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Tang Q, Tao C, Li X, Hu H, Chu X, Liu S, Zhang L, Su B, Xu J, An H. Data-knowledge co-driven feature based prediction model via photoplethysmography for evaluating blood pressure. Comput Biol Med 2024; 181:109076. [PMID: 39216405 DOI: 10.1016/j.compbiomed.2024.109076] [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: 04/22/2024] [Revised: 08/01/2024] [Accepted: 08/23/2024] [Indexed: 09/04/2024]
Abstract
BACKGROUND Knowledge feature (KF) with clear physiological significance of photoplethysmography are widely used in predicting blood pressure. However, KF primarily focus on local information of photoplethysmography, which may struggle to capture the overall characteristics. METHODS Firstly, functional data analysis (FDA) was introduced to extract two types of data feature (DF). Furthermore, data-knowledge co-driven feature (DKCF) was proposed by combining FDA and constraints of KF. Finally, random forest, ada boost, gradient boosting, support vector machine and deep neural network were adopted, to compare the abilities of KF, DFs and DKCF in predicting blood pressure with two datasets (A published dataset and a self-collected dataset). RESULTS Under the premise of extracting only 9 features, the average mean absolute errors (MAE) of systolic blood pressure (SBP) and diastolic blood pressure (DBP) obtained by DKCF are both the smallest in dataset 1. In dataset 2, DKCF acquires the smallest MAE in predicting SBP and obtains the second smallest MAE in predicting DBP. CONCLUSIONS The results demonstrate that low-dimensional DKCF of photoplethysmography is closely correlated with blood pressure, which may serve as an important indicator for health assessment.
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Affiliation(s)
- Qingfeng Tang
- Digital and Intelligent Health Research Center, Anqing Normal University, Anqing 246133, China; School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
| | - Chao Tao
- Digital and Intelligent Health Research Center, Anqing Normal University, Anqing 246133, China.
| | - Xin Li
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
| | - Huihui Hu
- Digital and Intelligent Health Research Center, Anqing Normal University, Anqing 246133, China.
| | - Xiaoyu Chu
- Digital and Intelligent Health Research Center, Anqing Normal University, Anqing 246133, China.
| | - Shiping Liu
- Digital and Intelligent Health Research Center, Anqing Normal University, Anqing 246133, China.
| | - Liangliang Zhang
- Digital and Intelligent Health Research Center, Anqing Normal University, Anqing 246133, China.
| | - Benyue Su
- Digital and Intelligent Health Research Center, Anqing Normal University, Anqing 246133, China; School of Mathematics and Computer Science, Tongling University, Tongling 244061, China.
| | - Jiatuo Xu
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
| | - Hui An
- Health Management & Physical Examination Center, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, China.
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Wong KFM, Huang W, Ee DYH, Ng EYK. Design and validation of dual-point time-differentiated photoplethysmogram (2PPG) wearable for cuffless blood pressure estimation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 253:108251. [PMID: 38824806 DOI: 10.1016/j.cmpb.2024.108251] [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: 01/23/2024] [Revised: 05/02/2024] [Accepted: 05/24/2024] [Indexed: 06/04/2024]
Abstract
BACKGROUND & OBJECTIVES Measurement of blood pressure (BP) in ambulatory patients is crucial for at high-risk cardiovascular patients. A non-obtrusive, non-occluding device that continuously measures BP via photoplethysmography will enable long-term ambulatory assessment of BP. The aim of this study is to validate the metasense 2PPG cuffless wearable design for continuous BP estimation without ECG. METHODS A customized high-speed electronic optical sensor architecture with laterally spaced reflectance pulse oximetry was designed into a simple unobtrusive low-power wearable in the form of a watch. 78 volunteers with a mean age of 32.72 ± 7.4 years (21 to 64), 51% male, 49% female were recruited with ECG-2PPG signals acquired. The fiducial features of the 2PPG morphologies were then attributed to the estimator. A 9-1 K-fold cross-validation was applied in the ML. RESULTS The correlation for PTT-SBP was 0.971 and for PTT-DBP was 0.954. The mean absolute error was 3.167±1.636 mmHg for SBP and 6.4 ± 3.9 mm Hg for DBP. The ambulatory estimate for SBP and DBP for an individual over 3 days with 8-hour recordings was 0.70-0.81 for SBP and 0.42-0.51 for DBP with a ± 2.65 mmHg for SBP and ±2.02 mmHg for DBP. For SBP, 98% of metasense measurements were within 15 mm Hg and for DBP, 91% of metasense measurements were within 10 mmHg CONCLUSIONS: The metasense device provides continuous, non-invasive BP estimations that are comparable to ambulatory BP meters. The portability and unobtrusiveness of this device, as well as the ability to continuously measure BP could one day enable long-term ambulatory BP measurement for precision cardiovascular therapeutic regimens.
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Affiliation(s)
- Kei Fong Mark Wong
- School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore
| | | | | | - Eddie Yin Kwee Ng
- School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore.
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Raju SMTU, Dipto SA, Hossain MI, Chowdhury MAS, Haque F, Nashrah AT, Nishan A, Khan MMH, Hashem MMA. DNN-BP: a novel framework for cuffless blood pressure measurement from optimal PPG features using deep learning model. Med Biol Eng Comput 2024:10.1007/s11517-024-03157-1. [PMID: 38963467 DOI: 10.1007/s11517-024-03157-1] [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: 06/05/2023] [Accepted: 06/10/2024] [Indexed: 07/05/2024]
Abstract
Continuous blood pressure (BP) provides essential information for monitoring one's health condition. However, BP is currently monitored using uncomfortable cuff-based devices, which does not support continuous BP monitoring. This paper aims to introduce a blood pressure monitoring algorithm based on only photoplethysmography (PPG) signals using the deep neural network (DNN). The PPG signals are obtained from 125 unique subjects with 218 records and filtered using signal processing algorithms to reduce the effects of noise, such as baseline wandering, and motion artifacts. The proposed algorithm is based on pulse wave analysis of PPG signals, extracted various domain features from PPG signals, and mapped them to BP values. Four feature selection methods are applied and yielded four feature subsets. Therefore, an ensemble feature selection technique is proposed to obtain the optimal feature set based on major voting scores from four feature subsets. DNN models, along with the ensemble feature selection technique, outperformed in estimating the systolic blood pressure (SBP) and diastolic blood pressure (DBP) compared to previously reported approaches that rely only on the PPG signal. The coefficient of determination ( R 2 ) and mean absolute error (MAE) of the proposed algorithm are 0.962 and 2.480 mmHg, respectively, for SBP and 0.955 and 1.499 mmHg, respectively, for DBP. The proposed approach meets the Advancement of Medical Instrumentation standard for SBP and DBP estimations. Additionally, according to the British Hypertension Society standard, the results attained Grade A for both SBP and DBP estimations. It concludes that BP can be estimated more accurately using the optimal feature set and DNN models. The proposed algorithm has the potential ability to facilitate mobile healthcare devices to monitor continuous BP.
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Affiliation(s)
- S M Taslim Uddin Raju
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh.
| | - Safin Ahmed Dipto
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh
| | - Md Imran Hossain
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh
| | - Md Abu Shahid Chowdhury
- Department of Biomedical Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh
| | - Fabliha Haque
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh
| | - Ayesha Tun Nashrah
- Department of Biomedical Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh
| | - Araf Nishan
- Department of Business Administration, International American University, Los Angeles, CA, 90010, USA
| | - Md Mahamudul Hasan Khan
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh
| | - M M A Hashem
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh
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Liu ZD, Li Y, Zhang YT, Zeng J, Chen ZX, Liu JK, Miao F. HGCTNet: Handcrafted Feature-Guided CNN and Transformer Network for Wearable Cuffless Blood Pressure Measurement. IEEE J Biomed Health Inform 2024; 28:3882-3894. [PMID: 38687656 DOI: 10.1109/jbhi.2024.3395445] [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: 05/02/2024]
Abstract
Biosignals collected by wearable devices, such as electrocardiogram and photoplethysmogram, exhibit redundancy and global temporal dependencies, posing a challenge in extracting discriminative features for blood pressure (BP) estimation. To address this challenge, we propose HGCTNet, a handcrafted feature-guided CNN and transformer network for cuffless BP measurement based on wearable devices. By leveraging convolutional operations and self-attention mechanisms, we design a CNN-Transformer hybrid architecture to learn features from biosignals that capture both local information and global temporal dependencies. Then, we introduce a handcrafted feature-guided attention module that utilizes handcrafted features extracted from biosignals as query vectors to eliminate redundant information within the learned features. Finally, we design a feature fusion module that integrates the learned features, handcrafted features, and demographics to enhance model performance. We validate our approach using two large wearable BP datasets: the CAS-BP dataset and the Aurora-BP dataset. Experimental results demonstrate that HGCTNet achieves an estimation error of 0.9 ± 6.5 mmHg for diastolic BP (DBP) and 0.7 ± 8.3 mmHg for systolic BP (SBP) on the CAS-BP dataset. On the Aurora-BP dataset, the corresponding errors are -0.4 ± 7.0 mmHg for DBP and -0.4 ± 8.6 mmHg for SBP. Compared to the current state-of-the-art approaches, HGCTNet reduces the mean absolute error of SBP estimation by 10.68% on the CAS-BP dataset and 9.84% on the Aurora-BP dataset. These results highlight the potential of HGCTNet in improving the performance of wearable cuffless BP measurements.
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Saikevičius L, Raudonis V, Dervinis G, Baranauskas V. Non-Contact Vision-Based Techniques of Vital Sign Monitoring: Systematic Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:3963. [PMID: 38931747 PMCID: PMC11207835 DOI: 10.3390/s24123963] [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: 05/15/2024] [Revised: 06/10/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024]
Abstract
The development of non-contact techniques for monitoring human vital signs has significant potential to improve patient care in diverse settings. By facilitating easier and more convenient monitoring, these techniques can prevent serious health issues and improve patient outcomes, especially for those unable or unwilling to travel to traditional healthcare environments. This systematic review examines recent advancements in non-contact vital sign monitoring techniques, evaluating publicly available datasets and signal preprocessing methods. Additionally, we identified potential future research directions in this rapidly evolving field.
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Affiliation(s)
| | - Vidas Raudonis
- Automation Department, Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania; (L.S.); (G.D.); (V.B.)
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Alam J, Khan MF, Khan MA, Singh R, Mundazeer M, Kumar P. A Systematic Approach Focused on Machine Learning Models for Exploring the Landscape of Physiological Measurement and Estimation Using Photoplethysmography (PPG). J Cardiovasc Transl Res 2024; 17:669-684. [PMID: 38010481 DOI: 10.1007/s12265-023-10462-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/08/2023] [Indexed: 11/29/2023]
Abstract
A non-invasive optical technique known as photoplethysmography (PPG) can be used to provide various physiological measurements and estimations. PPG can be used to assess cardiovascular disease (CVD). Hypertension is a primary risk factor for CVD and a major health problem worldwide. PPG is popular because of its important applications in the evaluation of cardiac activity, variations in venous blood volume, blood oxygen saturation, blood pressure and heart rate variability, etc. In this study, we provide a comprehensive analysis of the extraction of various physiological parameters using PPG waveforms. In addition, we focused on the role of machine learning (ML) models used for the estimation of blood pressure and hypertension classification based on PPG waveforms to make future research and innovation recommendations. This study will be helpful for researchers, scientists, and medical practitioners working on PPG waveforms for monitoring, screening, and diagnosis, as a comparative study or reference.
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Affiliation(s)
- Javed Alam
- Quantlase Lab LLC, Masdar City, Abu Dhabi, United Arab Emirates.
| | | | - Meraj Alam Khan
- Quantlase Lab LLC, Masdar City, Abu Dhabi, United Arab Emirates
- DigiBiomics Inc, Mississauga, Ontario, Canada
| | - Rinky Singh
- Quantlase Lab LLC, Masdar City, Abu Dhabi, United Arab Emirates
| | | | - Pramod Kumar
- Quantlase Lab LLC, Masdar City, Abu Dhabi, United Arab Emirates
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El Sherbini A, Rosenson RS, Al Rifai M, Virk HUH, Wang Z, Virani S, Glicksberg BS, Lavie CJ, Krittanawong C. Artificial intelligence in preventive cardiology. Prog Cardiovasc Dis 2024; 84:76-89. [PMID: 38460897 DOI: 10.1016/j.pcad.2024.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Accepted: 03/03/2024] [Indexed: 03/11/2024]
Abstract
Artificial intelligence (AI) is a field of study that strives to replicate aspects of human intelligence into machines. Preventive cardiology, a subspeciality of cardiovascular (CV) medicine, aims to target and mitigate known risk factors for CV disease (CVD). AI's integration into preventive cardiology may introduce novel treatment interventions and AI-centered clinician assistive tools to reduce the risk of CVD. AI's role in nutrition, weight loss, physical activity, sleep hygiene, blood pressure, dyslipidemia, smoking, alcohol, recreational drugs, and mental health has been investigated. AI has immense potential to be used for the screening, detection, and monitoring of the mentioned risk factors. However, the current literature must be supplemented with future clinical trials to evaluate the capabilities of AI interventions for preventive cardiology. This review discusses present examples, potentials, and limitations of AI's role for the primary and secondary prevention of CVD.
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Affiliation(s)
- Adham El Sherbini
- Faculty of Health Sciences, Queen's University, Kingston, ON, Canada
| | - Robert S Rosenson
- Cardiometabolics Unit, Mount Sinai Hospital, Mount Sinai Heart, NY, United States of America
| | - Mahmoud Al Rifai
- Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, United States of America
| | - Hafeez Ul Hassan Virk
- Harrington Heart & Vascular Institute, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, OH, United States of America
| | - Zhen Wang
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States of America; Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States of America
| | - Salim Virani
- Section of Cardiology, The Aga Khan University, Texas Heart Institute, Baylor College of Medicine, Houston, TX, United States of America
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Carl J Lavie
- John Ochsner Heart and Vascular Institute, Ochsner Clinical School, The University of Queensland School of Medicine, New Orleans, LA, USA
| | - Chayakrit Krittanawong
- Cardiology Division, NYU Langone Health and NYU School of Medicine, New York, NY, United States of America.
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Shokouhmand A, Jiang X, Ayazi F, Ebadi N. MEMS Fingertip Strain Plethysmography for Cuffless Estimation of Blood Pressure. IEEE J Biomed Health Inform 2024; 28:2699-2712. [PMID: 38442050 DOI: 10.1109/jbhi.2024.3372968] [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: 03/07/2024]
Abstract
OBJECTIVE To develop a cuffless method for estimating blood pressure (BP) from fingertip strain plethysmography (SPG) recordings. METHODS A custom-built micro-electromechanical systems (MEMS) strain sensor is employed to record heartbeat-induced vibrations at the fingertip. An XGboost regressor is then trained to relate SPG recordings to beat-to-beat systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP) values. For this purpose, each SPG segment in this setup is represented by a feature vector consisting of cardiac time interval, amplitude features, statistical properties, and demographic information of the subjects. In addition, a novel concept, coined geometric features, are introduced and incorporated into the feature space to further encode the dynamics in SPG recordings. The performance of the regressor is assessed on 32 healthy subjects through 5-fold cross-validation (5-CV) and leave-subject-out cross validation (LSOCV). RESULTS Mean absolute errors (MAEs) of 3.88 mmHg and 5.45 mmHg were achieved for DBP and SBP estimations, respectively, in the 5-CV setting. LSOCV yielded MAEs of 8.16 mmHg for DBP and 16.81 mmHg for SBP. Through feature importance analysis, 3 geometric and 26 integral-related features introduced in this work were identified as primary contributors to BP estimation. The method exhibited robustness against variations in blood pressure level (normal to critical) and body mass index (underweight to obese), with MAE ranges of [1.28, 4.28] mmHg and [2.64, 7.52] mmHg, respectively. CONCLUSION The findings suggest high potential for SPG-based BP estimation at the fingertip. SIGNIFICANCE This study presents a fundamental step towards the augmentation of optical sensors that are susceptible to dark skin tones.
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Goda MÁ, Charlton PH, Behar JA. pyPPG: a Python toolbox for comprehensive photoplethysmography signal analysis. Physiol Meas 2024; 45:045001. [PMID: 38478997 PMCID: PMC11003363 DOI: 10.1088/1361-6579/ad33a2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 02/21/2024] [Accepted: 03/13/2024] [Indexed: 04/09/2024]
Abstract
Objective.Photoplethysmography is a non-invasive optical technique that measures changes in blood volume within tissues. It is commonly and being increasingly used for a variety of research and clinical applications to assess vascular dynamics and physiological parameters. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and limited open tools exist for continuous photoplethysmogram (PPG) analysis. Consequently, the primary objective of this research was to identify, standardize, implement and validate key digital PPG biomarkers.Approach.This work describes the creation of a standard Python toolbox, denotedpyPPG, for long-term continuous PPG time-series analysis and demonstrates the detection and computation of a high number of fiducial points and digital biomarkers using a standard fingerbased transmission pulse oximeter.Main results.The improved PPG peak detector had an F1-score of 88.19% for the state-of-the-art benchmark when evaluated on 2054 adult polysomnography recordings totaling over 91 million reference beats. The algorithm outperformed the open-source original Matlab implementation by ∼5% when benchmarked on a subset of 100 randomly selected MESA recordings. More than 3000 fiducial points were manually annotated by two annotators in order to validate the fiducial points detector. The detector consistently demonstrated high performance, with a mean absolute error of less than 10 ms for all fiducial points.Significance.Based on these fiducial points,pyPPGengineered a set of 74 PPG biomarkers. Studying PPG time-series variability usingpyPPGcan enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models.pyPPGis available onhttps://physiozoo.com/.
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Affiliation(s)
- Márton Á Goda
- Faculty of Biomedical Engineering, Technion Institute of Technology, Technion-IIT, Haifa, 32000, Israel
- Pázmány Péter Catholic University Faculty of Information Technology and Bionics, Budapest, Práter u. 50/A, 1083, Hungary
| | - Peter H Charlton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom
| | - Joachim A Behar
- Faculty of Biomedical Engineering, Technion Institute of Technology, Technion-IIT, Haifa, 32000, Israel
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Nishan A, M. Taslim Uddin Raju S, Hossain MI, Dipto SA, M. Tanvir Uddin S, Sijan A, Chowdhury MAS, Ahmad A, Mahamudul Hasan Khan M. A continuous cuffless blood pressure measurement from optimal PPG characteristic features using machine learning algorithms. Heliyon 2024; 10:e27779. [PMID: 38533045 PMCID: PMC10963242 DOI: 10.1016/j.heliyon.2024.e27779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 03/01/2024] [Accepted: 03/06/2024] [Indexed: 03/28/2024] Open
Abstract
Background and objective Hypertension is a potentially dangerous health condition that can be detected by measuring blood pressure (BP). Blood pressure monitoring and measurement are essential for preventing and treating cardiovascular diseases. Cuff-based devices, on the other hand, are uncomfortable and prevent continuous BP measurement. Methods In this study, a new non-invasive and cuff-less method for estimating Systolic Blood Pressure (SBP), Mean Arterial Pressure (MAP), and Diastolic Blood Pressure (DBP) has been proposed using characteristic features of photoplethysmogram (PPG) signals and nonlinear regression algorithms. PPG signals were collected from 219 participants, which were then subjected to preprocessing and feature extraction steps. Analyzing PPG and its derivative signals, a total of 46 time, frequency, and time-frequency domain features were extracted. In addition, the age and gender of each subject were also included as features. Further, correlation-based feature selection (CFS) and Relief F feature selection (ReliefF) techniques were used to select the relevant features and reduce the possibility of over-fitting the models. Finally, support vector regression (SVR), K-nearest neighbour regression (KNR), decision tree regression (DTR), and random forest regression (RFR) were established to develop the BP estimation model. Regression models were trained and evaluated on all features as well as selected features. The best regression models for SBP, MAP, and DBP estimations were selected separately. Results The SVR model, along with the ReliefF-based feature selection algorithm, outperforms other algorithms in estimating the SBP, MAP, and DBP with the mean absolute error of 2.49, 1.62 and 1.43 mmHg, respectively. The proposed method meets the Advancement of Medical Instrumentation standard for BP estimations. Based on the British Hypertension Society standard, the results also fall within Grade A for SBP, MAP, and DBP. Conclusion The findings show that the method can be used to estimate blood pressure non-invasively, without using a cuff or calibration, and only by utilizing the PPG signal characteristic features.
