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. 2018 Jan 28;18(2):379.
doi: 10.3390/s18020379.

On the Design of an Efficient Cardiac Health Monitoring System Through Combined Analysis of ECG and SCG Signals

Prasan Kumar Sahoo  1   2 , Hiren Kumar Thakkar  3 , Wen-Yen Lin  4   5 , Po-Cheng Chang  6 , Ming-Yih Lee  7   8
Affiliations

On the Design of an Efficient Cardiac Health Monitoring System Through Combined Analysis of ECG and SCG Signals

Prasan Kumar Sahoo et al. Sensors (Basel). .

Abstract

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.

Keywords: cardiac anomalies; cardiovascular disease (CVD); electrocardiogram (ECG); seismocardiogram (SCG).

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Architectural view of ECG/SCG data collection model.
Figure 2
Figure 2
Cardiac electrical and mechanical activities.
Figure 3
Figure 3
Example of normal and abnormal ECG morphologies.
Figure 4
Figure 4
Feature points delineation in (a) normal ECG cycles; (b) abnormal ECG cycles.
Figure 5
Figure 5
Feature points delineation in (a) normal SCG cycles; (b) abnormal SCG cycles.
Figure 6
Figure 6
Evaluation of delineation of ECG feature point R and SCG feature point AO.
Figure 7
Figure 7
Example of ECG feature points, onset points and offset points.
Figure 8
Figure 8
Feature-variables derived from SCG Tricuspid valve site.
Figure 9
Figure 9
Combined evaluation of ECG and SCG signals using set of five cardiac cycles.
Figure 10
Figure 10
Combined evaluation of ECG and SCG signals using a set of 10 cardiac cycles.
Figure 11
Figure 11
Performance comparison of ECG only, SCG only, and ECG and SCG combined analysis.
See this image and copyright information in PMC

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