School of Materials and Energy, State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, China.
IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, Guangdong, China.
J Healthc Eng. 2022 Apr 12;2022:3408501. doi: 10.1155/2022/3408501. eCollection 2022.
Recently, cardiac arrhythmia recognition from electrocardiography (ECG) with deep learning approaches is becoming popular in clinical diagnosis systems due to its good prognosis findings, where expert data preprocessing and feature engineering are not usually required. But a lightweight and effective deep model is highly demanded to face the challenges of deploying the model in real-life applications and diagnosis accurately. In this work, two effective and lightweight deep learning models named Deep-SR and Deep-NSR are proposed to recognize ECG beats, which are based on two-dimensional convolution neural networks (2D CNNs) while using different structural regularizations. First, 97720 ECG beats extracted from all records of a benchmark MIT-BIH arrhythmia dataset have been transformed into 2D RGB (red, green, and blue) images that act as the inputs to the proposed 2D CNN models. Then, the optimization of the proposed models is performed through the proper initialization of model layers, on-the-fly augmentation, regularization techniques, Adam optimizer, and weighted random sampler. Finally, the performance of the proposed models is evaluated by a stratified 5-fold cross-validation strategy along with callback features. The obtained overall accuracy of recognizing normal beat and three arrhythmias (V-ventricular ectopic, S-supraventricular ectopic, and F-fusion) based on the Association for the Advancement of Medical Instrumentation (AAMI) is 99.93%, and 99.96% for the proposed Deep-SR model and Deep-NSR model, which demonstrate that the effectiveness of the proposed models has surpassed the state-of-the-art models and also expresses the higher model generalization. The received results with model size suggest that the proposed CNN models especially Deep-NSR could be more useful in wearable devices such as medical vests, bracelets for long-term monitoring of cardiac conditions, and in telemedicine to accurate diagnose the arrhythmia from ECG automatically. As a result, medical costs of patients and work pressure on physicians in medicals and clinics would be reduced effectively.
最近，由于深度学习方法在心脏心律失常识别方面的良好预后发现，基于心电图（ECG）的心脏心律失常识别在临床诊断系统中变得越来越流行，通常不需要专家进行数据预处理和特征工程。但是，需要一个轻量级且有效的深度学习模型来应对在实际应用中部署模型和准确诊断的挑战。在这项工作中，提出了两个有效的轻量级深度学习模型，名为 Deep-SR 和 Deep-NSR，用于识别心电图心跳，它们基于二维卷积神经网络（2D CNN），同时使用不同的结构正则化。首先，从基准 MIT-BIH 心律失常数据集的所有记录中提取了 97720 个心电图心跳，并将其转换为 2D RGB（红、绿、蓝）图像，作为所提出的 2D CNN 模型的输入。然后，通过适当的模型层初始化、实时增强、正则化技术、Adam 优化器和加权随机采样器对所提出的模型进行优化。最后，通过分层 5 折交叉验证策略以及回调功能评估所提出模型的性能。基于美国医疗器械促进协会（AAMI）的正常心跳和三种心律失常（V-室性异位、S-室上性异位和 F-融合）的识别总准确率为 99.93%，Deep-SR 模型和 Deep-NSR 模型的准确率分别为 99.96%，这表明所提出的模型的有效性已经超过了最先进的模型，并且也表达了更高的模型泛化。根据模型大小得出的结果表明，所提出的 CNN 模型，特别是 Deep-NSR，在可穿戴设备（如医疗背心、用于长期监测心脏状况的手镯）和远程医疗中可能更有用，以自动准确诊断心电图中的心律失常。因此，将有效降低患者的医疗费用和医疗和诊所医生的工作压力。
