Next Article in Journal
Emotional Response to Vibrothermal Stimuli
Previous Article in Journal
Research on Non-Contact and Non-Fixed Cable Force Measurement Based on Smartphone
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 11
Issue 19
10.3390/app11198896
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

The Latest Progress and Development Trend in the Research of Ballistocardiography (BCG) and Seismocardiogram (SCG) in the Field of Health Care

by
Xiuping Han
1,2,
Xiaofei Wu
1,
Jiadong Wang
1,
Hongwen Li
1,
Kaimin Cao
1,
Hui Cao
3,
Kai Zhong
1 and
Xiangdong Yang
1,2,*
1
Yangtze Delta Region Institute of Tsinghua University, Jiaxing 314006, China
2
Department of Mechanical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
3
Keeson Technology Corporation Limited, Jiaxing 314016, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2021, 11(19), 8896; https://doi.org/10.3390/app11198896
Submission received: 12 July 2021 / Revised: 10 September 2021 / Accepted: 16 September 2021 / Published: 24 September 2021
Browse Figures
Review Reports Versions Notes

Abstract

:

Featured Application

Our team currently works on the IPBS (IPBS for the “Internet + Smart bed”) system based on the BCG, using AI and Big Data algorithm to perform health management. After testing our system for a while, we found out that the IPBS system is feasible and effective in elder chronic diseases management. We believe it will bring a solution to the ageing population and create economic benefits.

Abstract

The current status of the research of Ballistocardiography (BCG) and Seismocardiogram (SCG) in the field of medical treatment, health care and nursing was analyzed systematically, and the important direction in the research was explored, to provide reference for the relevant researches. This study, based on two large databases, CNKI and PubMed, used the bibliometric analysis method to review the existing documents in the past 20 years, and made analyses on the literature of BCG and SCG for their annual changes, main countries/regions, types of research, frequently-used subject words, and important research subjects. The results show that the developed countries have taken a leading position in the researches in this field, and have made breakthroughs in some subjects, but their research results have been mainly gained in the area of research and development of the technologies, and very few have been actually industrialized into commodities. This means that in the future the researchers should focus on the transformation of BCG and SCG technologies into commercialized products, and set up quantitative health assessment models, so as to become the daily tools for people to monitor their health status and manage their own health, and as the main approaches of improving the quality of life and preventing diseases for individuals.

1. Introduction

For the past 20 years, with the innovation of sensing technology [1], many new technologies have emerged in the academic circles, enabling scholars to expand their research fields extensively, and have the opportunities to make use of the technologies that were unable to get in-depth research due to technical limitations in the past. At this time, Ballistocardiography (BCG) and Seismocardiogram (SCG) technologies see their revival.

1.1. Ballistocardiogram

The BCG signal records the movement of the heart as it pumps blood to shift the body’s center of gravity during the heart beating cycle. It is the indirect medium for interpretation of heart activities [2,3]. Its earliest discovery dated back to 1877, when Gordon et al. [4] observed the phenomenon of BCG signals, i.e., when a human body stood on a scale, the scale pointer swung regularly in synchronization with the heartbeat. This was the earliest BCG record. Then, in 1939, Starr et al. [5] were the first to systematically study body trembling caused by heart contraction. They also designed and made a platform capable of recording body movement, and systematically studied the relationship between the formation mechanism of various waveforms in BCG signals and the heart dynamics, thus laid the foundation for researching the subject of BCG [3,6]. Thereafter, during the period of 1940 to 1980, the academic circles proposed various methods for detection of BCG signals, to apply BCG technology to clinical medicine for research of various physiological parameter detection [6,7,8]. The expression of its typical waveforms is shown in Figure 1. The significant morphological features of its output can be used to analyze human health information [9,10]. For example, the amplitude of IJ segment reflects fluctuation of cardiac output (CO), associated with difference in aortic blood pressure [11]; and peak J, the highest peak, corresponds to the movement of ventricular valves and can be used for monitoring to predict cardiac function [12].
Figure 1 shows Electrocardiogram (ECG), Ballistocardiogram (BCG), and tri-axial Seismocardiogram (SCG) in a human volunteer [13]. For the SCG, the x, y, and z subscripts correspond to the medial-lateral, the cranialcaudal, and the posterior-anterior axes respectively. The S1 and S2 components, and their respective fiducial markers are shown labelled: MC = mitral valve closure, AO = aortic valve opening, RE = rapid ejection, AC = Aortic valve closing, MO = mitral valve opening and RF = rapid filling.
But after the 1980s, the research of BCG was gradually stopped [6,14] for the reasons of its bulky equipment and complicated signal analysis, which caused the researchers to turn their sight to electrocardiograph (ECG) shows as Figure 1. ECG is a graphical representation of heart voltage over time measured by electrodes placed on the skin. It is a standard non-invasive diagnostic and monitoring technique used to record far-field cardiac electrophysiological signals. During normal sinus rhythm (NSR), the ECG will produce a stable and reproducible waveform of healthy subjects. This waveform is used here to illustrate the heart cycle [13]. The ECG has some limitations, it needs to be performed by trained physicians or sonographers. Their acquisition and interpretation are operator-dependent and show non-negligible intra-operator and inter-operator variability, raising questions about their reproducibility while the acquisition and interpretation should be fully automated and quantitatively reproducible. Thus, since 2000, BCG has been popular again in research around the world for its medical potential [6].

1.2. Seismocardiogram

The SCG signal presents the vibration due to the pressure within the body and the acceleration of the blood during the diastole and systole of the cardiac cycle [15,16]. Compared with BCG and ECG signals, SCG waveform is more responsive to the changes in heart function, because its signal is directly caused by heart vibration [17,18]. The SCG monitoring position is usually set near the sternum of the human body, as shown in Figure 2. The signal is measured from the dorsal ventral direction, from the front of the subject’s chest to the back [19,20,21,22]. Figure 1 shows a typical ECG, head-to-foot BCG, and tri-axial SCG. The SCG records the accelerations of the chest wall and is thus presented in units of milligram. In the SCG signal from AO to AC, the heart is in systole, while in the rest periods, the heart is in diastolic [23]. Choudhary’s team [15,24,25] carried out research on the physiological waveform of SCG signal and the opening and closing of the corresponding heart aortic valve, to mark the start and end time of cardiac systolic and diastolic periods, reflect the strength of myocardial contraction, and accurately mark abnormal information in cardiac activities [19].
The research of SCG started from the 1960s [26]. This technology was initially used in space programs to monitor health of astronauts, and then gradually applied to clinical medicine [14]. The development of SCG was very similar to that of BCG, both declined due to limitations of technology development, and have risen again for their technical potential [26].

