采用自注意力抗干扰网络的视频房颤检测

Video atrial fibrillation detection using self-attentional anti-interference network

房颤的早期发现与诊断是降低房颤以及并发症风险的关键。视频光电体积描记术(VPPG)技术为房颤筛查提供了新途径,但易受到现实场景中运动干扰。现有VPPG房颤检测方法存在运动干扰时会造成脉冲信号失真,从而发生误判。为解决以上问题,提出一种抗干扰视频房颤检测模型。该模型使用注意力编码器网络,从包含运动干扰的脉冲信号中提取鲁棒的脉冲潜在特征,径向基分类网络根据潜在特征实现房颤检测。注意力编码器将复杂脉冲信号映射到高维子空间,重点关注有效信息,提取稳健潜在特征。径向基分类网络在房颤标签监督下提高房颤识别能力,输出可靠结果。在200名测试者参与的自建数据集上进行实验,结果表明该模型在各类场景中均表现优异。在静态场景中,检测精度较最优对比算法提高了8.1%,敏感性提高了7.5%。在动态场景中,对比算法准确度均大幅下降,所提模型精度相比提升了16.5%,特异性提升了18.3%。模型具有良好的抗运动干扰能力,可有效地消除运动干扰影响,提高现实场景中视频房颤检测精度。

Early detection and diagnosis of atrial fibrillation is the key to reducing the risk of atrial fibrillation and complications.Although the video photoplethysmography(VPPG)technology provides a new approach to atrial fibrillation screening,it is susceptible to motion interference in real-world scenarios.When the existing VPPG atrial fibrillation detection method encounters motion interference,the pulse signal will be distorted,resulting in misjudgment.To solve the above problems,an anti-interference video atrial fibrillation detection model was proposed.The model employed an attention encoder network to extract robust spiking latent features from spiking signals containing motion disturbances.A radial basis classification network then performed atrial fibrillation detection based on these latent features.The attention encoder mapped complex impulse signals into high-dimensional subspaces,focusing on effective information and extracting robust latent features.Furthermore,Radial Basis Classification Network enhanced atrial fibrillation recognition ability under the supervision of atrial fibrillation labels and output reliable results.Experiments were carried out on a self-built dataset with 200 testers,and the results show that the proposed model performed well in various scenarios.In static scenes,the detection accuracy was 8.1%higher than the optimal comparison algorithm,and the sensitivity was 7.5%higher.In dynamic scenes,where the accuracy of the comparison algorithms was greatly reduced,the accuracy of the proposed model was improved by 16.5%,and the specificity was improved by 18.3%.The model demonstrated good anti-motion interference ability,effectively eliminating the influence of motion interference and improving the detection accuracy of video atrial fibrillation in real scenes.