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Egorov V, Rosen T, Hill J, Khandelwal M, Kurtenoks V, Francy B, Sarvazyan N. Evaluating the Efficacy of Cervical Tactile Ultrasound Technique as a Predictive Tool for Spontaneous Preterm Birth. OPEN JOURNAL OF OBSTETRICS AND GYNECOLOGY 2024; 14:832-846. [PMID: 38845755 PMCID: PMC11155442 DOI: 10.4236/ojog.2024.145067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/09/2024]
Abstract
Background Premature cervical softening and shortening may be considered an early mechanical failure that predispose to preterm birth. Purpose This study aims to explore the applicability of an innovative cervical tactile ultrasound approach for predicting spontaneous preterm birth (sPTB). Materials and Methods Eligible participants were women with low-risk singleton pregnancies in their second trimester, enrolled in this prospective observational study. A Cervix Monitor (CM) device was designed with a vaginal probe comprising four tactile sensors and a single ultrasound transducer operating at 5 MHz. The probe enabled the application of controllable pressure to the external cervical surface, facilitating the acquisition of stress-strain data from both anterior and posterior cervical sectors. Gestational age at delivery was recorded and compared against cervical elasticity. Results CM examination data were analyzed for 127 women at 240/7 - 286/7 gestational weeks. sPTB was observed in 6.3% of the cases. The preterm group exhibited a lower average cervical stress-to-strain ratio (elasticity) of 0.70 ± 0.26 kPa/mm compared to the term group's 1.63 ± 0.65 kPa/mm with a p-value of 1.1 × 10-4. Diagnostic accuracy for predicting spontaneous preterm birth based solely on cervical elasticity data was found to be 95.0% (95% CI, 88.5 - 100.0). Conclusion These findings suggest that measuring cervical elasticity with the designed tactile ultrasound probe has the potential to predict spontaneous preterm birth in a cost-effective manner.
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Affiliation(s)
| | - Todd Rosen
- Department of Obstetrics, Gynecology and Reproductive Sciences, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Jennifer Hill
- Department of Obstetrics, Gynecology and Reproductive Sciences, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Meena Khandelwal
- Department of Maternal-Fetal Medicine, Cooper Medical School of Rowan University, Camden, New Jersey, USA
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Al-Zaben A, Al-Fahoum A, Ababneh M, Al-Naami B, Al-Omari G. Improved recovery of cardiac auscultation sounds using modified cosine transform and LSTM-based masking. Med Biol Eng Comput 2024:10.1007/s11517-024-03088-x. [PMID: 38627355 DOI: 10.1007/s11517-024-03088-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 04/02/2024] [Indexed: 04/24/2024]
Abstract
Obtaining accurate cardiac auscultation signals, including basic heart sounds (S1 and S2) and subtle signs of disease, is crucial for improving cardiac diagnoses and making the most of telehealth. This research paper introduces an innovative approach that utilizes a modified cosine transform (MCT) and a masking strategy based on long short-term memory (LSTM) to effectively distinguish heart sounds and murmurs from background noise and interfering sounds. The MCT is used to capture the repeated pattern of the heart sounds, while the LSTMs are trained to construct masking based on the repeated MCT spectrum. The proposed strategy's performance in maintaining the clinical relevance of heart sounds continues to demonstrate effectiveness, even in environments marked by increased noise and complex disruptions. The present work highlights the clinical significance and reliability of the suggested methodology through in-depth signal visualization and rigorous statistical performance evaluations. In comparative assessments, the proposed approach has demonstrated superior performance compared to recent algorithms, such as LU-Net and PC-DAE. Furthermore, the system's adaptability to various datasets enhances its reliability and practicality. The suggested method is a potential way to improve the accuracy of cardiovascular diagnostics in an era of rapid advancement in medical signal processing. The proposed approach showed an enhancement in the average signal-to-noise ratio (SNR) by 9.6 dB at an input SNR of - 6 dB and by 3.3 dB at an input SNR of 10 dB. The average signal distortion ratio (SDR) achieved across a variety of input SNR values was 8.56 dB.
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Affiliation(s)
- Awad Al-Zaben
- Biomedical Engineering Department, Engineering Faculty, Hashemite University, Zarqa, Jordan.