1.3. Revival of BCG/SCG

Since 2003, researches related to SCG/BCG technology have sprung up like mushrooms after a spring rain, with the publications of research in large quantities and high frequency, as Jähne-Raden [27] put it, the year 2003 was “the starting point for the revival of the BCG/SCG research”.
One of the highlights of BCG/SCG technology that attracts researchers is its non-invasive characteristics, i.e., the measurement can be done without direct contact with the human body [28,29]. It can be seen from Figure 3 that this non-invasive sensing technology can be mounted on a variety of carriers for measurement. Part a shows a BCG-based chair that using cuffless blood pressure measurement methods that detect the peak pressure via signals measured using photoplethysmogram (PPG) and electrocardiogram (ECG) sensors the pulse transit time (PTT) or pulse wave velocity (PWV) have been studied [30]. Part b shows a BCG-based mattress which had three accelerometers to measure the ballistocardiogram (BCG) [31]. Part c shows an ECG-based weighing scale which measures using dry electrodes R-J intervals were extracted as a BP correlated parameter at every cardiac cycle [32]. Part d shows a BCG-based camera A robust HR measurement method was proposed and, a bidirectional optical flow algorithm is designed to select and track valid features in the video captured by the camera [33]. Part e shows a PPG-based wearable device using a multi-location wireless vital signs monitor and head movement PPG signal [34]. Part f shows portable device placed on sternum reading simultaneously ECG and BCG signals during the flight [35]. BCG/SCG can be used in different application scenarios, such as a ward [36], a hospital waiting room [37], a MRI room [38], or at home [39], for monitoring physical health status.
Wearable devices can be carried easily in daily life realizing zero load measurement on human body [40]. With the gradual maturity of the technology related to measurement of BCG/SCG signals, the development and production costs have been gradually reduced [41], so as to meet the market demands, and be applied in wider fields such as medical treatment, health, and military applications [42]. The purpose of this study was to review the existing literature by means of bibliometrics, systematically discuss the development and trends of BCG/SCG technology since 2003 worldly, with the emphasis on the progress of the research of application of the technology, and exploration of the realization of the future technology, by analyzing the important research directions, to provide references for the research of BCG/SCG technology in the field of medical, health care and nursing.

2. Materials and Methods

2.1. Data Collection

Because BCG/SCG technology research involves multiple systems and disciplines, it is an interdisciplinary and crossover field. In order to include as many and accurate as possible the relevant documents in the field of medical treatment, health care and nursing, this study used two large databases, the compressive literature database China National Knowledge Infrastructure (CNKI) and PubMed for the publications in the recent 20 years, as the source of data retrieval for research of BCG/SCG technology. The main retrieval strategy was the papers containing “BCG/SCG technology” in the title, key words, or abstract. The supplementary retrieval strategies were: ① the papers containing “BCG/SCG technology” in the title, key words, or abstract. In PubMed and CHKI, “Ballistocardiography”, “Ballistocardiogram”, “Seismocardiography” and “Seismocardiogram” were used as the key words for retrieval; ② the focus of discussion in this study was the progress of the research in application, with the emphasis on the analysis of the field of medical treatment and health management. The relevant topics included detection/monitoring of basic vital signs, analysis of sleep phase, testing of cardiovascular function parameters, etc. The document types: to include literature that reflected the original research and innovation in theoretical perspectives. The retrieval period started from January 2003 and ended in December 2020, with the results of 368 related articles obtained.

2.2. Data Processing

The documents retrieved from CNKI and PubMed were used for analyzing functional analysis data, and the basic laws of bibliometrics were used as the method of data analysis, to make compressive analyses of the annual changes, main countries/regions, types of research, frequently-used subject words, and important research subjects of application of BCG/SCG technology in the field of medical treatment and health.

2.3. Standards of Classification

In order to have a compressive analysis of the direction and focus of BCG/SGC research on a global scale, the relevant documents were classified for their research fields, research environments, and research samples. Classification was done in accordance with the standards listed in Table 1 (In this study, the literature was classified according to the mutually compatible standards). For the study, analysis would be made on the representative articles selected from the classified documents.

3. Results

3.1. Annual Changes

In accordance with the above standards for classification, a total of 368 relevant documents were finally screened out from the two databases. The hot terms of BCG/SCG technology and the year-by-year changes of the number of documents in the field of medical treatment and health management, and the proportion of research focus in different fields can be obtained from Figure 4, Figure 5 and Figure 6. It can be seen that the scope of research and practice of this technology has been expanded, and the number of documents showed a trend of sharp increase. The developed countries headed by the US have made remarkable achievements in the research in this field, with the hot terms of their researches mainly focused on detection/monitoring of basic vital signs of human body (such as the indicators of heart rate and blood pressure), analysis of sleep phase, testing of cardiovascular function parameters, diagnosis of cardiovascular diseases, home care for the aged, and the evaluation tools for intelligent health management, etc.

3.2. Areas of Research Focus

It was discovered in literature study that the main concern of academia was how to promote the application practice of BCG/SCG technology, with the focuses on such aspects as signal model establishment [43,44,45], signal acquisition and measurement methods [46,47,48], signal processing approaches [49,50,51,52,53], and signal standardization [54]. At the same time, the limitations were also exposed in the study, which included great variability in the waveform itself [55,56,57,58], difficulty in signal standardization system [6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24], and complexity of independent measurement and analysis of signals [59,60].

3.3. Medicine

Application of BCG/SCG in the field of medical treatment is focused on cardiac activity analysis, respiratory monitoring, and sleep screening.

3.3.1. Cardiac Activity Analysis

There are many applications of BCG/SCG in cardiac activity analysis, such as diagnosis of cardiovascular diseases [61,62,63,64], monitoring of cardiovascular status of a patient [37,65,66,67,68,69], and assisting in evaluation of human cardiovascular health level [70,71,72]. It should be noted that ECG, as the golden standard for the means of cardiovascular monitoring, always takes the leading position [73,74], therefore, BCG/SCG research, often with reference of ECG measurement results, assists waveform reference point in signal analysis [75,76].

3.3.2. Respiratory Monitoring

BCG/SCG signals can accurately detect human respiratory status [77,78], evaluate the corresponding respiratory status of human body [79,80,81], and diagnose respiratory diseases [82,83,84]. See Table 2 for the typical research.
BCG/SCG technology, with its characteristics of non-invasive measurement, can realize the long-term collection of the signals of human vital signs with no interference or low interference, thus very suitable for sleep screening [85,86]. The research conducted by Zhao et al. [82] proposed to use BCG information to calculate heart rate variation index and determine sleep apnea syndrome. In addition, BCG/SCG technology can also produce a positive guiding effect on the patients with depression, for example, Alivar’s [87] team used a smart bed to monitor the sleep quality of a group of children suffering from depression, analyze their sleep quality, timely intervene in their abnormal physical status, and alleviate the risk of disease deterioration.

3.4. Health Management in Elderly Care (Wellness)

With aggravated aging of the population, the issue of health management for the elderly, a special group, has attracted academic attention, and BCG/SCG technology has thus received wide attention [88,89].
In fact, as early as in 2012, Paalasma’s [90] team established a self-service online sleep monitoring platform based on BCG measurement, as shown in Figure 7. Through this platform, users can access their personal sleep data to achieve intelligent health management of sleep quality. Since then, BCG/SCG measurements of sleep data have been further studied. Jaworski’s [91] research team combined the measurement results of the sleep monitoring analyzer with BCG signal to realize the assessment of the body state corresponding to different sleep phases. Park’s [92] team used BCG signal to study the sleep phases, and determined the state of human body in the sleep phase with the heart rate variability characteristics in BCG waveform.
After the effective integration of BCG/SCG signals with other physiological information, a more comprehensive health detection system was presented. Armanfard’s [93] team, for example, established a complete smart home health monitoring system, as shown in Figure 8, with the combination of such signals as ECG, PPG, EMG, BCG and IPPG. This system has achieved accurate collection of human body physiological information, and provided a powerful basic support to the realization of home health management.

4. Discussion

4.1. Increasing Attention on BCG/SCG Technology

The number of papers published in a certain research field reflects, to some extent, the degree of development and the level of research of the subject. The result of this study manifests the increasing number of the literature related to BCG/SCG technology in the field of medical treatment and health. This indicates that since the revival of BCG/SCG technology in 2003, the number of literature in this field included in CNKI and PubMed databases has been on the rise, revealing that BCG/SCG technology has received more and more attention from the health care circles. Deduced according to the law of literature growth, and the Price curve theory, the current research in this field is still in a period of rapid development. Thus, it can be seen that the attention to BCG/SCG technology research has increased year by year, and has gained certain growth in the recent years.

