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Balali P, Rabineau J, Hossein A, Tordeur C, Debeir O, van de Borne P. Investigating Cardiorespiratory Interaction Using Ballistocardiography and Seismocardiography-A Narrative Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:9565. [PMID: 36502267 PMCID: PMC9737480 DOI: 10.3390/s22239565] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 11/11/2022] [Accepted: 11/28/2022] [Indexed: 05/29/2023]
Abstract
Ballistocardiography (BCG) and seismocardiography (SCG) are non-invasive techniques used to record the micromovements induced by cardiovascular activity at the body's center of mass and on the chest, respectively. Since their inception, their potential for evaluating cardiovascular health has been studied. However, both BCG and SCG are impacted by respiration, leading to a periodic modulation of these signals. As a result, data processing algorithms have been developed to exclude the respiratory signals, or recording protocols have been designed to limit the respiratory bias. Reviewing the present status of the literature reveals an increasing interest in applying these techniques to extract respiratory information, as well as cardiac information. The possibility of simultaneous monitoring of respiratory and cardiovascular signals via BCG or SCG enables the monitoring of vital signs during activities that require considerable mental concentration, in extreme environments, or during sleep, where data acquisition must occur without introducing recording bias due to irritating monitoring equipment. This work aims to provide a theoretical and practical overview of cardiopulmonary interaction based on BCG and SCG signals. It covers the recent improvements in extracting respiratory signals, computing markers of the cardiorespiratory interaction with practical applications, and investigating sleep breathing disorders, as well as a comparison of different sensors used for these applications. According to the results of this review, recent studies have mainly concentrated on a few domains, especially sleep studies and heart rate variability computation. Even in those instances, the study population is not always large or diversified. Furthermore, BCG and SCG are prone to movement artifacts and are relatively subject dependent. However, the growing tendency toward artificial intelligence may help achieve a more accurate and efficient diagnosis. These encouraging results bring hope that, in the near future, such compact, lightweight BCG and SCG devices will offer a good proxy for the gold standard methods for assessing cardiorespiratory function, with the added benefit of being able to perform measurements in real-world situations, outside of the clinic, and thus decrease costs and time.
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Affiliation(s)
- Paniz Balali
- Laboratoray of Physics and Physiology, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Laboratory of Image Synthesis and Analysis, Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Jeremy Rabineau
- Laboratoray of Physics and Physiology, Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Amin Hossein
- Laboratoray of Physics and Physiology, Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Cyril Tordeur
- Laboratoray of Physics and Physiology, Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Olivier Debeir
- Laboratory of Image Synthesis and Analysis, Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Philippe van de Borne
- Laboratoray of Physics and Physiology, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Department of Cardiology, Erasme Hospital, Université Libre de Bruxelles, 1050 Brussels, Belgium
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Sieciński S, Kostka PS, Tkacz EJ. Gyrocardiography: A Review of the Definition, History, Waveform Description, and Applications. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6675. [PMID: 33266401 PMCID: PMC7700364 DOI: 10.3390/s20226675] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 11/13/2020] [Accepted: 11/20/2020] [Indexed: 02/07/2023]
Abstract
Gyrocardiography (GCG) is a non-invasive technique of analyzing cardiac vibrations by a MEMS (microelectromechanical system) gyroscope placed on a chest wall. Although its history is short in comparison with seismocardiography (SCG) and electrocardiography (ECG), GCG becomes a technique which may provide additional insight into the mechanical aspects of the cardiac cycle. In this review, we describe the summary of the history, definition, measurements, waveform description and applications of gyrocardiography. The review was conducted on about 55 works analyzed between November 2016 and September 2020. The aim of this literature review was to summarize the current state of knowledge in gyrocardiography, especially the definition, waveform description, the physiological and physical sources of the signal and its applications. Based on the analyzed works, we present the definition of GCG as a technique for registration and analysis of rotational component of local cardiac vibrations, waveform annotation, several applications of the gyrocardiography, including, heart rate estimation, heart rate variability analysis, hemodynamics analysis, and classification of various cardiac diseases.
