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Geng D, Yin Y, Fu Z, Pang G, Xu G, Geng Y, Wang A. Heart rate detection method based on Ballistocardiogram signal of wearable device:Algorithm development and validation. Heliyon 2024; 10:e27369. [PMID: 38486774 PMCID: PMC10937685 DOI: 10.1016/j.heliyon.2024.e27369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 02/27/2024] [Accepted: 02/28/2024] [Indexed: 03/17/2024] Open
Abstract
Background Heart rate, as the four vital signs of human body, is a basic indicator to measure a person's health status. Traditional electrocardiography (ECG) measurement, which is routinely monitored, requires subjects to wear lead electrodes frequently, which undoubtedly places great restrictions on participants' activities during the normal test. At present, the boom of wearable devices has created hope for non-invasive, simple operation and low-cost daily heart rate monitoring, among them, Ballistocardiogram signal (BCG) is an effective heart rate measurement method, but in the actual acquisition process, the robustness of non-invasive vital sign collection is limited. Therefore, it is necessary to develop a method to improve the robustness of heart rate monitoring. Objective Therefore, in view of the problem that the accuracy of untethered monitoring heart rate is not high, we propose a method aimed at detecting the heartbeat cycle based on BCG to accurately obtain the beat-to-beat heart rate in the sleep state. Methods In this study, we implement an innovative J-wave detection algorithm based on BCG signals. By collecting BCG signals recorded by 28 healthy subjects in different sleeping positions, after preprocessing, the data feature set is formed according to the clustering of morphological features in the heartbeat interval. Finally, a J-wave recognition model is constructed based on bi-directional long short-term memory (BiLSTM), and then the number of J-waves in the input sequence is counted to realize real-time detection of heartbeat. The performance of the proposed heartbeat detection scheme is cross-verified, and the proposed method is compared with the previous wearable device algorithm. Results The accuracy of J wave recognition in BCG signal is 99.67%, and the deviation rate of heart rate detection is only 0.27%, which has higher accuracy than previous wearable device algorithms. To assess consistency between method results and heart rates obtained by the ECG, seven subjects are compared using Bland-Altman plots, which show no significant difference between BCG and ECG results for heartbeat cycles. Conclusions Compared with other studies, the proposed method is more accurate in J-wave recognition, which improves the accuracy and generalization ability of BCG-based continuous heartbeat cycle extraction, and provides preliminary support for wearable-based untethered daily monitoring.
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Affiliation(s)
- Duyan Geng
- Hebei University of Technology, State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Tianjin, 300130, PR China
- Hebei University of Technology, School of Electrical Engineering, Tianjin, 300130, PR China
| | - Yue Yin
- Hebei University of Technology, State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Tianjin, 300130, PR China
- Hebei University of Technology, School of Electrical Engineering, Tianjin, 300130, PR China
| | - Zhigang Fu
- Physical Examination Center of the Fourth Joint Logistics Support Unit of the 983rd Hospital of the Tianjin Chinese People's Liberation Army, Tianjin, 300142, PR China
| | - Geng Pang
- Hebei University of Technology, State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Tianjin, 300130, PR China
- Hebei University of Technology, School of Electrical Engineering, Tianjin, 300130, PR China
| | - Guizhi Xu
- Hebei University of Technology, State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Tianjin, 300130, PR China
- Hebei University of Technology, School of Electrical Engineering, Tianjin, 300130, PR China
| | - Yan Geng
- Hebei Institute for Drug and Medical Device Control, Shijiazhuang, 050200, PR China
| | - Alan Wang
- Centre for Brain Research, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
- Centre for Medical Imaging, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
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Feng J, Huang W, Jiang J, Wang Y, Zhang X, Li Q, Jiao X. Non-invasive monitoring of cardiac function through Ballistocardiogram: an algorithm integrating short-time Fourier transform and ensemble empirical mode decomposition. Front Physiol 2023; 14:1201722. [PMID: 37664434 PMCID: PMC10472450 DOI: 10.3389/fphys.2023.1201722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 07/24/2023] [Indexed: 09/05/2023] Open
Abstract
The Ballistocardiogram (BCG) is a vibration signal that is generated by the displacement of the entire body due to the injection of blood during each heartbeat. It has been extensively utilized to monitor heart rate. The morphological features of the BCG signal serve as effective indicators for the identification of atrial fibrillation and heart failure, holding great significance for BCG signal analysis. The IJK-complex identification allows for the estimation of inter-beat intervals (IBI) and enables a more detailed analysis of BCG amplitude and interval waves. This study presents a novel algorithm for identifying the IJK-complex in BCG signals, which is an improvement over most existing algorithms that only perform IBI estimation. The proposed algorithm employs a short-time Fourier transform and summation across frequencies to initially estimate the occurrence of the J wave using peak finding, followed by Ensemble Empirical Mode Decomposition and a regional search to precisely identify the J wave. The algorithm's ability to detect the morphological features of BCG signals and estimate heart rates was validated through experiments conducted on 10 healthy subjects and 2 patients with coronary heart disease. In comparison to commonly used methods, the presented scheme ensures accurate heart rate estimation and exhibits superior capability in detecting BCG morphological features. This advancement holds significant value for future applications involving BCG signals.