- Biomedical Systems and Medical Informatics Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid, Jordan.
| | - Amjad Al-Fahoum
- Biomedical Systems and Medical Informatics Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid, Jordan
| | - Muhannad Ababneh
- Faculty of Medicine, Interventional Cardiologist, Jordan University of Science and Technology, Irbid, Jordan
| | - Bassam Al-Naami
- Biomedical Engineering Department, Engineering Faculty, Hashemite University, Zarqa, Jordan
| | - Ghadeer Al-Omari
- Biomedical Systems and Medical Informatics Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid, Jordan
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3
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Wang W, Qin D, Wang S, Fang Y, Zheng Y. A multi-channel UNet framework based on SNMF-DCNN for robust heart-lung-sound separation. Comput Biol Med 2023; 164:107282. [PMID: 37499297 DOI: 10.1016/j.compbiomed.2023.107282] [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: 10/06/2022] [Revised: 06/14/2023] [Accepted: 07/16/2023] [Indexed: 07/29/2023]
Abstract
Cardiopulmonary and cardiovascular diseases are fatal factors that threaten human health and cause many deaths worldwide each year, so it is essential to screen cardiopulmonary disease more accurately and efficiently. Auscultation is a non-invasive method for physicians' perception of the disease. The Heart Sounds (HS) and Lung Sounds (LS) recorded by an electronic stethoscope consist of acoustic information that is helpful in the diagnosis of pulmonary conditions. Still, inter-interference between HS and LS presented in both the time and frequency domains blocks diagnostic efficiency. This paper proposes a blind source separation (BSS)strategy that first classifies Heart-Lung-Sound (HLS) according to its LS features and then separates it into HS and LS. Sparse Non-negative Matrix Factorization (SNMF) is employed to extract the LS features in HLS, then proposed a network constructed by Dilated Convolutional Neural Network (DCNN) to classify HLS into five types by the magnitude features of LS. Finally, Multi-Channel UNet (MCUNet) separation model is utilized for each category of HLS. This paper is the first to propose the HLS classification method SNMF-DCNN and apply UNet to the cardiopulmonary sound separation domain. Compared with other state-of-the-art methods, the proposed framework in this paper has higher separation quality and robustness.
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Affiliation(s)
- Weibo Wang
- College of Electrical and Electronic Information, Xihua University, Chengdu, 610036, China.
| | - Dimei Qin
- College of Electrical and Electronic Information, Xihua University, Chengdu, 610036, China
| | - Shubo Wang
- College of Electrical and Electronic Information, Xihua University, Chengdu, 610036, China
| | - Yu Fang
- College of Electrical and Electronic Information, Xihua University, Chengdu, 610036, China
| | - Yongkang Zheng
- State Grid Sichuan Electric Power Research Institute, Chengdu, 610096, China
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4
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Ghosh SK, Ponnalagu R. Investigation of Discrete Wavelet Transform Domain Optimal Parametric Approach for Denoising of Phonocardiogram Signal. J MECH MED BIOL 2022. [DOI: 10.1142/s0219519422500464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Phonocardiogram (PCG) signals are contaminated with various noise signals, which hinders the accurate diagnostic interpretation of the signal. Discrete wavelet transform (DWT) is a well-known technique used to remove noise from PCG signals and improve signal quality. The performance of DWT-based denoising depends upon several parameters involved in the process, such as mother wavelets used for decomposition, the number of decomposition levels (DLs), thresholding technique used and the threshold estimation rule followed. In this work, an investigative study is carried out to select the optimal parameter values which give the best denoising performance. The metrics such as mean-square error (MSE), normalized-mean-square error (NMSE), root-mean-square error (RMSE), percentage root-mean-square difference (PRD) and signal-to-noise ratio (SNR) are used to evaluate the performance of the denoising in this study. The results obtained show that the fifth-order Coiflet wavelet is best suited for denoising PCG signals when applied with the soft thresholding (ST) function and rigrsure threshold selection rule. Also, the optimum number of DLs resulting in better performance is level 6. SNR value obtained from the studies shows the efficacy of the parameters selected for denoising. The denoised PCG signals provide accurate information to determine various kinds of heart valve-related disorders (HVDs).