4.2. Future Development Direction of BCG/SCG Technology

Search of literature of BCG/SCG technology in the past 20 years in the field of medicine is synthesized and presented in Figure 6, in which, 37.16% of the researches were related to cardiovascular health, 9.84% were related to application of sleep monitoring; compared with this, it accounted for 28.96% in health management research, in which, 9.29% were related to the research of sleep health management. The directions of future development of BCG and SCG are the application of sleep health management, cardiovascular monitoring and diagnosis, and respiratory testing.

4.3. Application of BCG/SCG in Medical Treatment and Health Care

4.3.1. Specificity and Comprehensiveness

The application of BCG/SCG in medical field is very specific. The researchers applied this technology to the measurement and diagnosis of cardiovascular parameters, as shown in Figure 9a. The measurement of a specific cardiovascular parameter was achieved by analyzing a characteristic value in the signal waveform, and the corresponding diseases (heart failure, sleep apnea, atrial fibrillation, etc.) were studied. Diagnoses using BCG/SCG technology on specific diseases mentioned before have high medical reference value.
The application of BCG/SCG in the field of health management is comprehensive. By macroscopically detecting various physiological parameters of the human body, as shown in Figure 9b, the physiological changes of the human body in a certain period of time can be calculated, and the body functions can be evaluated. Based on these functions, BCG/SCG, for its features of easy design and operation, low measurement threshold, comprehensive data analysis, etc., will have a great potential for development in the field of health care.

4.3.2. Subjectivity and Objectivity

The main part of BCG/SCG in the diagnostic monitoring in medical treatment is the signal itself, which has certain subjectivity. The research of its application in the medical field mainly focuses on the correlation between waveform characteristics and heart activities to draw monitoring conclusions. The signal itself and the analysis results are strongly causal. Therefore, BCG/SCG signal can be used to monitor and feedback human heart functional status in real time, and make decisions on the outcome of changes in the body when signal abnormalities occur. In Kim et al. [44]’s work, features of BCG signal were related to cardiovascular activities by mathematical modelling. In a normal BCG signal (see Figure 1), the time interval between the beginning of the I wave and peak of the J wave may represent the aortic pulse transit time while the ratio of the amplitude of the J-K down-stroke to the amplitude of the J wave may indicate PP (aortic pulse pressure, PP = systolic BP  −  diastolic BP) amplification. Both of them are powerful predictor of cardiovascular risk [94].
The application of BCG/SCG in health management is more objective, requiring long-term monitoring and evaluation of human macroscopic conditions, so as to make timely prediction when the health level declines. Although the basis of data analysis in the field of health management is still signal, with the accumulation of a large number of data, the judgment indicators of human health functions will gradually become regular and identical, because of high comprehensiveness of the data analysis results, even if one of the parameters has an error, the overall judgment will not be affected. Through multi-parameter and comprehensive analysis, balance or imbalance of human health status can be judged, and the occurrence of diseases can be predicted prospectively, thus timely intervention, prevention and control of diseases can be achieved in the early stage.

4.3.3. BCG/SCG’s Field Implementation

Despite the rapid development of the research of BCG/SCG technology, the applications of this technology in real life have been still very few. This is mainly because this technology is still on the rise and has not reached full maturity, with the main manifestations:

Standardization and Reference

The lack of physiological standards of BCG/SCG waveforms and the existence of many variabilities, resulting in the difficulty to guarantee the signal quality so that it is often applied to a specific scene. In Inan Omer’s review [6], he mentioned that there is a naming issue between BCG and SCG and a confused terminology when it comes to the description of specific features (peaks and valleys). Finally he proposed standardization on indication of the site of measurements, specs and orientation of sensors which will facilitate works based on BCG/SCG literature.

Noise and Interference

According to Inan et al. [6], sensor and circuit noise, motion artifacts and floor vibration can potentially corrupt BCG and SCG measurements. The sensor and circuit noise were characterized and reduced for weighing-scale-based BCG systems using an ac-bridge amplifier approach. This approach led to a SNR improvement of 6 dB. Motion artifact detection for standing BCG measurements was accomplished using auxiliary sensors as noise references; then, gating the BCG signal based on the detection of excessive noise. Floor vibration poses challenges for measurements taken on vehicles and planes. Inan Omer [95] gave an successful example of BCG measurement by using an auxiliary sensor for vibration detection and adaptive noise cancelling to cancel floor vibration artifacts.

Effect of Respiration, Posture, Sensor Adherence

In recent research of SCG signals, the authors presented some limitations concerning this relatively new measurement and their proposition of solutions regarding those points [96]. SCG has variability on its morphology decided by respiration, sensor location, subject posture etc. conforming with the conclusion of Inan Omer [6,8] where BCG had similar variability due to their nature of measurements. It needs further research to ameliorate SCG’s utility. Amirtahà Taebi et al. [96] proposed to make a comprehensive SCG signal database which will play an important role in stimulating basic research and medical device development. The combination of ECG, BCG, SCG and other electro-mechanical signals was equally mentioned for more efficient diagnostics.
Modern ballistocardiography and seismocardiography systems may be capable of monitoring slow, longitudinal changes in cardiac function associated with a number of cardiovascular diseases. Timely noninvasive detection of subtle changes in cardiac pathophysiology may one day enable daily drug dosage adjustments, thus reducing costly and morbid rehospitalizations [97]. Recent study has shown BCG and SCG potentiality in sleep quality assessment in terms of detecting obstructive sleep apnea [98].
But BCG/SCG’s the poor universality, which leads to high dependence on other reference signals, and makes it difficult to give full play to its advantages in practical applications. Despite the large number of published studies, as shown in Figure 6, the analysis found that 68% of BCG/SCG technology was implemented in a strictly controlled laboratory environment and still some way from being put in market applications.

5. Conclusions

In order to give consideration to the comprehensibility and effective authority of the inclusion of the literature, this study selected two comprehensive literature databases, CNKI and PubMed, which are authoritative databases in the medical and health field both at home and abroad, to conduct a comprehensive analysis of the representative high-quality literature of BCG/SCG in this field, able to better reflect the research characteristics of the application of this technology. The countries that contributed high quality papers were also those with high level of application. Moreover, in analysis, the subject words in different time periods could truly reflect the changes of the research topics of BCG/SCG technology. However, this study did not cover all the relevant domestic and foreign literatures for analysis, and there might be some deviations in the analysis of the development trend of BCG/SCG technology. In the future study, combination of multiple literature databases for comparative analysis can be tried so as to present a more comprehensive and overall disclosure of the distribution, development trend and research topics of the research of BCG/SCG technology.
In general, BCG/SCG has received extensive attention and in-depth research in the recent years. Academics are actively exploring the principles and physiological values of BCG/SCG, and the ways of applying them to life. In addition to its amazing development speed, BCG/SCG also faces many issues, including the obstacles at the technical improvement level and the difficulties in the technical implementation process. Only by commercializing BCG/SCG technology products, those difficulties in technical implementation process could be overcome.
To sum up, the application of BCG/SCG technology has been presenting broadprospects getting attentions from researchers and in-depth studies were conducted. With the continuous improvement of signal monitoring methods, products based on BCG/SCG will get a more precise approach to clinical diagnosis. Hopefully we shall see their commoditization in the future and a wider range of applications.

