摘要
针对心房颤动疾病诊断检测复杂,病理检查有创等问题,构建基于脉搏波与深度学习的心房颤动分类预测模型,实现对心房颤动疾病的准确预测。首先,通过脉搏波设备采集数据,与MIMIC-Ⅲ数据库数据共同构建PPG-AF数据集;其次,基于Pytorch深度学习框架构建用于房颤分类的ResNet-CBAM-1DCNN双通道卷积神经网络;最后,将数据集按照8:1:1的比例划分为训练集,验证集和测试集,将脉搏波和其对应的格拉姆角场图作为输入,通过对网络结构和超参数的优化,在测试集中分类的F1分数达到了97.30%,准确度达到98.12%。本研究基于脉搏波信号与双通道卷积神经网络模型,能够实现对心房颤动疾病的准确诊断,有望为临床医师制定最佳治疗决策提供重要依据。
To address the problems in diagnosis and detection of atrial fibrillation(AF)and invasive pathological examination,a model for AF classification based on pulse waves and deep learning is constructed to realize the accurate prediction of AF.The data collected from the photoplethysmography(PPG)acquisition device and the MIMIC-III database data are used to establish PPG-AF dataset,and a ResNet-CBAM-1DCNN dual-channel convolutional neural network for AF classification is constructed based on the Pytorch deep learning framework.The established dataset is divided into a training set,a validation set and a test set in a ratio of 8:1:1.The PPG and its corresponding Gramian angular field map are taken as input.After the optimization of network structure and hyperparameters,the proposed model obtains a F1 score of 97.30%in the test set,and has an accuracy of 98.12%for AF classification.The multimodal fusion approach based on PPG and dual-channel convolutional neural network can achieve the accurate diagnosis of AF,which is expected to provide an important basis for decision-making in clinic.
作者
张瑞芳
梁永波
崔谋
陈真诚
ZHANG Ruifang;LIANG Yongbo;CUI Mou;CHEN Zhencheng(School of Life and Environmental Sciences,GuiLin University of Electronic Technology,GuiLin 541004,China;Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments,Guilin 541004,China;Guangxi Engineering Technology Research Center of Human Physiological Information Noninvasive Detection,Guilin 541004,China)
出处
《中国医学物理学杂志》
CSCD
2023年第10期1260-1269,共10页
Chinese Journal of Medical Physics
基金
国家自然科学基金(61627807,62101148)
广西自然科学基金(2020GXNSFBA297156)
广西创新驱动发展专项(Guike AA19254003)。