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Affiliation(s)
- Samit Kumar Ghosh
- Electrical and Electronics Engineering Department, Birla Institute of Technology and Science, Pilani, Hyderabad - 500078, Telangana, India
| | - R. N. Ponnalagu
- Electrical and Electronics Engineering Department, Birla Institute of Technology and Science, Pilani, Hyderabad - 500078, Telangana, India
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Cheikh F, Benhassine NE, Sbaa S. Fetal phonocardiogram signals denoising using improved complete ensemble (EMD) with adaptive noise and optimal thresholding of wavelet coefficients. BIOMED ENG-BIOMED TE 2022; 67:237-247. [PMID: 35647890 DOI: 10.1515/bmt-2022-0006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 05/17/2022] [Indexed: 11/15/2022]
Abstract
Although fetal phonocardiogram (fPCG) signals have become a good indicator for discovered heart disease, they may be contaminated by various noises that reduce the signals quality and the final diagnosis decision. Moreover, the noise may cause the risk of the data to misunderstand the heart signal and to misinterpret it. The main objective of this paper is to effectively remove noise from the fPCG signal to make it clinically feasible. So, we proposed a novel noise reduction method based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), wavelet threshold and Crow Search Algorithm (CSA). This noise reduction method, named ICEEMDAN-DWT-CSA, has three major advantages. They were, (i) A better suppress of mode mixing and a minimized number of IMFs, (ii) A choice of wavelet corresponding to the study signal proven by the literature and (iii) Selection of the optimal threshold value. Firstly, the noisy fPCG signal is decomposed into Intrinsic Mode Functions (IMFs) by the (ICEEMDAN). Each noisy IMFs were decomposed by the Discrete Wavelet Transform (DWT). Then, the optimal threshold value using the (CSA) technique is selected and the thresholding function is carried out in the detail's coefficients. Secondly, each denoised (IMFs) is reconstructed by applying the Inverse Discrete Wavelet Transform (IDWT). Finally, all these denoised (IMFs) are combined to get the denoised fPCG signal. The performance of the proposed method has been evaluated by Signal to Noise Ratio (SNR), Mean Square Error (MSE) and the Correlation Coefficient (COR). The experiment gave a better result than some standard methods.
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Affiliation(s)
- Fethi Cheikh
- Department of Electrical Engineering, University of Biskra, Biskra, Algeria.,Laboratory of LESIA, University of Biskra, Biskra, Algeria
| | - Nasser Edinne Benhassine
- Department of Mathematics and Informatics, Aflou university Center, Aflou, Algeria.,Advanced Control Laboratory (LABCAV), University 8 Mai 1945 Guelma, Guelma, Algeri
| | - Salim Sbaa
- Department of Electrical Engineering, University of Biskra, Biskra, Algeria.,Laboratory of LESIA, University of Biskra, Biskra, Algeria
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6
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An audio processing pipeline for acquiring diagnostic quality heart sounds via mobile phone. Comput Biol Med 2022; 145:105415. [PMID: 35366471 DOI: 10.1016/j.compbiomed.2022.105415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 02/22/2022] [Accepted: 03/14/2022] [Indexed: 11/27/2022]
Abstract
Recently, heart sound signals captured using mobile phones have been employed to develop data-driven heart disease detection systems. Such signals are generally captured in person by trained clinicians who can determine if the recorded heart sounds are of diagnosable quality. However, mobile phones have the potential to support heart health diagnostics, even where access to trained medical professionals is limited. To adopt mobile phones as self-diagnostic tools for the masses, we would need to have a mechanism to automatically establish that heart sounds recorded by non-expert users in uncontrolled conditions have the required quality for diagnostic purposes. This paper proposes a quality assessment and enhancement pipeline for heart sounds captured using mobile phones. The pipeline analyzes a heart sound and determines if it has the required quality for diagnostic tasks. Also, in cases where the quality of the captured signal is below the required threshold, the pipeline can improve the quality by applying quality enhancement algorithms. Using this pipeline, we can also provide feedback to users regarding the cause of low-quality signal capture and guide them towards a successful one. We conducted a survey of a group of thirteen clinicians with auscultation skills and experience. The results of this survey were used to inform and validate the proposed quality assessment and enhancement pipeline. We observed a high level of agreement between the survey results and fundamental design decisions within the proposed pipeline. Also, the results indicate that the proposed pipeline can reduce our dependency on trained clinicians for capture of diagnosable heart sounds.
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7
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A simple proposition for heart sound signal de-noising for effective components identification in normal and abnormal cases. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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8
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Bondareva E, Han J, Bradlow W, Mascolo C. Segmentation-free Heart Pathology Detection Using Deep Learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:669-672. [PMID: 34891381 DOI: 10.1109/embc46164.2021.9630203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Cardiovascular (CV) diseases are the leading cause of death in the world, and auscultation is typically an essential part of a cardiovascular examination. The ability to diagnose a patient based on their heart sounds is a rather difficult skill to master. Thus, many approaches for automated heart auscultation have been explored. However, most of the previously proposed methods involve a segmentation step, the performance of which drops significantly for high pulse rates or noisy signals. In this work, we propose a novel segmentation-free heart sound classification method. Specifically, we apply discrete wavelet transform to denoise the signal, followed by feature extraction and feature reduction. Then, Support Vector Machines and Deep Neural Networks are utilised for classification. On the PASCAL heart sound dataset our approach showed superior performance compared to others, achieving 81% and 96% precision on normal and murmur classes, respectively. In addition, for the first time, the data were further explored under a user-independent setting, where the proposed method achieved 92% and 86% precision on normal and murmur, demonstrating the potential of enabling automatic murmur detection for practical use.