Author Contributions

Conceptualization, X.H.; methodology, X.W.; software, J.W.; validation, X.H., K.C.; formal analysis, H.L.; investigation, K.C.; resources, H.C.; data curation, K.Z.; writing—original draft preparation, X.W.; writing—review and editing, X.H.; visualization, K.Z.; supervision, X.Y.; project administration, X.H.; funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Zhejiang Provincial Department of Science and Technology-Zhejiang Industry-University-Research Institute Collaboration Association, grant number [2019]48; and Zhejiang Primary Health Research Center, grant number 2020JC07.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

We would like to thank Pan Wenchao and Gao Zihao for their advice and support during the writing of this review.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Zhu, Y.; Fook, V.F.S.; Jianzhong, E.H.; Maniyeri, J.; Guan, C.; Zhang, H.; Jiliang, E.P.; Biswas, J. Heart rate estimation from FBG sensors using cepstrum analysis and sensor fusion. In Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA, 26–30 August 2004; pp. 5365–5368. [Google Scholar] [CrossRef]
  2. Giovangrandi, L.; Inan, O.T.; Wiard, R.M.; Etemadi, M.; Kovacs, G.T. Ballistocardiography A method worth revisiting. In Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA, 30 August–3 September 2011; pp. 4279–4282. [Google Scholar] [CrossRef] [Green Version]
  3. Sadek, I.; Biswas, J.; Abdulrazak, B. Ballistocardiogram signal processing: A review. Health Inf. Sci. Syst. 2019, 7, 1–23. [Google Scholar] [CrossRef] [PubMed]
  4. Gordon, J.W. On certain molar movements of the human body produced by the circulation. J. Anat. Physiol. 1877, 11, 533–536. [Google Scholar]
  5. Starr, I.; Rawson, A.J.; Schroeder, H.A.; Joseph, N.R. Studies on the estimation of cardiac output in man, and of abnormalities in cardiac function, from the heart’s recoil and the blood’s impacts; theballistocardiogram. Am. J. Physiol. 1939, 127, 1–28. [Google Scholar] [CrossRef]
  6. Inan, O.T.; Migeotte, P.-F.; Park, K.-S.; Etemadi, M.; Tavakolian, K.; Casanella, R.; Zanetti, J.M.; Tank, J.; Funtova, I.; Prisk, G.K.; et al. Ballistocardiography and Seismocardiography: A Review of Recent Advances. IEEE J. Biomed. Health Inform. 2014, 19, 1414–1427. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. He, D.D. A Wearable Heart Monitor at the Ear Using Ballistocardiogram (BCG) and Electrocardiogram (ECG) with a Nanowatt ECG Heartbeat Detection Circuit; MIT: Boston, MA, USA, 2013. [Google Scholar]
  8. Israel, S.A.; Irvine, J.M.; Cheng, A.; Wiederhold, M.D.; Wiederhold, B.K. ECG to identify individuals. Pattern Recognit. 2005, 38, 133–142. [Google Scholar] [CrossRef]
  9. Allsop, T.; Lloyd, G.; Bhamber, R.S.; Hadzievski, L.; Halliday, M.; Webb, D.; Bennion, I. Cardiac-induced localized thoracic motion detected by a fiber optic sensing scheme. J. Biomed. Opt. 2014, 19, 117006. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  10. Chen, S.; Tan, F.; Lyu, W.; Yu, C. A Ballistocardiography monitoring system based on optical fiber interferometer aided with IJK complex segmentation algorithm. Biomed. Opt. Express 2020, 11, 5458–5469. [Google Scholar] [CrossRef]
  11. Van Rooij, B.; Tavakolian, K.; Arzanpour, S.; Blaber, A.; Leguy, C. Non-invasive estimation of cardiovascular parameters using ballistocardiography. In Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy, 25–29 August 2015; pp. 1247–1250. [Google Scholar]
  12. Chung, G.S.; Lee, J.S.; Hwang, S.H.; Lim, Y.K.; Jeong, D.-U.; Park, K.S. Wakefulness estimation only using ballistocardiogram: Nonintrusive method for sleep monitoring. In Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina, 31 August–4 September 2010; pp. 2459–2462. [Google Scholar] [CrossRef]
  13. Cordero, R. Subcutaneous Monitoring of Cardiac Activity for Chronically Implanted Medical Devices; Paris-Saclay University: Gif-sur-Yvette, France, 2020. [Google Scholar]
  14. Vogt, E.; MacQuarrie, D.; Neary, J.P. Using ballistocardiography to measure cardiac performance: A brief review of its history and future significance. Clin. Physiol. Funct. Imaging 2012, 32, 415–420. [Google Scholar] [CrossRef] [PubMed]
  15. Choudhary, T.; Sharma, L.N.; Bhuyan, M.K. Automatic Detection of Aortic Valve Opening Using Seismocardiography in Healthy Individuals. IEEE J. Biomed. Health Inform. 2018, 23, 1032–1040. [Google Scholar] [CrossRef]
  16. Etemadi, M.; Inan, O.T.; Heller, J.A.; Hersek, S.; Klein, L.; Roy, S. A Wearable Patch to Enable Long-Term Monitoring of Environmental, Activity and Hemodynamics Variables. IEEE Trans. Biomed. Circuits Syst. 2016, 10, 280–288. [Google Scholar] [CrossRef] [Green Version]
  17. Becker, M.; Roehl, A.; Siekmann, U.; Koch, A.; De La Fuente, M.; Roissant, R.; Radermacher, K.; Marx, N.; Hein, M. Simplified detection of myocardial ischemia by seismocardiography. Herz 2013, 39, 586–592. [Google Scholar] [CrossRef]
  18. Jain, P.K.; Tiwari, A.K. Heart monitoring systems—A review. Comput. Biol. Med. 2014, 54, 1–13. [Google Scholar] [CrossRef] [PubMed]
  19. Dehkordi, P.; Bauer, E.P.; Tavakolian, K.; Zakeri, V.; Blaber, A.P.; Khosrow-Khavar, F. Identifying Patients with Coronary Artery Disease Using Rest and Exercise Seismocardiography. Front. Physiol. 2019, 10, 1211. [Google Scholar] [CrossRef] [PubMed]
  20. Castiglioni, P.; Meriggi, P.; Rizzo, F.; Vaini, E.; Faini, A.; Parati, G.; Di Rienzo, M. Seismocardiography while sleeping at high altitudeIn. In Proceedings of the 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), San Diego, CA, USA, 28 August–1 September 2012; pp. 3793–3796. [Google Scholar] [CrossRef]
  21. Paukkunen, M.; Parkkila, P.; Kettunen, R.; Sepponen, R. Unified frame of reference improves inter-subject variability of seismocardiograms. Biomed. Eng. Online 2015, 14, 16. [Google Scholar] [CrossRef] [Green Version]
  22. Jain, P.K.; Tiwari, A.K.; Chourasia, V.S. Performance analysis of seismocardiography for heart sound signal recording in noisy scenarios. J. Med. Eng. Technol. 2016, 40, 106–118. [Google Scholar] [CrossRef] [PubMed]
  23. Di Rienzo, M.; Rizzo, G.; Işilay, Z.; Lombardi, P. SeisMote: A Multi-Sensor Wireless Platform for Cardiovascular Monitoring in Laboratory, Daily Life, and Telemedicine. Sensors 2020, 20, 680. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Johnson, E.M.I.; Heller, J.A.; Vicente, F.G.; Sarnari, R.; Gordon, D.; McCarthy, P.M.; Barker, A.J.; Etemadi, M.; Markl, M. Detecting Aortic Valve-Induced Abnormal Flow with Seismocardiography and Cardiac MRI. Ann. Biomed. Eng. 2020, 48, 1779–1792. [Google Scholar] [CrossRef]
  25. Dinh, A.; Bui, F.M.; Nguyen, T. An accelerometer based system to measure myocardial performance index during stress testing. In Proceedings of the 2016 Annual International Conference of the IEEE Engineering in Medicine and Biology, Orlando, FL, USA, 16–20 August 2016; pp. 4877–4880. [Google Scholar] [CrossRef]