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Affiliation(s)
- Szymon Sieciński
- Department of Biosensors and Processing of Biomedical Signals, Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland; (P.S.K.); (E.J.T.)
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Lahdenoja O, Hurnanen T, Kaisti M, Koskinen J, Tuominen J, Vähä-Heikkilä M, Parikka L, Wiberg M, Koivisto T, Pänkäälä M. Cardiac monitoring of dogs via smartphone mechanocardiography: a feasibility study. Biomed Eng Online 2019; 18:47. [PMID: 31014339 PMCID: PMC6480821 DOI: 10.1186/s12938-019-0667-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 04/10/2019] [Indexed: 11/11/2022] Open
Abstract
Background In the context of monitoring dogs, usually, accelerometers have been used to measure the dog’s movement activity. Here, we study another application of the accelerometers (and gyroscopes)—seismocardiography (SCG) and gyrocardiography (GCG)—to monitor the dog’s heart. Together, 3-axis SCG and 3-axis GCG constitute of 6-axis mechanocardiography (MCG), which is inbuilt to most modern smartphones. Thus, the objective of this study is to assess the feasibility of using a smartphone-only solution to studying dog’s heart. Methods A clinical trial (CT) was conducted at the University Small Animal Hospital, University of Helsinki, Finland. 14 dogs (3 breeds) including 18 measurements (about one half of all) where the dog’s status was such that it was still and not panting were further selected for the heart rate (HR) analysis (each signal with a duration of 1 min). The measurement device in the CT was a custom Holter monitor including synchronized 6-axis MCG and ECG. In addition, 16 dogs (9 breeds, one mixed-breed) were measured at home settings by the dog owners themselves using Sony Xperia Android smartphone sensor to further validate the applicability of the method. Results The developed algorithm was able to select 10 good-quality signals from the 18 CT measurements, and for 7 of these, the automated algorithm was able to detect HR with deviation below or equal to 5 bpm (compared to ECG). Further visual analysis verified that, for approximately half of the dogs, the signal quality at home environment was sufficient for HR extraction at least in some signal locations, while the motion artifacts due to dog’s movements are the main challenges of the method. Conclusion With improved data analysis techniques for managing noisy measurements, the proposed approach could be useful in home use. The advantage of the method is that it can operate as a stand-alone application without requiring any extra equipment (such as smart collar or ECG patch).
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Affiliation(s)
- Olli Lahdenoja
- Department of Future Technologies, Faculty of Science and Engineering, University of Turku, Vesilinnantie 5, 20014, Turku, Finland.
| | - Tero Hurnanen
- Department of Future Technologies, Faculty of Science and Engineering, University of Turku, Vesilinnantie 5, 20014, Turku, Finland
| | - Matti Kaisti
- Department of Future Technologies, Faculty of Science and Engineering, University of Turku, Vesilinnantie 5, 20014, Turku, Finland
| | - Juho Koskinen
- Department of Future Technologies, Faculty of Science and Engineering, University of Turku, Vesilinnantie 5, 20014, Turku, Finland
| | - Jarno Tuominen
- Department of Future Technologies, Faculty of Science and Engineering, University of Turku, Vesilinnantie 5, 20014, Turku, Finland
| | - Matti Vähä-Heikkilä
- Department of Future Technologies, Faculty of Science and Engineering, University of Turku, Vesilinnantie 5, 20014, Turku, Finland
| | - Laura Parikka
- Department of Equine and Small Animal Medicine, Faculty of Veterinary Medicine, University of Helsinki, PL 57 Koetilantie 2, 00014, Helsinki, Finland
| | - Maria Wiberg
- Department of Equine and Small Animal Medicine, Faculty of Veterinary Medicine, University of Helsinki, PL 57 Koetilantie 2, 00014, Helsinki, Finland
| | - Tero Koivisto
- Department of Future Technologies, Faculty of Science and Engineering, University of Turku, Vesilinnantie 5, 20014, Turku, Finland
| | - Mikko Pänkäälä
- Department of Future Technologies, Faculty of Science and Engineering, University of Turku, Vesilinnantie 5, 20014, Turku, Finland
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