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9
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Adithya PC, Hart S, Tejada-Martinez A, Moreno WA, Sankar R. Novel Catheter Multiscope: A Feasibility Study. IEEE Trans Biomed Eng 2021; 68:606-615. [PMID: 32746059 DOI: 10.1109/tbme.2020.3009361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Open Challenges: Continuous monitoring of fundamental cardiovascular hemodynamic parameters is essential to accomplish critical care diagnostics. Today's standard of care measures these critical parameters using multiple monitoring technologies. These state-of-the-art technologies require expensive instrumentation and complex infrastructure. Therefore, it is challenging to use current technologies to accomplish monitoring in a low resource setting. OBJECTIVE In order to address the challenges caused by having to use multiple monitoring systems, a point of care monitoring device was developed in this work to provide multiple critical parameters by uniquely measuring the hemodynamic process. METHODS To demonstrate the usability of this novel catheter multiscope, a feasibility study was performed using an animal model. The developed measurement system first acquires the dynamics of blood flow through a minimally invasive catheter. Then, a signal processing framework was developed to characterize the blood flow dynamics and to obtain critical parameters such as heart rate, respiratory rate, and blood pressure. The framework used to extract the physiological data corresponding to the acoustic field of the blood flow consisted of a noise cancellation method and wavelet-based source separation. RESULTS The preliminary results of the acoustic pressure field of the blood flow revealed the presence of acoustic heart and respiratory pulses. A unique framework was also developed to extract continuous blood pressure from the acoustic pressure field of the blood flow. Finally, the computed heart and respiratory rates, systolic and diastolic pressures were benchmarked with actual values measured using conventional devices to validate the hypothesis. CONCLUSION The results confirm that catheter multiscope can provide multiple critical parameters with clinical reliability. SIGNIFICANCE A novel critical care monitoring system has been developed to accurately measure heart rate, respiratory rate, systolic and diastolic pressures from the blood flow dynamics.
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10
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Khan MU, Aziz S, Akram T, Amjad F, Iqtidar K, Nam Y, Khan MA. Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme. SENSORS 2021; 21:s21010247. [PMID: 33401652 PMCID: PMC7794944 DOI: 10.3390/s21010247] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 12/22/2020] [Accepted: 12/24/2020] [Indexed: 12/27/2022]
Abstract
Hypertension is an antecedent to cardiac disorders. According to the World Health Organization (WHO), the number of people affected with hypertension will reach around 1.56 billion by 2025. Early detection of hypertension is imperative to prevent the complications caused by cardiac abnormalities. Hypertension usually possesses no apparent detectable symptoms; hence, the control rate is significantly low. Computer-aided diagnosis based on machine learning and signal analysis has recently been applied to identify biomarkers for the accurate prediction of hypertension. This research proposes a new expert hypertension detection system (EHDS) from pulse plethysmograph (PuPG) signals for the categorization of normal and hypertension. The PuPG signal data set, including rich information of cardiac activity, was acquired from healthy and hypertensive subjects. The raw PuPG signals were preprocessed through empirical mode decomposition (EMD) by decomposing a signal into its constituent components. A combination of multi-domain features was extracted from the preprocessed PuPG signal. The features exhibiting high discriminative characteristics were selected and reduced through a proposed hybrid feature selection and reduction (HFSR) scheme. Selected features were subjected to various classification methods in a comparative fashion in which the best performance of 99.4% accuracy, 99.6% sensitivity, and 99.2% specificity was achieved through weighted k-nearest neighbor (KNN-W). The performance of the proposed EHDS was thoroughly assessed by tenfold cross-validation. The proposed EHDS achieved better detection performance in comparison to other electrocardiogram (ECG) and photoplethysmograph (PPG)-based methods.
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Affiliation(s)
- Muhammad Umar Khan
- Department of Electronics Engineering, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (M.U.K.); (F.A.)
| | - Sumair Aziz
- Department of Electronics Engineering, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (M.U.K.); (F.A.)
- Correspondence: (S.A.); (Y.N.)
| | - Tallha Akram
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, Wah Cantonment, Islamabad 45550, Pakistan;
| | - Fatima Amjad
- Department of Electronics Engineering, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (M.U.K.); (F.A.)
| | - Khushbakht Iqtidar
- Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan;
| | - Yunyoung Nam
- Department of Computer Science and Engineering, Soonchunhyang University, Asan 31538, Korea
- Correspondence: (S.A.); (Y.N.)
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Upper Limb Bionic Orthoses: General Overview and Forecasting Changes. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10155323] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Using robotics in modern medicine is slowly becoming a common practice. However, there are still important life science fields which are currently devoid of such advanced technology. A noteworthy example of a life sciences field which would benefit from process automation and advanced robotic technology is rehabilitation of the upper limb with the use of an orthosis. Here, we present the state-of-the-art and prospects for development of mechanical design, actuator technology, control systems, sensor systems, and machine learning methods in rehabilitation engineering. Moreover, current technical solutions, as well as forecasts on improvement, for exoskeletons are presented and reviewed. The overview presented might be the cornerstone for future research on advanced rehabilitation engineering technology, such as an upper limb bionic orthosis.
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12
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Rouis M, Sbaa S, Benhassine NE. The effectiveness of the choice of criteria on the stationary and non-stationary noise removal in the phonocardiogram (PCG) signal using discrete wavelet transform. BIOMED ENG-BIOMED TE 2020; 65:353-366. [PMID: 31782944 DOI: 10.1515/bmt-2019-0197] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Accepted: 08/30/2019] [Indexed: 11/15/2022]
Abstract
The greatest problem with recording heart sounds is parasitic noise effects. A reasonable solution to reduce noise can be carried out by minimization of extraneous noises in the vicinity of the patient during recording, in addition to the methods of signal processing that must be effective in noisy environments. Wavelet transform has become an essential tool for many applications, but its effectiveness is influenced by main parameters. Determination of mother wavelet function and decomposition level (DL) are important key factors to demonstrate the advantages of wavelet denoising. So, selection of optimal mother wavelet with DL is a main challenge to current algorithms. The principal aim of this study was the choice of an appropriate criterion for finding the optimal DL and the optimal mother wavelet function according to four criteria which are: signal-to-noise ratio (SNR), mean square error (MSE), percentage root-mean-square difference (PRD) and the structure similarity index measure (SSIM) for testing the robustness of the proposed algorithm. The proposed method is applied to the PCG signal contaminated with four colored noise types, in addition to the Gaussian noise. The obtained results show the effectiveness of the proposed method in reducing noise from the noisy PCG signals, especially at a low SNR.
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Affiliation(s)
- Mohamed Rouis
- Department of Electrical Engineering, University of Biskra, Biskra 07020, Algeria
- Laboratory of LESIA, University of Biskra, Biskra, Algeria
| | - Salim Sbaa
- Department of Electrical Engineering, University of Biskra, Biskra 07020, Algeria
- Laboratory of LESIA, University of Biskra, Biskra, Algeria
| | - Nasser Edinne Benhassine
- Department of Exact Science and Informatics, University Zian Achour, Djelfa 17000, Algeria
- Advanced Control Laboratory (LABCAV), Université 8 Mai 1945 Guelma, Guelma, Algeria
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13
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Egorov V, Rosen T, van Raalte H, Kurtenoks V. Cervical Characterization with Tactile-Ultrasound Probe. OPEN JOURNAL OF OBSTETRICS AND GYNECOLOGY 2020; 10:85-99. [PMID: 32133244 PMCID: PMC7055710 DOI: 10.4236/ojog.2020.101008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
BACKGROUND Premature cervical softening and shortening may be considered an early mechanical failure that predisposes to preterm birth. Preliminary clinical studies demonstrate that cervical elastography may be able to quantify this phenomenon and predict spontaneous preterm delivery. OBJECTIVE To explore a new approach for cervix elasticity and length measurements with tactile-ultrasound probe. METHODS Cervix probe has tactile array and ultrasound transducer designed to apply controllable load to cervix and acquire stress-strain data for calculation of cervical elasticity (Young's modulus) and cervical length for four cervix sectors. Average values, standard deviations, intraclass correlation coefficients and the 95% limits of agreement (Bland-Altman plots) were estimated. RESULTS Ten non-pregnant and ten pregnant women were examined with the probe. The study with non-pregnant women demonstrated a reliable acquisition of the tactile signals. The ultrasound signals had a prolonged appearance; identification of the internal os of the cervix in these signals was not reliable. The study with pregnant women with the gestational age of 25.4 ± 2.3 weeks demonstrated reliable data acquisition with real-time visualization of the ultrasound signals. Average values for cervical elasticity and standard deviations of 19.7 ± 15.4 kPa and length of 30.7 ± 6.6 mm were calculated based on two measurements per 4 sectors. Measurement repeatability calculated as intraclass correlation coefficients between two measurements at the same cervix sector on pregnant women was found to be 0.97 for cervical elasticity and 0.93 for the cervical length. The 95% limits of agreement of 1) cervical elasticity were from -22.4% to +14.9%, and 2) cervical length from -13.3% to +16.5%. CONCLUSIONS This study demonstrated clinically acceptable measurement performance and reproducibility. The availability of stress-strain data allowed the computation of cervical elasticity and length. This approach has the potential to provide cervical markers to predict spontaneous preterm delivery.
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Affiliation(s)
| | - Todd Rosen
- Department of Obstetrics, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
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14
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Rouis M, Ouafi A, Sbaa S. Optimal level and order detection in wavelet decomposition for PCG signal denoising. ACTA ACUST UNITED AC 2019; 64:163-176. [PMID: 29791308 DOI: 10.1515/bmt-2018-0001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Accepted: 04/09/2018] [Indexed: 11/15/2022]
Abstract
The recorded phonocardiogram (PCG) signal is often contaminated by different types of noises that can be seen in the frequency band of the PCG signal, which may change the characteristics of this signal. Discrete wavelet transform (DWT) has become one of the most important and powerful tools of signal representation, but its effectiveness is influenced by the issue of the selected mother wavelet and decomposition level (DL). The selection of the DL and the mother wavelet are the main challenges. This work proposes a new approach for finding an optimal DL and optimal mother wavelet for PCG signal denoising. Our approach consists of two algorithms designed to tackle the problems of noise and variability caused by PCG acquisition in a real clinical environment for different categories of patients. The results obtained are evaluated by examining the coherence analysie (Coh) correlation coefficient (Corr) and the mean square error (MSE) and signal-to-noise ratio (SNR) in simulated noisy PCG signals. The experimental results show that the proposed method can effectively reduce noise.
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Affiliation(s)
- Mohamed Rouis
- Department of Electrical Engineering, University of Biskra, Biskra, Algeria.,Laboratory of LESIA, University of Biskra, Biskra, Algeria
| | - Abdelkrim Ouafi
- Department of Electrical Engineering, University of Biskra, Biskra, Algeria.,Laboratory of LESIA, University of Biskra, Biskra, Algeria
| | - Salim Sbaa
- Department of Electrical Engineering, University of Biskra, Biskra, Algeria.,Laboratory of LESIA, University of Biskra, Biskra, Algeria
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15
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Carballo-Leyenda B, Villa JG, López-Satué J, Rodríguez-Marroyo JA. Characterizing Wildland Firefighters' Thermal Environment During Live-Fire Suppression. Front Physiol 2019; 10:949. [PMID: 31427982 PMCID: PMC6688527 DOI: 10.3389/fphys.2019.00949] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 07/09/2019] [Indexed: 11/23/2022] Open
Abstract
Wildland firefighters work under adverse environments (e.g., heat and fire exposure), which contribute to increasing the heat strain. Despite this there is a paucity of knowledge about the thermal environment in real wildfire suppression scenarios. Therefore, the main purpose of this study was to characterize the environmental thermal exposure and the risk of heat burn injuries during real wildfire suppression (n = 23). To characterize the wildland firefighter’s (n = 5) local thermal exposure, measurements of air temperature and heat flux were performed. Heat flux measurements were made using four thin-planar heat flux sensors. Two were affixed on the outer surface of the garment on the left chest and thigh. Two other sensors were placed on the inner surface of the fabric in parallel to those placed externally. Four thermal classes were defined based on the heat flux across the inner sensors (≤1000, ≤5000, ≤7000, and >7000 W⋅m–2). The risk of pain and first-degree burns were calculated using the dose of thermal radiation method. The inner sensors mean and maximum heat flux and environment temperature were 286.7 ± 255.0 and 2370.4 ± 3004.5 W⋅m–2 and 32.6 ± 8.9 and 78.0 ± 8.9°C, respectively. Approximately 81, 15, and 3.5% of the exposure time the heat flux was ≤1000, >1000–5000, and >5000 W⋅m–2, respectively. The highest average and maximum thermal dose values were ∼94 and ∼110 (kW⋅m–2)4/3⋅s. In conclusion, the thermal exposure obtained may be considered light. However, high thermal exposure values may be obtained in punctual moments, which can elicit first-degree burns.
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Affiliation(s)
| | - José G Villa
- VALFIS Research Group, Institute of Biomedicine, University of León, León, Spain
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Zhang Q, Francisco CO, Kabir M, Zhang J, Montazeri N, Taati B, Yadollahi A. Noise Removal of Tracheal Sound Recorded During CPET to Determine Respiratory Rate. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2019:4650-4653. [PMID: 31946900 DOI: 10.1109/embc.2019.8857738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This study aimed to extract respiratory signal from tracheal sound recordings during cardio-pulmonary exercise test (CPET), which is the state-of-the-art diagnosis of cardiovascular and respiratory diseases. However, the availability of CPET is limited due to its expense. Physiological signal analysis is a convenient alternative to measure clinical parameters. We collected data from 30 healthy adults and applied wavelet transform thresholding (WTT), empirical mode decomposition (EMD), and Synchrosqueezing transform filtering (SST) to de-noise the raw data. Signal to noise ratio (SNR) was calculated as a quantitative measure of signal quality. We observed that SST yielded the highest SNR and introduced lowest signal distortion by visual-auditory inspection. Respiratory rate was then determined using the signal extracted by SST. Discrepancy of respiratory rate determined from the signal and the gold standard CPET was within 1.2 breaths per minute. In conclusion, SST effectively removed noises in tracheal sound recorded during CPET and provided analyzable respiratory signal for clinical parameter determination.
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17
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A Wavelet Transform-Based Neural Network Denoising Algorithm for Mobile Phonocardiography. SENSORS 2019; 19:s19040957. [PMID: 30813479 PMCID: PMC6412858 DOI: 10.3390/s19040957] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Revised: 02/13/2019] [Accepted: 02/20/2019] [Indexed: 11/30/2022]
Abstract
Cardiovascular pathologies cause 23.5% of human deaths, worldwide. An auto-diagnostic system monitoring heart activity, which can identify the early symptoms of cardiac illnesses, might reduce the death rate caused by these problems. Phonocardiography (PCG) is one of the possible techniques able to detect heart problems. Nevertheless, acoustic signal enhancement is required since it is exposed to various disturbances coming from different sources. The most common denoising enhancement is based on the Wavelet Transform (WT). However, the WT is highly susceptible to variations in the noise frequency distribution. This paper proposes a new adaptive denoising algorithm, which combines WT and Time Delay Neural Networks (TDNN). The acquired signal is decomposed by means of the WT using the coif five-wavelet basis at the tenth decomposition level and then provided as input to the TDNN. Besides the advantage of adaptive thresholding, the reason for using TDNNs is their capacity of estimating the Inverse Wavelet Transform (IWT). The best parameters of the TDNN were found for a NN consisting of 25 neurons in the first and 15 in the second layer and the delay block of 12 samples. The method was evaluated on several pathological heart sounds and on signals recorded in a noisy environment. The performance of the developed system with respect to other wavelet-based denoising approaches was validated by the online questionnaire.
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HO WENHSIEN, CHEN YENMINGJ, ZHANG YUZHEN, TAO YANYUN, KUO HSINWEN. HEART DISEASES DETECTION FROM NOISY RECORDINGS OF SMARTPHONE DEVICES. J MECH MED BIOL 2018. [DOI: 10.1142/s0219519418500392] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This paper aims to develop an algorithm to detect heart diseases through ordinary smartphones without additional equipment for cost accessibility. Among various vital signs emitted by organs, sounds can be easily observed and carry ample information. However, these sounds are small and noisy. Detecting anomalies involves great challenges in signal processing. This study presents a novel method that overcomes noises to estimate cardiovascular health. We use time-scale techniques in time series analysis to extract disease traits and suppress excessive ambient noises. Using datasets from PhysioNet, our model achieved a nearly 100% accuracy in heart disease diagnosis. Our approach also performs well under excessive noises for diseases producing heart murmurs. With heavy noise contaminated signals, training accuracy still closed to 100%, and the testing accuracy still remained around 84%.
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Affiliation(s)
- WEN-HSIEN HO
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan, ROC
| | - YENMING J. CHEN
- Department of Logistics Management, National Kaohsiung University of Science and Technology, Taiwan, ROC
| | - YUZHEN ZHANG
- Cardiology, The First Affiliated Hospital of Soochow University, Suzhou, P. R. China
| | - YANYUN TAO
- School of Railway Transportation, Soochow University, Suzhou, P. R. China
| | - HSIN-WEN KUO
- College of Management, National Kaohsiung University of Science and Technology, Taiwan, ROC
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Leal A, Nunes D, Couceiro R, Henriques J, Carvalho P, Quintal I, Teixeira C. Noise detection in phonocardiograms by exploring similarities in spectral features. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.04.015] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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20
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Siddiqui SA, Zhang Y, Lloret J, Song H, Obradovic Z. Pain-Free Blood Glucose Monitoring Using Wearable Sensors: Recent Advancements and Future Prospects. IEEE Rev Biomed Eng 2018; 11:21-35. [DOI: 10.1109/rbme.2018.2822301] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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21
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Jain PK, Tiwari AK. A Robust Algorithm for Segmentation of Phonocardiography Signal Using Tunable Quality Wavelet Transform. J Med Biol Eng 2017. [DOI: 10.1007/s40846-017-0320-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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22
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Jain PK, Tiwari AK. An adaptive thresholding method for the wavelet based denoising of phonocardiogram signal. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2017.07.002] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Mondal A, Saxena I, Tang H, Banerjee P. A Noise Reduction Technique Based on Nonlinear Kernel Function for Heart Sound Analysis. IEEE J Biomed Health Inform 2017; 22:775-784. [PMID: 28207404 DOI: 10.1109/jbhi.2017.2667685] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The main difficulty encountered in interpretation of cardiac sound is interference of noise. The contaminated noise obscures the relevant information, which are useful for recognition of heart diseases. The unwanted signals are produced mainly by lungs and surrounding environment. In this paper, a novel heart sound denoising technique has been introduced based on a combined framework of wavelet packet transform and singular value decomposition (SVD). The most informative node of the wavelet tree is selected on the criteria of mutual information measurement. Next, the coefficient corresponding to the selected node is processed by the SVD technique to suppress noisy component from heart sound signal. To justify the efficacy of the proposed technique, several experiments have been conducted with heart sound dataset, including normal and pathological cases at different signal to noise ratios. The significance of the method is validated by statistical analysis of the results. The biological information preserved in denoised heart sound signal is evaluated by the k-means clustering algorithm. The overall results show that the proposed method is superior than the baseline methods.
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Nunes D, Leal A, Couceiro R, Henriques J, Mendes L, Carvalho P, Teixeira C. A low-complex multi-channel methodology for noise detection in phonocardiogram signals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:5936-9. [PMID: 26737643 DOI: 10.1109/embc.2015.7319743] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The phonocardiography (PCG) is an important technique for the diagnosis of several heart conditions. However, the PCG signal is highly prone to noise, which can be an obstacle for the detection and interpretation of physiological heart sounds. Thus, the detection and elimination of noise present in PCG signals is crucial for the accurate analysis of heart sounds, especially in p-health environments. Noise can be introduced by various internal factors (e.g., respiration and laughing) and by external conditions (e.g., phone ringing or door closing). To mention also that the noise frequency components are typically overlapped with the PCG spectrum, increasing the complexity of the analysis. The purpose of the present work consists in the detection of noisy periods willfully introduced during the performance of three different sets of tasks. The developed method returns the classification of the signal content, in a window-by-window analysis and can be divided in two distinct phases. The first step consists in the search for a noise free window using a feature obtained from the PCG time-domain. In the second step, the noise free window is compared with the remaining signal. The classification between clean and contaminated PCG is performed using two features from the frequency domain. The algorithm was able to discriminate clean from contamined PCG sections with an average sensitivity and specificity of 95.59% and 92.68%, respectively.
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Leng S, Tan RS, Chai KTC, Wang C, Ghista D, Zhong L. The electronic stethoscope. Biomed Eng Online 2015; 14:66. [PMID: 26159433 PMCID: PMC4496820 DOI: 10.1186/s12938-015-0056-y] [Citation(s) in RCA: 138] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2015] [Accepted: 06/11/2015] [Indexed: 11/13/2022] Open
Abstract
Most heart diseases are associated with and reflected by the sounds that the heart produces. Heart auscultation, defined as listening to the heart sound, has been a very important method for the early diagnosis of cardiac dysfunction. Traditional auscultation requires substantial clinical experience and good listening skills. The emergence of the electronic stethoscope has paved the way for a new field of computer-aided auscultation. This article provides an in-depth study of (1) the electronic stethoscope technology, and (2) the methodology for diagnosis of cardiac disorders based on computer-aided auscultation. The paper is based on a comprehensive review of (1) literature articles, (2) market (state-of-the-art) products, and (3) smartphone stethoscope apps. It covers in depth every key component of the computer-aided system with electronic stethoscope, from sensor design, front-end circuitry, denoising algorithm, heart sound segmentation, to the final machine learning techniques. Our intent is to provide an informative and illustrative presentation of the electronic stethoscope, which is valuable and beneficial to academics, researchers and engineers in the technical field, as well as to medical professionals to facilitate its use clinically. The paper provides the technological and medical basis for the development and commercialization of a real-time integrated heart sound detection, acquisition and quantification system.
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Affiliation(s)
- Shuang Leng
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore.
| | - Ru San Tan
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore.
- Cardiovascular and Metabolic Disorders Program, Duke-NUS Graduate Medical School, 8 College Road, Singapore, 169857, Singapore.
| | - Kevin Tshun Chuan Chai
- Institute of Microelectronics, A*STAR, 11 Science Park Road, Singapore, 117685, Singapore.
| | - Chao Wang
- Institute of Microelectronics, A*STAR, 11 Science Park Road, Singapore, 117685, Singapore.
| | | | - Liang Zhong
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore.
- Cardiovascular and Metabolic Disorders Program, Duke-NUS Graduate Medical School, 8 College Road, Singapore, 169857, Singapore.
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