Department of Computer Science and Information Engineering, Chang Gung University, Guishan 33302, Taiwan.
Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Linkou 33305, Taiwan.
Sensors (Basel). 2018 Jan 28;18(2):379. doi: 10.3390/s18020379.
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.
心血管疾病(CVD)是全球范围内一个主要的公共关注和社会经济问题。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 扫描)和超声心动图(Echo),价格非常昂贵,并且不能在不干扰患者日常生活活动(ADL)的情况下支持长期连续监测。本文旨在探索使用非侵入性传感器进行连续和非侵入性心脏健康监测,旨在提供一种可行且低成本的替代方案,以便在早期预见可能的心脏异常。研究表明,仅基于心电图(ECG)信号的心脏健康监测可能无法提供有力的见解,因为 ECG 仅以电脉冲的形式提供有关各种心脏活动的浅层信息。因此,本文联合研究了一种新颖的低成本、非侵入性的心冲击图(SCG)信号和 ECG 信号,以进行稳健的心脏健康监测。为此,设计了实验室数据采集模型,用于同时采集 ECG 和 SCG 信号,然后为自动划定采集到的 ECG 和 SCG 信号中的相关特征点设计机制。此外,采用了一种基于单独特征点的新颖方法,用于区分每个 ECG 和 SCG 心脏周期中的正常和异常形态。最后,通过设计朴素贝叶斯条件概率模型对 ECG 和 SCG 进行联合分析。在机构审查委员会(IRB)批准的从真实受试者采集的 ECG/SCG 信号上进行的实验包含 12000 个心脏周期,实验结果表明,所提出的特征点划定机制和异常形态检测方法表现良好,给出了有前途的结果。此外,实验结果表明,与单独使用 ECG 和 SCG 相比,ECG 和 SCG 信号的联合分析提供了更可靠的心脏健康监测。