  26. Zanetti, J.M.; Tavakolian, K. Seismocardiography: Past, present and future. In Proceedings of the 2013 Annual International Conference of the IEEE Engineering in Medicine and Biology, Osaka, Japan, 3–7 July 2013; pp. 7004–7007. [Google Scholar]
  27. Jähne-Raden, N.; Gütschleg, H.; Marschollek, M. Trodden lanes or new paths: Ballisto- and Seismocardiography till now. Stud. Health Technol. Inform. 2020, 270, 479–483. [Google Scholar] [CrossRef] [PubMed]
  28. Li, X.; Li, Y. J peak extraction from non-standard ballistocardiography data: A preliminary study. In Proceedings of the 2016 Annual International Conference of the IEEE Engineering in Medicine and Biology, Orlando, FL, USA, 16–20 August 2016; pp. 688–691. [Google Scholar] [CrossRef]
  29. Lee, J.; Sohn, J.; Park, J.; Yang, S.; Lee, S.; Kim, H.C. Novel blood pressure and pulse pressure estimation based on pulse transit time and stroke volume approximation. Biomed. Eng. Online 2018, 17, 81. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Lee, K.J.; Roh, J.; Cho, D.; Hyeong, J.; Kim, S. A Chair-Based Unconstrained/Nonintrusive Cuffless Blood Pressure Monitoring System Using a Two-Channel Ballistocardiogram. Sensors 2019, 19, 595. [Google Scholar] [CrossRef] [Green Version]
  31. Laurino, M.; Menicucci, D.; Gemignani, A.; Carbonaro, N.; Tognetti, A. Moving Auto-Correlation Window Approach for Heart Rate Estimation in Ballistocardiography Extracted by Mattress-Integrated Accelerometers. Sensors 2020, 20, 5438. [Google Scholar] [CrossRef] [PubMed]
  32. Campo, D.; Khettab, H.; Yu, R.; Genain, N.; Edouard, P.; Buard, N.; Boutouyrie, P. Measurement of Aortic Pulse Wave Velocity with a Connected Bathroom Scale. Am. J. Hypertens. 2017, 30, 876–883. [Google Scholar] [CrossRef] [Green Version]
  33. Li, F.; Zhao, Y.; Kong, L.; Dong, L.; Liu, M.; Hui, M.; Liu, X. A camera-based ballistocardiogram heart rate measurement method. Rev. Sci. Instrum. 2020, 91, 054105. [Google Scholar] [CrossRef]
  34. Onizuka, K.; Sodini, C.G. Head ballistocardiogram based on wireless multi-location sensors. In Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy, 25–29 August 2015; pp. 1275–1278. [Google Scholar]
  35. Wolf, M.C.; Jähne-Raden, N.; Gütschleg, H.; Kulau, U.; Kallenbach, M.; Wolf, K.-H. First feasibility analysis of Ballistocardiography on a passenger flight. Stud. Health Technol. Inform. 2019, 264, 1648–1649. [Google Scholar] [CrossRef] [PubMed]
  36. Naziyok, T.P.; Zeleke, A.A.; Röhrig, R. Contactless patient monitoring for general wards: A systematic technology review. Stud. Health Technol. Inform. 2016, 228, 707–711. [Google Scholar] [PubMed]
  37. Pino, E.J.; Chavez, J.A.P.; Aqueveque, P. Noninvasive ambulatory measurement system of cardiac activity. In Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy, 25–29 August 2015; pp. 7622–7625. [Google Scholar]
  38. Nedoma, J.; Fajkus, M.; Martinek, R.; Nazeran, H. Vital Sign Monitoring and Cardiac Triggering at 1.5 Tesla: A Practical Solution by an MR-Ballistocardiography Fiber-Optic Sensor. Sensors 2019, 19, 470. [Google Scholar] [CrossRef] [Green Version]
  39. Aydemir, V.B.; Nagesh, S.; Shandhi, M.H.; Fan, J.; Klein, L.; Etemadi, M.; Heller, J.A.; Inan, O.T.; Rehg, J. Classification of Decompensated Heart Failure from Clinical and Home Ballistocardiography. IEEE Trans. Biomed. Eng. 2020, 67, 1303–1313. [Google Scholar] [CrossRef]
  40. Castiglioni, P.; Meriggi, P.; Rizzo, F.; Vaini, E.; Faini, A.; Parati, G.; Merati, G.; Di Rienzo, M. Cardiac sounds from a wearable device for sternal seismocardiography. In Proceedings of the 2011 33th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Boston, MA, USA, 20 August–3 September 2011; pp. 4283–4286. [Google Scholar] [CrossRef]
  41. Cathelain, G.; Rivet, B.; Achard, S.; Bergounioux, J.; Jouen, F. U-Net Neural Network for Heartbeat Detection in Ballistocardiography. In Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Montréal, QC, Canada, 20–24 July 2020; pp. 465–468. [Google Scholar]
  42. Surzhikov, P.V.; Kitsyshin, V.P.; Makiev, R.G. Ballistocardiography as a part of cardiac function research. Voen.-Meditsinskii Zhurnal 2014, 335, 24–30. [Google Scholar]
  43. Lejeune, L.; Prisk, G.K.; Nonclercq, A.; Migeotte, P.-F. MRI-based aortic blood flow model in 3D ballistocardiography. In Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy, 25–29 August 2015; pp. 7171–7174. [Google Scholar]
  44. Kim, C.-S.; Ober, S.L.; McMurtry, M.S.; Finegan, B.A.; Inan, O.T.; Mukkamala, R.; Hahn, J.-O. Ballistocardiogram: Mechanism and Potential for Unobtrusive Cardiovascular Health Monitoring. Sci. Rep. 2016, 6, 31297. [Google Scholar] [CrossRef] [Green Version]
  45. Yousefian, P.; Shin, S.; Mousavi, A.S.; Kim, C.-S.; Finegan, B.; McMurtry, M.S.; Mukkamala, R.; Jang, D.-G.; Kwon, U.; Kim, Y.H.; et al. Physiological Association between Limb Ballistocardiogram and Arterial Blood Pressure Waveforms: A Mathematical Model-Based Analysis. Sci. Rep. 2019, 9, 5146. [Google Scholar] [CrossRef] [Green Version]
  46. Leitão, F.; Moreira, E.; Alves, F.; Lourenço, M.; Azevedo, O.; Gaspar, J.; Rocha, L.A. High-Resolution Seismocardiogram Acquisition and Analysis System. Sensors 2018, 18, 3441. [Google Scholar] [CrossRef] [Green Version]
  47. Su, B.Y.; Enayati, M.; Ho, K.C.; Skubic, M.; Despins, L.; Keller, J.M.; Popescu, M.; Guidoboni, G.; Rantz, M. Monitoring the Relative Blood Pressure Using a Hydraulic Bed Sensor System. IEEE Trans. Biomed. Eng. 2019, 66, 740–748. [Google Scholar] [CrossRef]
  48. Martin, S.L.-O.; Carek, A.M.; Kim, C.-S.; Ashouri, H.; Inan, O.T.; Hahn, J.-O.; Mukkamala, R. Weighing Scale-Based Pulse Transit Time is a Superior Marker of Blood Pressure than Conventional Pulse Arrival Time. Sci. Rep. 2016, 6, 39273. [Google Scholar] [CrossRef] [PubMed]
  49. Javaid, A.Q.; Chang, I.S.; Mihailidis, A. Ballistocardiogram Based Identity Recognition: Towards Zero-Effort Health Monitoring in an Internet-of-Things (IoT) Environment. In Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA, 17–22 July 2018; pp. 3326–3329. [Google Scholar] [CrossRef]
  50. Xie, Q.; Wang, M.; Zhao, Y.; He, Z.; Li, Y.; Wang, G.; Lian, Y. A Personalized Beat-to-Beat Heart Rate Detection System from Ballistocardiogram for Smart Home Applications. IEEE Trans. Biomed. Circuits Syst. 2019, 13, 1593–1602. [Google Scholar] [CrossRef] [PubMed]
  51. Wen, X.; Huang, Y.; Wu, X.; Zhang, B. A Correlation-Based Algorithm for Beat-to-Beat Heart Rate Estimation from Ballistocardiograms. In Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 23–27 July 2019; pp. 6355–6358. [Google Scholar]