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Affiliation(s)
- Jingda Feng
- Department of Aerospace Science and Technology, Space Engineering University, Beijing, China
- China Astronaut Research and Training Center, Beijing, China
| | - WeiFen Huang
- China Astronaut Research and Training Center, Beijing, China
| | - Jin Jiang
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China
| | - Yanlei Wang
- China Astronaut Research and Training Center, Beijing, China
| | - Xiang Zhang
- China Astronaut Research and Training Center, Beijing, China
| | - Qijie Li
- China Astronaut Research and Training Center, Beijing, China
| | - Xuejun Jiao
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China
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3
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Czapanskiy MF, Ponganis PJ, Fahlbusch JA, Schmitt TL, Goldbogen JA. An accelerometer-derived ballistocardiogram method for detecting heartrates in free-ranging marine mammals. J Exp Biol 2022; 225:275276. [PMID: 35502794 PMCID: PMC9167577 DOI: 10.1242/jeb.243872] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 04/28/2022] [Indexed: 11/24/2022]
Abstract
Physio-logging methods, which use animal-borne devices to record physiological variables, are entering a new era driven by advances in sensor development. However, existing datasets collected with traditional bio-loggers, such as accelerometers, still contain untapped eco-physiological information. Here, we present a computational method for extracting heart rate from high-resolution accelerometer data using a ballistocardiogram. We validated our method with simultaneous accelerometer–electrocardiogram tag deployments in a controlled setting on a killer whale (Orcinus orca) and demonstrate the predictions correspond with previously observed cardiovascular patterns in a blue whale (Balaenoptera musculus), including the magnitude of apneic bradycardia and increase in heart rate prior to and during ascent. Our ballistocardiogram method may be applied to mine heart rates from previously collected accelerometery data and expand our understanding of comparative cardiovascular physiology. Highlighted Article: Validation of a computational method for extracting heart rate in free-ranging cetaceans from high-resolution accelerometer data using a ballistocardiogram.
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Affiliation(s)
- Max F Czapanskiy
- Hopkins Marine Station, Department of Biology, Stanford University, USA
| | - Paul J Ponganis
- Scripps Institution of Oceanography, University of California San Diego, USA
| | - James A Fahlbusch
- Hopkins Marine Station, Department of Biology, Stanford University, USA
| | - T L Schmitt
- Animal Health Department, SeaWorld of California, USA
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Chang IS, Mak S, Armanfard N, Boger J, Grace SL, Arcelus A, Chessex C, Mihailidis A. Quantification of Resting-State Ballistocardiogram Difference Between Clinical and Non-Clinical Populations for Ambient Monitoring of Heart Failure. IEEE J Transl Eng Health Med 2020; 8:2700811. [PMID: 33094034 PMCID: PMC7571868 DOI: 10.1109/jtehm.2020.3029690] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 09/14/2020] [Accepted: 10/05/2020] [Indexed: 11/12/2022]
Abstract
A ballistocardiogram (BCG) is a versatile bio-signal that enables ambient remote monitoring of heart failure (HF) patients in a home setting, achieved through embedded sensors in the surrounding environment. Numerous methods of analysis are available for extracting physiological information using the BCG; however, most have been developed based on non-clinical subjects. While the difference between clinical and non-clinical populations are expected, quantification of the difference may serve as a useful tool. In this work, the differences in resting-state BCGs of the two cohorts in a sitting posture were quantified. An instrumented chair was used to collect the BCG from 29 healthy adults and 26 NYHA HF class I and II patients while seated without any stress test for five minutes. Five 20-second epochs per subject were used to calculate the waveform fluctuation metric at rest (WFMR). The WFMR was obtained in two steps. The ensemble average of the segmented BCG heartbeats within an epoch were calculated first. Mean square errors (MSE) between different ensemble average pairs were then retrieved. The MSEs were averaged to produce the WFMR. The comparison showed that the clinical cohort had higher fluctuation than the non-clinical population and had at least 82.2% separation, suggesting that greater errors may result when existing algorithms were used. The WFMR acts as a bridge that may enable important features, including the addition of error margins in parameter estimation and ways to devise a calibration strategy when resting-state BCG is unstable.
