基于云模型的体震信号智能诊断方法

研究了使用云模型理论智能诊断体震信号中心率异常信息的方法.利用云模型将定性的专家诊断体系与定量的计算机辅助诊断系统相结合,模拟了专家诊断过程.建立了心率异常智能诊断规则云模型,构造了体震信号JJ间期分布曲线图,以此自动校正模型参数,最终建立智能诊断机制.采用实验室搭建的体震信号实时采集系统提取2 000组样本作为采样对象,并与传统标准阈值法进行对比,验证了所提方法的可行性.实验结果表明:该方法自动聚类的准确率达到90.2%,高于传统方法 2个百分点.

BCG Signal Intelligent Diagnosis Method Based on Cloud Model

The method to diagnose intelligently abnormal information from heart rate by BCG signal ( ballistocardiogram signal) based on cloud model was studied. The qualitative expert diagnosis system and quantitative computer aided diagnosis system were combined by using the cloud model， and the expert diagnosis process was simulated. A cloud model for abnormal heart rate intelligent diagnosis was established. Then， distribution curve diagram of JJ interval in BCG signal was constructed， and the model parameters were adjusted automatically to establish the intelligent diagnosis mechanism. The signal acquisition system in our laboratory was used to extract 2000 groups of BCG signal as the sample object， the feasibility of the proposed method was verified by comparing with the traditional standard threshold method. The experiment results showed that the automatic clustering accuracy of the proposed method could reach 90.2%， which was 2 percentage points higher than that of the traditional method.