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基于张量的心电图异常检测在健康物联网中的心脏监测

Tensor-Based ECG Anomaly Detection toward Cardiac Monitoring in the Internet of Health Things.

机构信息

Department of Industrial, Manufacturing, and Systems Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA.

出版信息

Sensors (Basel). 2021 Jun 17;21(12):4173. doi: 10.3390/s21124173.

Abstract

Advanced heart monitors, especially those enabled by the Internet of Health Things (IoHT), provide a great opportunity for continuous collection of the electrocardiogram (ECG), which contains rich information about underlying cardiac conditions. Realizing the full potential of IoHT-enabled cardiac monitoring hinges, to a great extent, on the detection of disease-induced anomalies from collected ECGs. However, challenges exist in the current literature for IoHT-based cardiac monitoring: (1) Most existing methods are based on supervised learning, which requires both normal and abnormal samples for training. This is impractical as it is generally unknown when and what kind of anomalies will occur during cardiac monitoring. (2) Furthermore, it is difficult to leverage advanced machine learning approaches for information processing of 1D ECG signals, as most of them are designed for 2D images and higher-dimensional data. To address these challenges, a new sensor-based unsupervised framework is developed for IoHT-based cardiac monitoring. First, a high-dimensional tensor is generated from the multi-channel ECG signals through the Gramian Angular Difference Field (GADF). Then, multi-linear principal component analysis (MPCA) is employed to unfold the ECG tensor and delineate the disease-altered patterns. Obtained principal components are used as features for anomaly detection using machine learning models (e.g., deep support vector data description (deep SVDD)) as well as statistical control charts (e.g., Hotelling T2 chart). The developed framework is evaluated and validated using real-world ECG datasets. Comparing to the state-of-the-art approaches, the developed framework with deep SVDD achieves superior performances in detecting abnormal ECG patterns induced by various types of cardiac disease, e.g., an F-score of 0.9771 is achieved for detecting atrial fibrillation, 0.9986 for detecting right bundle branch block, and 0.9550 for detecting ST-depression. Additionally, the developed framework with the control chart facilitates personalized cycle-to-cycle monitoring with timely detected abnormal ECG patterns. The developed framework has a great potential to be implemented in IoHT-enabled cardiac monitoring and smart management of cardiac health.

摘要

高级心脏监测器,特别是那些通过物联网 (IoHT) 实现的监测器,为连续采集心电图 (ECG) 提供了绝佳的机会,因为心电图包含了有关潜在心脏状况的丰富信息。要充分发挥 IoHT 支持的心脏监测的潜力,在很大程度上取决于从采集的 ECG 中检测到疾病引起的异常。然而,当前基于物联网的心脏监测文献中存在一些挑战:

  1. 大多数现有方法基于监督学习,这需要正常和异常样本进行训练。这在实践中是不切实际的,因为通常无法知道在心脏监测期间何时以及会出现何种异常。

  2. 此外,利用先进的机器学习方法处理一维 ECG 信号的信息处理具有一定的难度,因为大多数方法都是针对二维图像和更高维数据设计的。为了解决这些挑战,提出了一种基于新型传感器的、用于基于物联网的心脏监测的无监督框架。首先,通过 Gramian Angular Difference Field (GADF) 从多通道 ECG 信号生成高维张量。然后,采用多线性主成分分析 (MPCA) 展开 ECG 张量并描绘疾病改变的模式。获得的主成分被用作机器学习模型(例如,深度支持向量数据描述 (deep SVDD)) 和统计控制图(例如,Hotelling T2 图)进行异常检测的特征。该框架使用真实 ECG 数据集进行评估和验证。与最先进的方法相比,使用 deep SVDD 的开发框架在检测各种类型的心脏疾病引起的异常 ECG 模式方面表现出色,例如,检测心房颤动的 F 分数为 0.9771,检测右束支传导阻滞的 F 分数为 0.9986,检测 ST 压低的 F 分数为 0.9550。此外,使用控制图的开发框架便于实现个性化的逐周期监测,并及时检测到异常 ECG 模式。该框架具有在物联网支持的心脏监测和心脏健康的智能管理中实施的巨大潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f247/8234952/f7b5be71c432/sensors-21-04173-g001.jpg

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