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Yu K, Feng L, Chen Y, Wu M, Zhang Y, Zhu P, Chen W, Wu Q, Hao J. Accurate wavelet thresholding method for ECG signals. Comput Biol Med 2024; 169:107835. [PMID: 38096762 DOI: 10.1016/j.compbiomed.2023.107835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/25/2023] [Accepted: 12/05/2023] [Indexed: 02/08/2024]
Abstract
Current wavelet thresholding methods for cardiogram signals captured by flexible wearable sensors face a challenge in achieving both accurate thresholding and real-time signal denoising. This paper proposes a real-time accurate thresholding method based on signal estimation, specifically the normalized ACF, as an alternative to traditional noise estimation without the need for parameter fine-tuning and extensive data training. This method is experimentally validated using a variety of electrocardiogram (ECG) signals from different databases, each containing specific types of noise such as additive white Gaussian (AWG) noise, baseline wander noise, electrode motion noise, and muscle artifact noise. Although this method only slightly outperforms other methods in removing AWG noise in ECG signals, it far outperforms conventional methods in removing other real noise. This is attributed to the method's ability to accurately distinguish not only AWG noise that is significantly different spectrum of the ECG signal, but also real noise with similar spectra. In contrast, the conventional methods are effective only for AWG noise. In additional, this method improves the denoising visualization of the measured ECG signals and can be used to optimize other parameters of other wavelet methods to enhancing the denoised periodic signals, thereby improving diagnostic accuracy.
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Affiliation(s)
- Kaimin Yu
- School of Marine Equipment and Mechanical Engineering, Jimei University, Xiamen, 361021, Fujian, China
| | - Lei Feng
- School of Ocean Information Engineering, Jimei University, Xiamen, 361021, Fujian, China
| | - Yunfei Chen
- School of Ocean Information Engineering, Jimei University, Xiamen, 361021, Fujian, China
| | - Minfeng Wu
- School of Electrical Engineering and Artificial Intelligence, Xiamen University Malaysia, Sepang, 43900, Malaysia
| | - Yuanfang Zhang
- School of Ocean Information Engineering, Jimei University, Xiamen, 361021, Fujian, China
| | - Peibin Zhu
- School of Ocean Information Engineering, Jimei University, Xiamen, 361021, Fujian, China
| | - Wen Chen
- School of Ocean Information Engineering, Jimei University, Xiamen, 361021, Fujian, China.
| | - Qihui Wu
- School of Marine Equipment and Mechanical Engineering, Jimei University, Xiamen, 361021, Fujian, China
| | - Jianzhong Hao
- Institute for Infocomm Research (I(2)R), Agency for Science, Technology and Research (A⋆STAR), 138632, Singapore
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Pulcinelli M, Pinnelli M, Massaroni C, Lo Presti D, Fortino G, Schena E. Wearable Systems for Unveiling Collective Intelligence in Clinical Settings. SENSORS (BASEL, SWITZERLAND) 2023; 23:9777. [PMID: 38139623 PMCID: PMC10747409 DOI: 10.3390/s23249777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 11/29/2023] [Accepted: 12/07/2023] [Indexed: 12/24/2023]
Abstract
Nowadays, there is an ever-growing interest in assessing the collective intelligence (CI) of a team in a wide range of scenarios, thanks to its potential in enhancing teamwork and group performance. Recently, special attention has been devoted on the clinical setting, where breakdowns in teamwork, leadership, and communication can lead to adverse events, compromising patient safety. So far, researchers have mostly relied on surveys to study human behavior and group dynamics; however, this method is ineffective. In contrast, a promising solution to monitor behavioral and individual features that are reflective of CI is represented by wearable technologies. To date, the field of CI assessment still appears unstructured; therefore, the aim of this narrative review is to provide a detailed overview of the main group and individual parameters that can be monitored to evaluate CI in clinical settings, together with the wearables either already used to assess them or that have the potential to be applied in this scenario. The working principles, advantages, and disadvantages of each device are introduced in order to try to bring order in this field and provide a guide for future CI investigations in medical contexts.
