Electrical Engineering Department, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia.
Engineering Department, Faculty of Engineering, Alexandria University, Alexandria 5424041, Egypt.
Sensors (Basel). 2023 Jan 26;23(3):1365. doi: 10.3390/s23031365.
Automated electrocardiogram (ECG) classification using machine learning (ML) is extensively utilized for arrhythmia detection. Contemporary ML algorithms are typically deployed on the cloud, which may not always meet the availability and privacy requirements of ECG monitoring. Edge inference is an emerging alternative that overcomes the concerns of cloud inference; however, it poses new challenges due to the demanding computational requirements of modern ML algorithms and the tight constraints of edge devices. In this work, we propose a tiny convolutional neural network (CNN) classifier for real-time monitoring of ECG at the edge with the aid of the matched filter (MF) theory. The MIT-BIH dataset with inter-patient division is used for model training and testing. The model generalization capability is validated on the INCART, QT, and PTB diagnostic databases, and the model performance in the presence of noise is experimentally analyzed. The proposed classifier can achieve average accuracy, sensitivity, and F1 scores of 98.18%, 91.90%, and 92.17%, respectively. The sensitivity of detecting supraventricular and ventricular ectopic beats (SVEB and VEB) is 85.3% and 96.34%, respectively. The model is 15 KB in size, with an average inference time of less than 1 ms. The proposed model achieves superior classification and real-time performance results compared to the state-of-the-art ECG classifiers while minimizing the model complexity. The proposed classifier can be readily deployed on a wide range of resource-constrained edge devices for arrhythmia monitoring, which can save millions of cardiovascular disease patients.
使用机器学习 (ML) 进行自动化心电图 (ECG) 分类广泛用于检测心律失常。现代 ML 算法通常部署在云端，但云端推理可能并不总是满足 ECG 监测的可用性和隐私要求。边缘推理是一种新兴的替代方案，可以克服云推理的问题；然而，由于现代 ML 算法的计算要求较高，以及边缘设备的严格限制，它带来了新的挑战。在这项工作中，我们提出了一种基于匹配滤波器 (MF) 理论的边缘实时 ECG 监测小卷积神经网络 (CNN) 分类器。使用 MIT-BIH 数据集进行患者间划分进行模型训练和测试。模型的泛化能力在 INCART、QT 和 PTB 诊断数据库上进行了验证，并对模型在噪声存在下的性能进行了实验分析。所提出的分类器可以实现平均准确率、敏感度和 F1 分数分别为 98.18%、91.90%和 92.17%。检测室上性和室性异位搏动 (SVEB 和 VEB) 的敏感度分别为 85.3%和 96.34%。模型大小为 15 KB，平均推理时间不到 1 ms。与最先进的 ECG 分类器相比，所提出的模型在最小化模型复杂度的同时实现了卓越的分类和实时性能。所提出的分类器可以轻松部署在广泛的资源受限的边缘设备上，用于心律失常监测，可以为数百万心血管疾病患者提供帮助。