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Affiliation(s)
- Isaac Sungjae Chang
- Institute of Biomaterials and Biomedical Engineering, University of TorontoONM5S 3G9Canada
| | - Susanna Mak
- Division of CardiologyDepartment of MedicineMount Sinai HospitalTorontoONM5G 1X5Canada
| | - Narges Armanfard
- Department of Electrical and Computer EngineeringMcGill UniversityMontrealQCH3A 0G4Canada
| | - Jennifer Boger
- Department of Systems Design EngineeringUniversity of WaterlooWaterlooONN2L 3G1Canada.,Research Institute for AgingWaterlooONN2J 0E2Canada
| | - Sherry L Grace
- Faculty of HealthYork UniversityTorontoONM3J IP3Canada.,Toronto Rehabilitation Institute, University Health NetworkTorontoONM5T 2S8Canada
| | - Amaya Arcelus
- Toronto Rehabilitation Institute, University Health NetworkTorontoONM5T 2S8Canada
| | - Caroline Chessex
- Toronto Rehabilitation Institute, University Health NetworkTorontoONM5T 2S8Canada
| | - Alex Mihailidis
- Toronto Rehabilitation Institute, University Health NetworkTorontoONM5T 2S8Canada
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Abstract
Across the world, healthcare costs are projected to continue to increase, and the pressure on the healthcare system is only going to grow in intensity as the rate of growth of elderly population increases in the coming decades. As an example, when people age one possible condition that they may experience is sleep-disordered breathing (SDB). SDB, better known as the obstructive sleep apnea (OSA) syndrome, and associated cardiovascular complications are among the most common clinical disorders. The gold-standard approach to accurately diagnose OSA, is polysomnography (PSG), a test that should be performed in a specialist sleep clinic and requires a complete overnight stay at the clinic. The PSG system can provide accurate and real-time data; however, it introduces several challenges such as complexity, invasiveness, excessive cost, and absence of privacy. Technological advancements in hardware and software enable noninvasive and unobtrusive sensing of vital signs. An alternative approach which may help diagnose OSA and other cardiovascular diseases is the ballistocardiography. The ballistocardiogram (BCG) signal captures the ballistic forces of the heart caused by the sudden ejection of blood into the great vessels with each heartbeat, breathing, and body movement. In recent years, BCG sensors such as polyvinylidene fluoride film-based sensors, electromechanical films, strain Gauges, hydraulic sensors, microbend fiber-optic sensors as well as fiber Bragg grating sensors have been integrated within ambient locations such as mattresses, pillows, chairs, beds, or even weighing scales, to capture BCG signals, and thereby measure vital signs. Analysis of the BCG signal is a challenging process, despite being a more convenient and comfortable method of vital signs monitoring. In practice, BCG sensors are placed under bed mattresses for sleep tracking, and hence several factors, e.g., mattress thickness, body movements, motion artifacts, bed-partners, etc. can deteriorate the signal. In this paper, we introduce the sensors that are being used for obtaining BCG signals. We also present an in-depth review of the signal processing methods as applied to the various sensors, to analyze the BCG signal and extract physiological parameters such heart rate and breathing rate, as well as determining sleep stages. Besides, we recommend which methods are more suitable for processing BCG signals due to their nonlinear and nonstationary characteristics.
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Affiliation(s)
- Ibrahim Sadek
- 1ST Engineering Electronics-SUTD Cyber Security Laboratory, Singapore University of Technology and Design (SUTD), Singapore, Singapore
| | - Jit Biswas
- 2iTrust - Center for Research in Cyber Security, Singapore University of Technology and Design (SUTD), Singapore, Singapore
| | - Bessam Abdulrazak
- 3Département d'Informatique, Faculté des Sciences, Université de Sherbrooke (UdeS), Sherbrooke, Canada
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Klovatch-Podlipsky I, Gazit T, Fahoum F, Tsirelson B, Kipervasser S, Kremer U, Ben-Zeev B, Goldberg-Stern H, Eisenstein O, Harpaz Y, Levy O, Kirschner A, Neufeld MY, Fried I, Hendler T, Medvedovsky M. Dual array EEG-fMRI: An approach for motion artifact suppression in EEG recorded simultaneously with fMRI. Neuroimage 2016; 142:674-86. [PMID: 27402597 DOI: 10.1016/j.neuroimage.2016.07.014] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Revised: 07/05/2016] [Accepted: 07/06/2016] [Indexed: 10/21/2022] Open
Abstract
OBJECTIVE Although simultaneous recording of EEG and MRI has gained increasing popularity in recent years, the extent of its clinical use remains limited by various technical challenges. Motion interference is one of the major challenges in EEG-fMRI. Here we present an approach which reduces its impact with the aid of an MR compatible dual-array EEG (daEEG) in which the EEG itself is used both as a brain signal recorder and a motion sensor. METHODS We implemented two arrays of EEG electrodes organized into two sets of nearly orthogonally intersecting wire bundles. The EEG was recorded using referential amplifiers inside a 3T MR-scanner. Virtual bipolar measurements were taken both along bundles (creating a small wire loop and therefore minimizing artifact) and across bundles (creating a large wire loop and therefore maximizing artifact). Independent component analysis (ICA) was applied. The resulting ICA components were classified into brain signal and noise using three criteria: 1) degree of two-dimensional spatial correlation between ICA coefficients along bundles and across bundles; 2) amplitude along bundles vs. across bundles; 3) correlation with ECG. The components which passed the criteria set were transformed back to the channel space. Motion artifact suppression and the ability to detect interictal epileptic spikes following daEEG and Optimal Basis Set (OBS) procedures were compared in 10 patients with epilepsy. RESULTS The SNR achieved by daEEG was 11.05±3.10 and by OBS was 8.25±1.01 (p<0.00001). In 9 of 10 patients, more spikes were detected after daEEG than after OBS (p<0.05). SIGNIFICANCE daEEG improves signal quality in EEG-fMRI recordings, expanding its clinical and research potential.
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Javaid AQ, Ashouri H, Tridandapani S, Inan OT. Elucidating the Hemodynamic Origin of Ballistocardiographic Forces: Toward Improved Monitoring of Cardiovascular Health at Home. IEEE J Transl Eng Health Med 2016; 4:1900208. [PMID: 27620621 PMCID: PMC4991685 DOI: 10.1109/jtehm.2016.2544752] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2015] [Revised: 01/19/2016] [Accepted: 03/10/2016] [Indexed: 11/06/2022]
Abstract
The ballistocardiogram (BCG), a signal describing the reaction forces of the body to cardiac ejection of blood, has recently gained interest in the research community as a potential tool for monitoring the mechanical aspects of cardiovascular health for patients at home and during normal activities of daily living. An important limitation in the field of BCG research is that while the BCG signal measures the forces of the body, the information desired (and understood) by clinicians and caregivers, regarding mechanical health of the cardiovascular system, is typically expressed as blood pressure or flow. This paper aims to explore, using system identification tools, the mathematical relationship between the BCG signal and the better-understood impedance cardiography (ICG) and arterial blood pressure (ABP) waveforms, with a series of human subject studies designed to asynchronously modulate cardiac output and blood pressure and with different magnitudes. With this approach, we demonstrate for 19 healthy subjects that the BCG waveform more closely maps to the ICG (flow) waveform as compared with the finger-cuff-based ABP (pressure) waveform, and that the BCG can provide a more accurate estimate of stroke volume ([Formula: see text], [Formula: see text]) as compared with pulse pressure changes ([Formula: see text]). We also examined, as a feasibility study, for one subject, the ability to calibrate the BCG measurement tool with an ICG measurement on the first day, and then track changes in stroke volume on subsequent days. Accordingly, we conclude that the BCG is a signal more closely related to blood flow than pressures, and that a key health parameter for titrating care-stroke volume-can potentially be accurately measured with BCG signals at home using unobtrusive and inexpensive hardware, such as a modified weighing scale, as compared with the state-of-the-art ICG and ABP devices, which are expensive and obtrusive for use at home.