  52. Yao, J.; Tridandapani, S.; Wick, C.A.; Bhatti, P.T. Seismocardiography-Based Cardiac Computed Tomography Gating Using Patient-Specific Template Identification and Detection. IEEE J. Transl. Eng. Health Med. 2017, 5, 1–14. [Google Scholar] [CrossRef]
  53. Yao, J.; Tridandapani, S.; Auffermann, W.F.; Wick, C.A.; Bhatti, P.T. An Adaptive Seismocardiography (SCG)-ECG Multimodal Framework for Cardiac Gating Using Artificial Neural Networks. IEEE J. Transl. Eng. Health Med. 2018, 6, 1–11. [Google Scholar] [CrossRef] [PubMed]
  54. Jähne-Raden, N.; Bavendiek, U.; Gütschleg, H.; Kulau, U.; Sigg, S.; Wolf, M.; Zeppernick, T.; Marschollek, M. A structured measurement of highly synchronous real-time Ballistocardiography signal data of heart failure patients. Stud. Health Technol. Inform. 2020, 270, 808–812. [Google Scholar] [CrossRef]
  55. Shin, J.H.; Park, K.S. HRV analysis and blood pressure monitoring on weighing scale using BCG. In Proceedings of the 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), San Diego, CA, USA, 28 August–1 September 2012; pp. 3789–3792. [Google Scholar] [CrossRef]
  56. Bicen, A.O.; Whittingslow, D.C.; Inan, O.T. Template-Based Statistical Modeling and Synthesis for Noise Analysis of Ballistocardiogram Signals: A Cycle-Averaged Approach. IEEE J. Biomed. Health Inform. 2019, 23, 1516–1525. [Google Scholar] [CrossRef] [PubMed]
  57. Wiens, A.; Etemadi, M.; Klein, L.; Roy, S.; Inan, O.T. Wearable ballistocardiography: Preliminary methods for mapping surface vibration measurements to whole body forces. In Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA, 26–30 August 2014; pp. 5172–5175. [Google Scholar] [CrossRef] [Green Version]
  58. Lee, H.; Lee, H.; Whang, M. An Enhanced Method to Estimate Heart Rate from Seismocardiography via Ensemble Averaging of Body Movements at Six Degrees of Freedom. Sensors 2018, 18, 238. [Google Scholar] [CrossRef] [Green Version]
  59. Yang, C.; Tavassolian, N. An Independent Component Analysis Approach to Motion Noise Cancelation of Cardio-Mechanical Signals. IEEE Trans. Biomed. Eng. 2018, 66, 784–793. [Google Scholar] [CrossRef]
  60. Shin, J.H.; Choi, B.H.; Lim, Y.G.; Jeong, D.U.; Park, K.S. Automatic ballistocardiogram (BCG) beat detection using a template matching approach. In Proceedings of the 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Vancouver, BC, Canada, 21–24 August 2008; pp. 1144–1146. [Google Scholar] [CrossRef]
  61. He, S.; Dajani, H.R.; Meade, R.D.; Kenny, G.P.; Bolic, M. Continuous Tracking of Changes in Systolic Blood Pressure using BCG and ECG. In Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 23–27 July 2019; pp. 6826–6829. [Google Scholar]
  62. Kim, C.-S.; Carek, A.M.; Mukkamala, R.; Inan, O.T.; Hahn, J.-O. Ballistocardiogram as Proximal Timing Reference for Pulse Transit Time Measurement: Potential for Cuffless Blood Pressure Monitoring. IEEE Trans. Biomed. Eng. 2015, 62, 2657–2664. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Kim, C.-S.; Carek, A.M.; Inan, O.T.; Mukkamala, R.; Hahn, J.-O. Ballistocardiogram-Based Approach to Cuffless Blood Pressure Monitoring: Proof of Concept and Potential Challenges. IEEE Trans. Biomed. Eng. 2018, 65, 2384–2391. [Google Scholar] [CrossRef] [PubMed]
  64. Zink, M.D.; Brüser, C.; Winnersbach, P.; Napp, A.; Leonhardt, S.; Marx, N.; Schauerte, P.; Mischke, K. Heartbeat Cycle Length Detection by a Ballistocardiographic Sensor in Atrial Fibrillation and Sinus Rhythm. BioMed Res. Int. 2015, 2015, 1–10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Yu, B.; Zhang, B.; Xu, L.; Fang, P.; Hu, J. Automatic Detection of Atrial Fibrillation from Ballistocardiogram (BCG) Using Wavelet Features and Machine Learning. In Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 23–27 July 2019; pp. 4322–4325. [Google Scholar] [CrossRef]
  66. Antink, C.H.; Mai, Y.; Aalto, R.; Brueser, C.; Leonhardt, S.; Oksala, N.; Vehkaoja, A. Ballistocardiography Can Estimate Beat-to-Beat Heart Rate Accurately at Night in Patients After Vascular Intervention. IEEE J. Biomed. Health Inform. 2020, 24, 2230–2237. [Google Scholar] [CrossRef] [PubMed]
  67. Johnson, E.M.I.; Etemadi, M.; Malaisrie, S.C.; McCarthy, P.M.; Markl, M.; Barker, A.J. Seismocardiography and 4D flow MRI reveal impact of aortic valve replacement on chest acceleration and aortic hemodynamics. J. Card. Surg. 2020, 35, 232–235. [Google Scholar] [CrossRef]
  68. Yang, C.; Aranoff, N.D.; Green, P.; Tavassolian, N. Classification of Aortic Stenosis Using Time–Frequency Features from Chest Cardio-Mechanical Signals. IEEE Trans. Biomed. Eng. 2019, 67, 1672–1683. [Google Scholar] [CrossRef] [PubMed]
  69. Inan, O.T.; Pouyan, M.B.; Javaid, A.Q.; Dowling, S.; Etemadi, M.; Dorier, A.; Heller, J.A.; Bicen, A.O.; Roy, S.; De Marco, T.; et al. Novel Wearable Seismocardiography and Machine Learning Algorithms Can Assess Clinical Status of Heart Failure Patients. Circ. Heart Fail. 2018, 11, e004313. [Google Scholar] [CrossRef] [PubMed]
  70. Yao, Y.; Shin, S.; Mousavi, A.; Kim, C.-S.; Xu, L.; Mukkamala, R.; Hahn, J.-O. Unobtrusive Estimation of Cardiovascular Parameters with Limb Ballistocardiography. Sensors 2019, 19, 2922. [Google Scholar] [CrossRef] [Green Version]
  71. Sørensen, K.; Poulsen, M.K.; Karbing, D.S.; Søgaard, P.; Struijk, J.; Schmidt, S.E. A Clinical Method for Estimation of VO2max Using Seismocardiography. Int. J. Sports Med. 2020, 41, 661–668. [Google Scholar] [CrossRef]
  72. Yang, C.; Aranoff, N.D.; Green, P.; Tavassolian, N. A Binary Classification of Cardiovascular Abnormality Using Time-Frequency Features of Cardio-mechanical Signals. In Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA, 17–22 July 2018; pp. 5438–5441. [Google Scholar]
  73. Yamakoshi, K.-I. In the Spotlight: BioInstrumentation. IEEE Rev. Biomed. Eng. 2012, 6, 9–12. [Google Scholar] [CrossRef] [Green Version]
  74. Proll, S.M.; Hofbauer, S.; Kolbitsch, C.; Schubert, R.; Fritscher, K.D. Ejection Wave Segmentation for Contact-Free Heart Rate Estimation from Ballistocardiographic Signals. In Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 23–27 July 2019; pp. 3571–3576. [Google Scholar] [CrossRef]
  75. Zhang, H.; Wang, Z.; Dong, K.; Soon, H.N.; Lin, Z. Towards precise tracking of electric-mechanical cardiac time intervals through joint ECG and BCG sensing and signal processing. In Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju Island, Korea, 15–16 July 2017; pp. 751–754. [Google Scholar] [CrossRef]
  76. Nguyen, H.; Zhang, J.; Nam, Y.-H. Timing detection and seismocardiography waveform extraction. In Proceedings of the 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), San Diego, CA, USA, 28 August–1 September 2012; pp. 3553–3556. [Google Scholar]