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Affiliation(s)
- Martina Pulcinelli
- Research Unit of Measurements and Biomedical Instrumentation, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy; (M.P.); (M.P.); (C.M.); (E.S.)
| | - Mariangela Pinnelli
- Research Unit of Measurements and Biomedical Instrumentation, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy; (M.P.); (M.P.); (C.M.); (E.S.)
| | - Carlo Massaroni
- Research Unit of Measurements and Biomedical Instrumentation, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy; (M.P.); (M.P.); (C.M.); (E.S.)
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
| | - Daniela Lo Presti
- Research Unit of Measurements and Biomedical Instrumentation, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy; (M.P.); (M.P.); (C.M.); (E.S.)
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
| | - Giancarlo Fortino
- DIMES, University of Calabria, Via P. Bucci 41C, 87036 Rende, Italy;
| | - Emiliano Schena
- Research Unit of Measurements and Biomedical Instrumentation, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy; (M.P.); (M.P.); (C.M.); (E.S.)
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
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Santucci F, Nobili M, Presti DL, Massaroni C, Setola R, Schena E, Oliva G. Waveform Similarity Analysis Using Graph Mining for the Optimization of Sensor Positioning in Wearable Seismocardiography. IEEE Trans Biomed Eng 2023; 70:2788-2798. [PMID: 37027279 DOI: 10.1109/tbme.2023.3264940] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
Abstract
OBJECTIVE A major concern with wearable devices aiming to measure the seismocardiogram (SCG) signal is the variability of SCG waveform with the sensor position and a lack of a standard measurement procedure. We propose a method to optimize sensor positioning based on the similarity among waveforms collected through repeated measurements. METHOD we design a graph-theoretical model to evaluate the similarity of SCG signals and apply the proposed methodology to signals collected by sensors placed in different positions on the chest. A similarity score returns the optimal measurement position based on the repeatability of SCG waveforms. We tested the methodology on signals collected by using two wearable patches based on optical technology placed in two positions: mitral and aortic valve auscultation site (inter-position analysis). 11 healthy subjects were enrolled in this study. Moreover, we evaluated the influence of the subject's posture on waveform similarity with a view on ambulatory use (inter-posture analysis). RESULTS the highest similarity among SCG waveforms is obtained with the sensor on the mitral valve and the subject lying down. CONCLUSIONS our approach aims to be a step forward in the optimization of sensor positioning in the field of wearable seismocardiography. We demonstrate that the proposed algorithm is an effective method to estimate similarity among waveforms and outperforms the state-of-the-art in comparing SCG measurement sites. SIGNIFICANCE results obtained from this study can be exploited to design more efficient protocols for SCG recording in both research studies and future clinical examinations.
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Siecinski S, Irshad MT, Abid Hasan M, Tkacz EJ, Kostka PS, Grzegorzek M. Symmetric Projection Attractor Reconstruction Analysis as a Method to Assess Seismocardiogram Quality in a Healthy Population. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083468 DOI: 10.1109/embc40787.2023.10340142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Signal quality significantly affects the processing, analysis, and interpretation of biomedical signals. There are many procedures for assessing signal quality that use averaged numerical values, thresholding, analysis in the time or frequency domain, or nonlinear approaches. An interesting approach to the assessment of signal quality is using symmetric projection attractor reconstruction (SPAR) analysis, which transforms an entire signal into a two-dimensional plot that reflects the waveform morphology. In this study, we present an application of SPAR to evaluate the quality of seismocardiograms (SCG signals) from the CEBS database, a publicly available seismocardiogram signal database. Visual inspection of symmetric projection attractors suggests that high-quality (clean) seismocardiogram projections resemble six-pointed asterisks (*), and any deviation from this shape suggests the influence of noise and artifacts.Clinical relevance- SPAR analysis enables quick identification of noise and artifacts that can affect the reliability of the diagnosis of cardiovascular diseases based on SCG signals.
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Siecinski S, Tkacz EJ, Grzegorzek M. Publicly available signal databases containing seismocardiographic signals - the state in early 2023. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083212 DOI: 10.1109/embc40787.2023.10340318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The development of information and communication technologies (ICT) changed many aspects of our lives, including cardiovascular research. This area of research is affected by the availability of open databases that can help conduct basic and applied research. In this study, we summarize the current state of knowledge in publicly available signal databases with seismocardiographic (SCG) signals in January 2023. Based on Google search results for the expression "seismocardiography dataset", we have found and described five databases with seismocardiograms, including three databases that contain SCG signals from healthy subjects, one database with data from porcine subjects, and one signal database with data obtained from human patients with valvular heart disease (VHD). All contain additional signals for reference points in the cardiac cycle. The most significant limitations of the described data sets are gender bias toward male subjects, the imbalance between healthy subjects, and subjects with two cardiovascular diseases (VHD and hemorrhage).