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Affiliation(s)
- Abdul Qadir Javaid
- School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332 USA
| | - Hazar Ashouri
- School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332 USA
| | - Srini Tridandapani
- Department of Radiology and Imaging Sciences Emory University School of Medicine Atlanta GA 30322 USA
| | - Omer T Inan
- School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332 USA
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8
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Abreu R, Leite M, Jorge J, Grouiller F, van der Zwaag W, Leal A, Figueiredo P. Ballistocardiogram artifact correction taking into account physiological signal preservation in simultaneous EEG-fMRI. Neuroimage 2016; 135:45-63. [PMID: 27012501 DOI: 10.1016/j.neuroimage.2016.03.034] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2015] [Accepted: 03/14/2016] [Indexed: 11/21/2022] Open
Abstract
The ballistocardiogram (BCG) artifact is currently one of the most challenging in the EEG acquired concurrently with fMRI, with correction invariably yielding residual artifacts and/or deterioration of the physiological signals of interest. In this paper, we propose a family of methods whereby the EEG is decomposed using Independent Component Analysis (ICA) and a novel approach for the selection of BCG-related independent components (ICs) is used (PROJection onto Independent Components, PROJIC). Three ICA-based strategies for BCG artifact correction are then explored: 1) BCG-related ICs are removed from the back-reconstruction of the EEG (PROJIC); and 2-3) BCG-related ICs are corrected for the artifact occurrences using an Optimal Basis Set (OBS) or Average Artifact Subtraction (AAS) framework, before back-projecting all ICs onto EEG space (PROJIC-OBS and PROJIC-AAS, respectively). A novel evaluation pipeline is also proposed to assess the methods performance, which takes into account not only artifact but also physiological signal removal, allowing for a flexible weighting of the importance given to physiological signal preservation. This evaluation is used for the group-level parameter optimization of each algorithm on simultaneous EEG-fMRI data acquired using two different setups at 3T and 7T. Comparison with state-of-the-art BCG correction methods showed that PROJIC-OBS and PROJIC-AAS outperformed the others when priority was given to artifact removal or physiological signal preservation, respectively, while both PROJIC-AAS and AAS were in general the best choices for intermediate trade-offs. The impact of the BCG correction on the quality of event-related potentials (ERPs) of interest was assessed in terms of the relative reduction of the standard error (SE) across trials: 26/66%, 32/62% and 18/61% were achieved by, respectively, PROJIC, PROJIC-OBS and PROJIC-AAS, for data collected at 3T/7T. Although more significant improvements were achieved at 7T, the results were qualitatively comparable for both setups, which indicate the wide applicability of the proposed methodologies and recommendations.
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Krishnaswamy P, Bonmassar G, Poulsen C, Pierce ET, Purdon PL, Brown EN. Reference-free removal of EEG-fMRI ballistocardiogram artifacts with harmonic regression. Neuroimage 2015; 128:398-412. [PMID: 26151100 DOI: 10.1016/j.neuroimage.2015.06.088] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2015] [Revised: 05/20/2015] [Accepted: 06/16/2015] [Indexed: 10/23/2022] Open
Abstract
Combining electroencephalogram (EEG) recording and functional magnetic resonance imaging (fMRI) offers the potential for imaging brain activity with high spatial and temporal resolution. This potential remains limited by the significant ballistocardiogram (BCG) artifacts induced in the EEG by cardiac pulsation-related head movement within the magnetic field. We model the BCG artifact using a harmonic basis, pose the artifact removal problem as a local harmonic regression analysis, and develop an efficient maximum likelihood algorithm to estimate and remove BCG artifacts. Our analysis paradigm accounts for time-frequency overlap between the BCG artifacts and neurophysiologic EEG signals, and tracks the spatiotemporal variations in both the artifact and the signal. We evaluate performance on: simulated oscillatory and evoked responses constructed with realistic artifacts; actual anesthesia-induced oscillatory recordings; and actual visual evoked potential recordings. In each case, the local harmonic regression analysis effectively removes the BCG artifacts, and recovers the neurophysiologic EEG signals. We further show that our algorithm outperforms commonly used reference-based and component analysis techniques, particularly in low SNR conditions, the presence of significant time-frequency overlap between the artifact and the signal, and/or large spatiotemporal variations in the BCG. Because our algorithm does not require reference signals and has low computational complexity, it offers a practical tool for removing BCG artifacts from EEG data recorded in combination with fMRI.
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Affiliation(s)
- Pavitra Krishnaswamy
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; Harvard-MIT Division of Health Sciences and Technology (HST), Cambridge, MA, USA.
| | - Giorgio Bonmassar
- Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, MA, USA; MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | | | - Eric T Pierce
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Patrick L Purdon
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA; Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Emery N Brown
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; Harvard-MIT Division of Health Sciences and Technology (HST), Cambridge, MA, USA; Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
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10
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Schulz MA, Regenbogen C, Moessnang C, Neuner I, Finkelmeyer A, Habel U, Kellermann T. On utilizing uncertainty information in template-based EEG-fMRI ballistocardiogram artifact removal. Psychophysiology 2015; 52:857-63. [PMID: 25649223 DOI: 10.1111/psyp.12406] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2014] [Accepted: 11/07/2014] [Indexed: 11/29/2022]
Abstract
The correction of ballistocardiogram artifacts in simultaneous EEG-fMRI often yields unsatisfactory results. To improve the signal-to-noise ratio (SNR) of results, we inferred EEG signal uncertainty from postcorrection artifact residuals and computed the uncertainty-weighted mean of ERPs. Using an uncertainty-weighted mean significantly and consistently reduced both inter- and intrasubject SEM in the analysis of auditory evoked responses (AER, indicated by the N1-P2 complex) and in the effects of an auditory oddball paradigm (N1-P3 complex, standard-deviant difference). SNR increased by 3% on average for the AER amplitude (intrasubject) and 17% on average for the auditory oddball ERP (intersubject). This demonstrates that weighting by uncertainty complements existing artifact correction algorithms to increase SNR in ERPs. More specifically, it is an efficient method to utilize seemingly corrupt (difficult-to-correct) EEG data that might otherwise be discarded.
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Affiliation(s)
- Marc-Andre Schulz
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Christina Regenbogen
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany.,Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Carolin Moessnang
- Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Heidelberg, Mannheim, Germany
| | - Irene Neuner
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany.,JARA Translational Brain Medicine
| | | | - Ute Habel
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany.,JARA Translational Brain Medicine
| | - Thilo Kellermann
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany.,JARA Translational Brain Medicine
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11
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Luo Q, Huang X, Glover GH. Ballistocardiogram artifact removal with a reference layer and standard EEG cap. J Neurosci Methods 2014; 233:137-49. [PMID: 24960423 DOI: 10.1016/j.jneumeth.2014.06.021] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2014] [Revised: 05/25/2014] [Accepted: 06/16/2014] [Indexed: 11/15/2022]
Abstract
BACKGROUND In simultaneous EEG-fMRI, the EEG recordings are severely contaminated by ballistocardiogram (BCG) artifacts, which are caused by cardiac pulsations. To reconstruct and remove the BCG artifacts, one promising method is to measure the artifacts in the absence of EEG signal by placing a group of electrodes (BCG electrodes) on a conductive layer (reference layer) insulated from the scalp. However, current BCG reference layer (BRL) methods either use a customized EEG cap composed of electrode pairs, or need to construct the custom reference layer through additional model-building experiments for each EEG-fMRI experiment. These requirements have limited the versatility and efficiency of BRL. The aim of this study is to propose a more practical and efficient BRL method and compare its performance with the most popular BCG removal method, the optimal basis sets (OBS) algorithm. NEW METHOD By designing the reference layer as a permanent and reusable cap, the new BRL method is able to be used with a standard EEG cap, and no extra experiments and preparations are needed to use the BRL in an EEG-fMRI experiment. RESULTS The BRL method effectively removed the BCG artifacts from both oscillatory and evoked potential scalp recordings and recovered the EEG signal. COMPARISON WITH EXISTING METHOD Compared to the OBS, this new BRL method improved the contrast-to-noise ratios of the alpha-wave, visual, and auditory evoked potential signals by 101%, 76%, and 75%, respectively, employing 160 BCG electrodes. Using only 20 BCG electrodes, the BRL improved the EEG signal by 74%/26%/41%, respectively. CONCLUSION The proposed method can substantially improve the EEG signal quality compared with traditional methods.
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Affiliation(s)
- Qingfei Luo
- Department of Radiology, Stanford University, Stanford, CA 94305, USA.
| | - Xiaoshan Huang
- Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Gary H Glover
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
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12
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Abstract
This paper was originally written for a conference entitled ‘The Future of Medical History. Now it ought to be clear – certainly to historians – that the future of anything is hard to predict; but at least in the short term, any future for medical history seems likely to include the history of disease, and the history of coronary heart disease (CHD) provides an excellent example of what the history of disease has to offer to a wide range of audiences.
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Affiliation(s)
- Joel D Howell
- Department of History, 1029J Tisch Hall, University of Michigan, Ann Arbor, MI 48109-1003, USA.
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