  77. Wusk, G.; Gabler, H. Non-Invasive Detection of Respiration and Heart Rate with a Vehicle Seat Sensor. Sensors 2018, 18, 1463. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  78. Skoric, J.; D’Mello, Y.; Aboulezz, E.; Hakim, S.; Clairmonte, N.; Lortie, M.; Plant, D.V. Relationship of the Respiration Waveform to a Chest Worn Inertial Sensor. In Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Montréal, QC, Canada, 20–24 July 2020; pp. 2732–2735. [Google Scholar] [CrossRef]
  79. Martin-Yebra, A.; Landreani, F.; Casellato, C.; Pavan, E.E.; Migeotte, P.-F.; Frigo, C.; Martínez, J.P.; Caiani, E.G. Evaluation of respiratory- and postural-induced changes on the ballistocardiogram signal by time warping averaging. Physiol. Meas. 2017, 38, 1426–1440. [Google Scholar] [CrossRef] [PubMed]
  80. Zakeri, V.; Akhbardeh, A.; Alamdari, N.; Fazel-Rezai, R.; Paukkunen, M.; Tavakolian, K. Analyzing Seismocardiogram Cycles to Identify the Respiratory Phases. IEEE Trans. Biomed. Eng. 2016, 64, 1786–1792. [Google Scholar] [CrossRef]
  81. Despins, L.A.; Guidoboni, G.; Skubic, M.; Sala, L.; Enayati, M.; Popescu, M.; Deroche, C.B. Using Sensor Signals in the Early Detection of Heart Failure: A Case Study. J. Gerontol. Nurs. 2020, 46, 41–46. [Google Scholar] [CrossRef]
  82. Zhao, W.; Ni, H.; Zhou, X.; Song, Y.; Wang, T. Identifying sleep apnea syndrome using heart rate and breathing effort variation analysis based on ballistocardiography. In Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy, 25–29 August 2015; pp. 4536–4539. [Google Scholar] [CrossRef]
  83. Gao, W.; Xu, Y.; Li, S.; Fu, Y.; Zheng, D.; She, Y. Obstructive sleep apnea syndrome detection based on ballistocardiogram via machine learning approach. Math. Biosci. Eng. 2019, 16, 5672–5686. [Google Scholar] [CrossRef]
  84. Sadek, I.; Mohktari, M. Nonintrusive Remote Monitoring of Sleep in Home-Based Situation. J. Med. Syst. 2018, 42, 64. [Google Scholar] [CrossRef]
  85. Wang, Z.; Zhou, X.; Zhao, W.; Liu, F.; Ni, H.; Yu, Z. Assessing the severity of sleep apnea syndrome based on ballistocardiogram. PLoS ONE 2017, 12, e0175351. [Google Scholar] [CrossRef] [Green Version]
  86. Di Rienzo, M.; Vaini, E.; Castiglioni, P.; Lombardi, P.; Meriggi, P.; Rizzo, F. A textile-based wearable system for the prolonged assessment of cardiac mechanics in daily life. In Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA, 26–30 August 2014; pp. 6896–6898. [Google Scholar] [CrossRef]
  87. Alivar, A.; Carlson, C.; Suliman, A.; Warren, S.; Prakash, P.; Thompson, D.E.; Natarajan, B. Smart bed based daytime behavior prediction in Children with autism spectrum disorder—A Pilot Study. Med Eng. Phys. 2020, 83, 15–25. [Google Scholar] [CrossRef]
  88. Kim, Y.; Yoo, S.; Han, C.; Kim, S.; Shin, J.; Choi, J. Evaluation of Unconstrained Monitoring Technology Used in the Smart Bed for u-Health Environment. Telemed. e-Health 2011, 17, 435–441. [Google Scholar] [CrossRef]
  89. Teichmann, D.; Brüser, C.; Eilebrecht, B.; Abbas, A.; Blanik, N.; Leonhardt, S. Non-contact monitoring techniques—Principles and applications. In Proceedings of the 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), San Diego, CA, USA, 28 August–1 September 2012; pp. 1302–1305. [Google Scholar] [CrossRef]
  90. Paalasmaa, J.; Waris, M.; Toivonen, H.; Leppäkorpi, L.; Partinen, M. Unobtrusive online monitoring of sleep at home. In Proceedings of the 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), San Diego, CA, USA, 28 August–1 September 2012; pp. 3784–3788. [Google Scholar] [CrossRef] [Green Version]
  91. Jaworski, D.J.; Roshan, Y.M.; Tae, C.-G.; Park, E.J. Detection of Sleep and Wake States Based on the Combined Use of Actigraphy and Ballistocardiography. In Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 23–27 July 2019; pp. 6701–6704. [Google Scholar] [CrossRef]
  92. Park, K.S.; Hwang, S.H.; Jung, D.W.; Yoon, H.N.; Lee, W.K. Ballistocardiography for nonintrusive sleep structure estimation. In Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA, 26–30 August 2014; pp. 5184–5187. [Google Scholar]
  93. Armanfard, N.; Komeili, M.; Mihailidis, A. Development of a Smart Home-Based Package for Unobtrusive Physiological Monitoring. In Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA, 17–22 July 2018; pp. 4444–4447. [Google Scholar] [CrossRef]
  94. Blacher, J.; Asmar, R.; Djane, S.; London, G.M.; Safar, M.E. Aortic Pulse Wave Velocity as a Marker of Cardiovascular Risk in Hypertensive Patients. Hypertension 1999, 33, 1111–1117. [Google Scholar] [CrossRef] [Green Version]
  95. Inan, O.T.; Etemadi, M.; Widrow, B.; Kovacs, G.T.A. Adaptive Cancellation of Floor Vibrations in Standing Ballistocardiogram Measurements Using a Seismic Sensor as a Noise Reference. IEEE Trans. Biomed. Eng. 2010, 57, 722–727. [Google Scholar] [CrossRef] [PubMed]
  96. Taebi, A.; Solar, B.E.; Bomar, A.J.; Sandler, R.H.; Mansy, H.A. Recent Advances in Seismocardiography. Vibration 2019, 2, 64–86. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  97. Gafoor, S.; Franke, J.; Lam, S.; Reinartz, M.; Bertog, S.; Vaskelyte, L.; Hofmann, I.; Sievert, H. Devices in Heart Failure. Circ. J. 2015, 79, 237–244. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  98. Morra, S.; Hossein, A.; Gorlier, D.; Rabineau, J.; Chaumont, M.; Migeotte, P.-F.; Van De Borne, P. Ballistocardiography and seismocardiography detection of hemodynamic changes during simulated obstructive apnea. Physiol. Meas. 2020, 41, 065007. [Google Scholar] [CrossRef] [PubMed]
Applsci 11 08896 g001 550
Figure 1. ECG/BCG/SCG signals.
Figure 1. ECG/BCG/SCG signals.
Applsci 11 08896 g001
Applsci 11 08896 g002 550
Figure 2. A schematic of SCG measurement device.
Figure 2. A schematic of SCG measurement device.
Applsci 11 08896 g002
Applsci 11 08896 g003 550
Figure 3. Various devices for BCG/SCG measurement.
Figure 3. Various devices for BCG/SCG measurement.
Applsci 11 08896 g003
Applsci 11 08896 g004 550
Figure 4. Hot keywords in the literature.
Figure 4. Hot keywords in the literature.
Applsci 11 08896 g004
Applsci 11 08896 g005 550
Figure 5. Distribution in countries/regions for contribution of the literature on BCG/SCG.
Figure 5. Distribution in countries/regions for contribution of the literature on BCG/SCG.
Applsci 11 08896 g005
Applsci 11 08896 g006 550
Figure 6. Annual statistics of publications and results of classification (top 10).
Figure 6. Annual statistics of publications and results of classification (top 10).
Applsci 11 08896 g006
Applsci 11 08896 g007 550
Figure 7. Online sleep monitoring platform: (a) sensor, (b) interface.
Figure 7. Online sleep monitoring platform: (a) sensor, (b) interface.
Applsci 11 08896 g007
Applsci 11 08896 g008 550
Figure 8. A schematic of smart home construction.
Figure 8. A schematic of smart home construction.
Applsci 11 08896 g008
Applsci 11 08896 g009 550
Figure 9. The types of data under the concern.