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Song Z, Zhou S, Qin Y, Xia X, Sun Y, Han G, Shu T, Hu L, Zhang Q. Flexible and Wearable Biosensors for Monitoring Health Conditions. BIOSENSORS 2023; 13:630. [PMID: 37366995 DOI: 10.3390/bios13060630] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 05/22/2023] [Accepted: 06/01/2023] [Indexed: 06/28/2023]
Abstract
Flexible and wearable biosensors have received tremendous attention over the past decade owing to their great potential applications in the field of health and medicine. Wearable biosensors serve as an ideal platform for real-time and continuous health monitoring, which exhibit unique properties such as self-powered, lightweight, low cost, high flexibility, detection convenience, and great conformability. This review introduces the recent research progress in wearable biosensors. First of all, the biological fluids often detected by wearable biosensors are proposed. Then, the existing micro-nanofabrication technologies and basic characteristics of wearable biosensors are summarized. Then, their application manners and information processing are also highlighted in the paper. Massive cutting-edge research examples are introduced such as wearable physiological pressure sensors, wearable sweat sensors, and wearable self-powered biosensors. As a significant content, the detection mechanism of these sensors was detailed with examples to help readers understand this area. Finally, the current challenges and future perspectives are proposed to push this research area forward and expand practical applications in the future.
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Affiliation(s)
- Zhimin Song
- Department of Anesthesiology, The Second Hospital of Jilin University, Changchun 130041, China
| | - Shu Zhou
- Department of Anesthesiology, Jilin Cancer Hospital, Changchun 130021, China
| | - Yanxia Qin
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China
| | - Xiangjiao Xia
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China
| | - Yanping Sun
- School of Biomedical Engineering, Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen Key Laboratory for Nano-Biosensing Technology, International Health Science Innovation Center, Research Center for Biosensor and Nanotheranostic, Health Science Center, Shenzhen University, Shenzhen 518060, China
| | - Guanghong Han
- Department of Oral Geriatrics, Hospital of Stomatology, Jilin University, Changchun 130021, China
| | - Tong Shu
- School of Biomedical Engineering, Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen Key Laboratory for Nano-Biosensing Technology, International Health Science Innovation Center, Research Center for Biosensor and Nanotheranostic, Health Science Center, Shenzhen University, Shenzhen 518060, China
| | - Liang Hu
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China
| | - Qiang Zhang
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China
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Sieciński S, Tkacz EJ, Kostka PS. Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms of Healthy Volunteers and Patients with Valvular Heart Diseases. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23042152. [PMID: 36850746 PMCID: PMC9960701 DOI: 10.3390/s23042152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 02/03/2023] [Accepted: 02/13/2023] [Indexed: 05/12/2023]
Abstract
Heart rate variability (HRV) is the physiological variation in the intervals between consecutive heartbeats that reflects the activity of the autonomic nervous system. This parameter is traditionally evaluated based on electrocardiograms (ECG signals). Seismocardiography (SCG) and/or gyrocardiography (GCG) are used to monitor cardiac mechanical activity; therefore, they may be used in HRV analysis and the evaluation of valvular heart diseases (VHDs) simultaneously. The purpose of this study was to compare the time domain, frequency domain and nonlinear HRV indices obtained from electrocardiograms, seismocardiograms (SCG signals) and gyrocardiograms (GCG signals) in healthy volunteers and patients with valvular heart diseases. An analysis of the time domain, frequency domain and nonlinear heart rate variability was conducted on electrocardiograms and gyrocardiograms registered from 29 healthy male volunteers and 30 patients with valvular heart diseases admitted to the Columbia University Medical Center (New York City, NY, USA). The results of the HRV analysis show a strong linear correlation with the HRV indices calculated from the ECG, SCG and GCG signals and prove the feasibility and reliability of HRV analysis despite the influence of VHDs on the SCG and GCG waveforms.
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Milena Č, Romano C, De Tommasi F, Carassiti M, Formica D, Schena E, Massaroni C. Linear and Non-Linear Heart Rate Variability Indexes from Heart-Induced Mechanical Signals Recorded with a Skin-Interfaced IMU. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23031615. [PMID: 36772656 PMCID: PMC9920051 DOI: 10.3390/s23031615] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 01/02/2023] [Accepted: 01/28/2023] [Indexed: 05/26/2023]
Abstract
Heart rate variability (HRV) indexes are becoming useful in various applications, from better diagnosis and prevention of diseases to predicting stress levels. Typically, HRV indexes are retrieved from the heart's electrical activity collected with an electrocardiographic signal (ECG). Heart-induced mechanical signals recorded from the body's surface can be utilized to record the mechanical activity of the heart and, in turn, extract HRV indexes from interbeat intervals (IBIs). Among others, accelerometers and gyroscopes can be used to register IBIs from precordial accelerations and chest wall angular velocities. However, unlike electrical signals, the morphology of mechanical ones is strongly affected by body posture. In this paper, we investigated the feasibility of estimating the most common linear and non-linear HRV indexes from accelerometer and gyroscope data collected with a wearable skin-interfaced Inertial Measurement Unit (IMU) positioned at the xiphoid level. Data were collected from 21 healthy volunteers assuming two common postures (i.e., seated and lying). Results show that using the gyroscope signal in the lying posture allows accurate results in estimating IBIs, thus allowing extracting of linear and non-linear HRV parameters that are not statistically significantly different from those extracted from reference ECG.
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Affiliation(s)
- Čukić Milena
- Empa Materials Science and Technology, Biomimetic Membranes and Textiles, 9014 St. Gallen, Switzerland
- 3EGA B.V., 1062 KS Amsterdam, The Netherlands
| | - Chiara Romano
- Unit of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 00128 Rome, Italy
| | - Francesca De Tommasi
- Unit of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 00128 Rome, Italy
- Unit of Anesthesia, Intensive Care and Pain Management, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 00128 Rome, Italy
| | - Massimiliano Carassiti
- Unit of Anesthesia, Intensive Care and Pain Management, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 00128 Rome, Italy
| | - Domenico Formica
- School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Emiliano Schena
- Unit of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 00128 Rome, Italy
| | - Carlo Massaroni
- Unit of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 00128 Rome, Italy
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Cosoli G, Antognoli L, Scalise L. Wearable Electrocardiography for Physical Activity Monitoring: Definition of Validation Protocol and Automatic Classification. BIOSENSORS 2023; 13:154. [PMID: 36831919 PMCID: PMC9953541 DOI: 10.3390/bios13020154] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 01/16/2023] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
Wearable devices are rapidly spreading thanks to multiple advantages. Their use is expanding in several fields, from medicine to personal assessment and sport applications. At present, more and more wearable devices acquire an electrocardiographic (ECG) signal (in correspondence to the wrist), providing potentially useful information from a diagnostic point of view, particularly in sport medicine and in rehabilitation fields. They are remarkably relevant, being perceived as a common watch and, hence, considered neither intrusive nor a cause of the so-called "white coat effect". Their validation and metrological characterization are fundamental; hence, this work aims at defining a validation protocol tested on a commercial smartwatch (Samsung Galaxy Watch3, Samsung Electronics Italia S.p.A., Milan, Italy) with respect to a gold standard device (Zephyr BioHarness 3.0, Zephyr Technology Corporation, Annapolis, MD, USA, accuracy of ±1 bpm), reporting results on 30 subjects. The metrological performance is provided, supporting final users to properly interpret the results. Moreover, machine learning and deep learning models are used to discriminate between resting and activity-related ECG signals. The results confirm the possibility of using heart rate data from wearable sensors for activity identification (best results obtained by Random Forest, with accuracy of 0.81, recall of 0.80, and precision of 0.81, even using ECG signals of limited duration, i.e., 30 s). Moreover, the effectiveness of the proposed validation protocol to evaluate measurement accuracy and precision in a wide measurement range is verified. A bias of -1 bpm and an experimental standard deviation of 11 bpm (corresponding to an experimental standard deviation of the mean of ≈0 bpm) were found for the Samsung Galaxy Watch3, indicating a good performance from a metrological point of view.
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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:s22239565. [PMID: 36502267 PMCID: PMC9737480 DOI: 10.3390/s22239565] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [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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