基于一维卷积神经网络的实时心脏按压评估

在评估胸外心脏按压加速度波形时,现有的利用加速度波形积分计算胸外心脏按压距离的方法多数存在积分漂移、误差累积的问题。在波形分割和标签修正的基础上,提出一种基于一维卷积神经网络的胸外心脏按压波形的识别算法。对滤波后的数据进行脉冲识别,使用滑动窗口模型分割识别后的脉冲得到单次按压的加速度波形,根据数据离散程度对标签进行修正,解决标签可信度低的问题,在此基础上运用学习率衰减、Adam算法等构建一维卷积神经网络模型并进行优化。实验结果表明,该算法基于一维卷积神经网络的分类正确率达到99.4%,对比传统的积分算法、BP神经网络算法提升近5%,且不受按压遮挡、电磁波干扰等因素的影响,对于胸外心脏按压评估具有良好的效果。

Real-time Cardiac Massage Assessment Based on One-dimensional Convolutional Neural Network

For the assessment of the acceleration waveform of the external cardiac massage,the existing methods of calculating the depth of cardiac massage using the acceleration waveform integral have the problems of integral drift and error accumulation.On the basis of waveform segmentation and label correction,this paper proposes a recognition algorithm based on one-dimensional convolutional neural network for external cardiac massage waveform.The filtered data is pulse-recognized and the recognized pulse is segmented with the sliding window model to obtain the acceleration waveform of a single massage.Then the data tags are corrected according to the degree of data discretization,which solves the problem of low label credibility.A one-dimensional convolutional neural network model is established and optimized by using learning rate decay and the Adam algorithm.Experimental results show that the one-dimensional convolutional neural network achieves an average accuracy of 99.4%,which is nearly 5%higher than the traditional integral algorithm and BP neural network algorithm.Also,the method is not affected by factors such as massage occlusion and electromagnetic interference,having a good effect on the assessment of external cardiac massage.