Figure 9. The types of data under the concern.
Applsci 11 08896 g009
Table
Table 1. Standards for Classification of Documents.
Table 1. Standards for Classification of Documents.
Classification StandardsDescription of Classification
Fields of the studyResearch of technologiesThe papers contain research on development of BCG/SCG technology, such as exploration of the principle of signal generation, design of signal detection devices, optimization of signal processing methods, establishment of the models for monitoring body status.
Research of applicationsExploration and attempt aimed at practical applications of BCG/SCG.
Field of medical treatment (application)A branch of the category of “application”. The documents intuitively indicated the assessment of BCG/SCG technology in clinic medicine for such aspects as disease diagnosis, patient health status monitoring, and postoperative recovery status.
Field of health management (application) A branch of the category of “application”. The research of evaluation of human body health level by means of BCG/ SCG technology, especially the important role in improving health level and preventing diseases.
Field of respiratory monitoring (medical)A branch of the category of “medical treatment”. BCG/SCG technology is used for monitoring abnormal breathing information, mainly applied in apnea and asthma.
Field of cardiovascular health (medical) A branch of the category of “medical treatment”. This technology is mainly used for evaluation of cardiovascular status, assisting in the detection of cardiovascular diseases, such as hypertension, coronary heart disease, coronary atherosclerosis, and atrial fibrillation, etc.
Field of sleep monitoring (medical)A branch of the category of “medical treatment”. The study explored application of BCG/SCG technology in sleep monitoring, and laid emphasis on monitoring sleep-related diseases.
Field of sleep monitoring (health)A branch of the category of “health”. The study mainly explored application of BCG/SCG technology in sleep monitoring, and laid emphasis on evaluation of the overall level of health during sleeping.
Environment for the studyLaboratory environmentThe experiment was conducted in a laboratory, and the experiment environment was strictly controlled.
Non-laboratory environmentThe experiment was conducted out of a laboratory, and the real site was affected by certain environmental variables, including the experimental tests conducted in a hospital, clinic or at home.
Both of the aboveThe experiments were conducted in both the laboratory and non-laboratory environments.
Not mentionedWhat environment the experiment was conducted in was not mentioned in the article.
Samples in the studyComplete experimental samplesThe experimental samples included male/female/healthy/diseased subjects.
Sufficient experimental samplesThe experimental samples did not cover the subjects of all types, but the candidates included in the samples were enough to provide the basis for experimental analysis. For example, in a study, which used BCG to monitor patients with heart failure, a group of samples, who were all patients with heart failure, were used.
Limited experimental samplesThe experimental samples were not universal, and some types of experimental subjects were ignored.
Not mentionedThe composition of the experimental samples was not mentioned in the article.
Table
Table 2. Typical Research of Application of BCG/SCG in Clinic Medicine in the Tears 2015–2020.
Table 2. Typical Research of Application of BCG/SCG in Clinic Medicine in the Tears 2015–2020.
Research ContentsResearcher [Representative Article] (Year)Conclusion
The relationship between BCG waveform and blood pressureShan He (2019) [61]By comparing BCG and ECG synchronous signals, the relationship between peak R and peak J in the waveforms of these two signals was analyzed, to obtain more accurate estimation of blood pressure, for diagnosis of hypertension.
Evaluation of cardiopulmonary function with SGCK. Sorensen (2020) [71]The relationship between SCG signal and VO2 max was used for in-depth exploration, to analyze the reference point of waveform, and cardiorespiratory fitness (CRF) grades could thus be accurately assessed.
Application of SCG in diagnosis and rehabilitation of coronary artery diseaseE. M. I. Johnson (2020) [67]A case study of a patient after cardiac valve replacement was conducted using SCG and MRI technologies, and it was found that the morphologic changes of SCG signal during cardiac diastolic period accurately reflected the status of cardiovascular functional recovery of the patient.
SCG/BCG for patients with aortic stenosis (AS) in home settingsC. Yang (2020) [68]A system was developed using SCG/BCG technology to monitor the health of patients with aortic stenosis (AS) in home settings to determine the severity of disease progression.
Application of BCG signal in diagnosis of cardiovascular diseasesB. Yu (2019) [65]Detection of atrial fibrillation by means of bed-type BCG signal measurement.
BCG signal for sleep phase analysisA. M. Yebra (2017) [79]The dynamic time warping (DTW) method was used to obtain the morphologic changes of BCG signal waveform corresponding to different postures/breathing phases to judge the phase of sleep.
SCG for sleep phase analysisV. Zakeri (2017) [80]A machine learning algorithm was developed using SCG signal to independently extract the information from different breathing phases in the signal.
BCG signal for diagnosis of respiratory diseasesZhao (2015) [82]The heart rate variation index was calculated, and the data of sleep phase was analyzed by means of BCG signal, these can be used for diagnosis of apnea syndrome and evaluation of the status of stress of the user.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Han, X.; Wu, X.; Wang, J.; Li, H.; Cao, K.; Cao, H.; Zhong, K.; Yang, X. The Latest Progress and Development Trend in the Research of Ballistocardiography (BCG) and Seismocardiogram (SCG) in the Field of Health Care. Appl. Sci. 2021, 11, 8896. https://doi.org/10.3390/app11198896

AMA Style

Han X, Wu X, Wang J, Li H, Cao K, Cao H, Zhong K, Yang X. The Latest Progress and Development Trend in the Research of Ballistocardiography (BCG) and Seismocardiogram (SCG) in the Field of Health Care. Applied Sciences. 2021; 11(19):8896. https://doi.org/10.3390/app11198896

Chicago/Turabian Style

Han, Xiuping, Xiaofei Wu, Jiadong Wang, Hongwen Li, Kaimin Cao, Hui Cao, Kai Zhong, and Xiangdong Yang. 2021. "The Latest Progress and Development Trend in the Research of Ballistocardiography (BCG) and Seismocardiogram (SCG) in the Field of Health Care" Applied Sciences 11, no. 19: 8896. https://doi.org/10.3390/app11198896

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop