On the Design of an Efficient Cardiac Health Monitoring System Through Combined Analysis of ECG and SCG Signals.
On the Design of an Efficient Cardiac Health Monitoring System Through Combined Analysis of ECG and SCG Signals.
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DOI：
10.3390/s18020379
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发表时间：
2018-01-28
期刊：
影响因子：
--
通讯作者：
Lee MY
中科院分区：
文献类型：
--
作者：
Sahoo PK;Thakkar HK;Lin WY;Chang PC;Lee MY
心血管疾病（CVD）是全球主要的公众关注和社会经济问题。目前流行的高端心脏健康监测系统，如磁共振成像（MRI）、计算机断层扫描（CT）和超声心动图（Echo）都非常昂贵，而且不支持在不影响患者日常生活活动（ADL）的情况下对患者进行长期连续监测。在本文中，探索使用不引人注目的传感器进行连续和无创心脏健康监测，旨在提供一种可行和低成本的替代方案，以在早期阶段预测可能的心脏异常。据了解，由于ECG仅以电脉冲的形式提供各种心脏活动的浅层信息，因此基于单一使用心电图（ECG）信号的心脏健康监测可能无法提供强大的见解。因此，一种新的低成本、无创的地震心动图（SCG）信号与ECG信号一起被研究用于鲁棒心脏健康监测。为此，设计了实验室数据采集模型，用于同时采集心电和SCG信号，并设计了自动描述采集到的心电和SCG信号中相关特征点的机制。此外，采用基于分离特征点的新方法来区分每个ECG和SCG心动周期的正常和异常形态。最后，通过设计Naïve贝叶斯条件概率模型对心电和SCG进行联合分析。在美国机构审查委员会（IRB）批准的、包含12000个心动周期的真实受试者的心电/SCG信号上进行的实验表明，所提出的特征点描绘机制和异常形态检测方法始终表现良好，并取得了令人满意的结果。此外，实验结果表明，与单独使用ECG和SCG相比，ECG和SCG信号联合分析可以提供更可靠的心脏健康监测。
Cardiovascular disease (CVD) is a major public concern and socioeconomic problem across the globe. The popular high-end cardiac health monitoring systems such as magnetic resonance imaging (MRI), computerized tomography scan (CT scan), and echocardiography (Echo) are highly expensive and do not support long-term continuous monitoring of patients without disrupting their activities of daily living (ADL). In this paper, the continuous and non-invasive cardiac health monitoring using unobtrusive sensors is explored aiming to provide a feasible and low-cost alternative to foresee possible cardiac anomalies in an early stage. It is learned that cardiac health monitoring based on sole usage of electrocardiogram (ECG) signals may not provide powerful insights as ECG provides shallow information on various cardiac activities in the form of electrical impulses only. Hence, a novel low-cost, non-invasive seismocardiogram (SCG) signal along with ECG signals are jointly investigated for the robust cardiac health monitoring. For this purpose, the in-laboratory data collection model is designed for simultaneous acquisition of ECG and SCG signals followed by mechanisms for the automatic delineation of relevant feature points in acquired ECG and SCG signals. In addition, separate feature points based novel approach is adopted to distinguish between normal and abnormal morphology in each ECG and SCG cardiac cycle. Finally, a combined analysis of ECG and SCG is carried out by designing a Naïve Bayes conditional probability model. Experiments on Institutional Review Board (IRB) approved licensed ECG/SCG signals acquired from real subjects containing 12,000 cardiac cycles show that the proposed feature point delineation mechanisms and abnormal morphology detection methods consistently perform well and give promising results. In addition, experimental results show that the combined analysis of ECG and SCG signals provide more reliable cardiac health monitoring compared to the standalone use of ECG and SCG.
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