Kim Sooho, Hahn Jin-Oh, Youn Byeng Dong
Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul 08826, South Korea.
OnePredict Inc., Seoul 06160, South Korea.
IEEE Access. 2021;9:127433-127443. doi: 10.1109/access.2021.3112678. Epub 2021 Sep 14.
This paper presents a novel deep learning-based arterial pulse wave analysis (PWA) approach to diagnosis of peripheral artery occlusive disease (PAD). Naïve application of deep learning to PAD diagnosis can be hampered by the fact that securing a large amount of longitudinal dataset encompassing diverse PAD severity as well as anatomical and physiological variability presents formidable challenge. Training of a deep neural network (DNN) to a small training dataset raises the risk of overfitting the PAD diagnosis algorithm only to the individuals in the training dataset while deteriorating its ability to generalize also to other individuals who may exhibit a large variability in anatomical and physiological characteristics beyond the training dataset. To overcome these obstacles, we propose a continuous property-adversarial regularization (CPAR) approach to robust generalization of a DNN against scarce datasets. Our approach fosters the exploitation of latent features that can facilitate the intended task independently of confounding property-induced disturbances. by regularizing the extraction of disturbance-dependent latent features in the network's feature extraction layer. By training and testing a deep convolutional neural network (CNN) for PAD diagnosis using scarce virtual datasets, we illustrated that the CNN trained by our approach was superior to a conventionally trained CNN in detecting and assessing the severity of PAD against disturbances originating from diversity in the patients' height and arterial stiffness: when trained with one-time pulse wave signal measurement at ankle and brachial arteries in a small number of patients, our approach achieved detection accuracy of >90% and severity assessment of 0.83 in r value, which were >15% and >40% improvement over conventional approach without CPAR. In addition, we ascertained the advantage of our approach in efficient training and robust generalization of DNN by contrasting it to multi-task learning which promotes the exploitation (as opposed to regularization in CPAR) of disturbance-dependent latent features in fulfilling the intended tasks.
本文提出了一种基于深度学习的新型动脉脉搏波分析（PWA）方法，用于诊断外周动脉闭塞性疾病（PAD）。将深度学习直接应用于PAD诊断可能会受到阻碍，因为要获取包含不同PAD严重程度以及解剖学和生理学变异性的大量纵向数据集是一项巨大的挑战。将深度神经网络（DNN）训练到一个小的训练数据集会增加PAD诊断算法仅过度拟合训练数据集中个体的风险，同时降低其对训练数据集之外解剖学和生理学特征可能存在较大变异性的其他个体的泛化能力。为了克服这些障碍，我们提出了一种连续属性对抗正则化（CPAR）方法，以使DNN针对稀缺数据集进行稳健泛化。我们的方法通过在网络的特征提取层中对依赖于干扰的潜在特征的提取进行正则化，促进对潜在特征的利用，这些潜在特征可以独立于混淆属性引起的干扰来促进预期任务。通过使用稀缺的虚拟数据集训练和测试用于PAD诊断的深度卷积神经网络（CNN），我们表明，通过我们的方法训练的CNN在检测和评估PAD严重程度以对抗源自患者身高和动脉僵硬度差异的干扰方面优于传统训练的CNN：当在少数患者的脚踝和肱动脉进行一次性脉搏波信号测量进行训练时，我们的方法实现了>90%的检测准确率和r值为0.83的严重程度评估，比没有CPAR的传统方法分别提高了>15%和>40%。此外，通过将我们的方法与多任务学习进行对比，我们确定了我们的方法在DNN的高效训练和稳健泛化方面的优势，多任务学习在完成预期任务时促进对依赖于干扰的潜在特征的利用（与CPAR中的正则化相反）。
