响.
研究的目的:
- 开发一种实时方法,从运动工件影响的PPG信号中估计BP.
- 为准确的PPG信号分类提出一个深度神经网络 (DNN) 方法.
- 将PPG信号分为正常压力,高血压前期和高血压类别.
主要方法:
- 使用里埃分解法 (FDM) 将PPG信号转化为时间频率 (TF) 谱图.
- 修改预训练DNN的最后三层 (GoogleNet,DenseNet,AlexNet) 用于PPG信号分类.
- 培训和测试使用五重交叉验证方法对MIMIC-III和PPG-BP数据库的框架.
主要成果:
- 与DenseNet-201网络一起提出的框架实现了96.5%的测试准确性.
- FDM有效计算了TF光谱图,将运动工件和噪声与PPG信号分开.
- 经过清洁PPG信号特征训练的DNN显示了对实时BP分类的改进的一般化能力.
结论:
- 基于DNN的方法使用FDM生成的TF光谱图准确估计BP从噪音PPG信号.
- 在PPG数据中对BP状态进行分类时,DenseNet-201表现出卓越的性能.
- 这种方法增强了实时,非侵入性血压监测和高血压查的潜力.
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