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Affiliation(s)
- Araf Nishan
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna - 9203, Bangladesh
| | - S. M. Taslim Uddin Raju
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna - 9203, Bangladesh
| | - Md Imran Hossain
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna - 9203, Bangladesh
| | - Safin Ahmed Dipto
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna - 9203, Bangladesh
| | - S. M. Tanvir Uddin
- Department of Electrical and Electronic Engineering, Dhaka University of Engineering & Technology, Gazipur, Bangladesh
| | - Asif Sijan
- Department of Software Engineering, American International University, Dhaka, Bangladesh
| | - Md Abu Shahid Chowdhury
- Department of Biomedical Engineering, Khulna University of Engineering & Technology, Khulna - 9203, Bangladesh
| | - Ashfaq Ahmad
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna - 9203, Bangladesh
| | - Md Mahamudul Hasan Khan
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna - 9203, Bangladesh
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Liu SH, Ting CE, Wang JJ, Chang CJ, Chen W, Sharma AK. Estimation of Gait Parameters for Adults with Surface Electromyogram Based on Machine Learning Models. SENSORS (BASEL, SWITZERLAND) 2024; 24:734. [PMID: 38339451 PMCID: PMC10857519 DOI: 10.3390/s24030734] [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: 10/16/2023] [Revised: 01/18/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024]
Abstract
Gait analysis has been studied over the last few decades as the best way to objectively assess the technical outcome of a procedure designed to improve gait. The treating physician can understand the type of gait problem, gain insight into the etiology, and find the best treatment with gait analysis. The gait parameters are the kinematics, including the temporal and spatial parameters, and lack the activity information of skeletal muscles. Thus, the gait analysis measures not only the three-dimensional temporal and spatial graphs of kinematics but also the surface electromyograms (sEMGs) of the lower limbs. Now, the shoe-worn GaitUp Physilog® wearable inertial sensors can easily measure the gait parameters when subjects are walking on the general ground. However, it cannot measure muscle activity. The aim of this study is to measure the gait parameters using the sEMGs of the lower limbs. A self-made wireless device was used to measure the sEMGs from the vastus lateralis and gastrocnemius muscles of the left and right feet. Twenty young female subjects with a skeletal muscle index (SMI) below 5.7 kg/m2 were recruited for this study and examined by the InBody 270 instrument. Four parameters of sEMG were used to estimate 23 gait parameters. They were measured using the GaitUp Physilog® wearable inertial sensors with three machine learning models, including random forest (RF), decision tree (DT), and XGBoost. The results show that 14 gait parameters could be well-estimated, and their correlation coefficients are above 0.800. This study signifies a step towards a more comprehensive analysis of gait with only sEMGs.
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Affiliation(s)
- Shing-Hong Liu
- Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung City 41349, Taiwan; (S.-H.L.); (C.-E.T.)
| | - Chi-En Ting
- Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung City 41349, Taiwan; (S.-H.L.); (C.-E.T.)
| | - Jia-Jung Wang
- Department of Biomedical Engineering, I-Shou University, Kaohsiung 82445, Taiwan
| | - Chun-Ju Chang
- Department of Golden-Ager Industry Management, Chaoyang University of Technology, Taichung City 41349, Taiwan;
| | - Wenxi Chen
- Division of Information Systems, School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu City 965-8580, Fukushima, Japan;
| | - Alok Kumar Sharma
- Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung City 41349, Taiwan; (S.-H.L.); (C.-E.T.)
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Sarkar S, Ghosh A. Schrödinger spectrum based continuous cuff-less blood pressure estimation using clinically relevant features from PPG signal and its second derivative. Comput Biol Med 2023; 166:107558. [PMID: 37806054 DOI: 10.1016/j.compbiomed.2023.107558] [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: 04/06/2023] [Revised: 09/02/2023] [Accepted: 09/28/2023] [Indexed: 10/10/2023]
Abstract
The presented study estimates cuff-less blood pressure (BP) from photoplethysmography (PPG) signals using multiple machine-learning (ML) models and the semi-classical signal analysis (SCSA) technique. The study proposes a novel signal reconstruction algorithm, which optimizes the semi-classical constant of the SCSA approach and eliminates the trade-off between complexity and accuracy during signal reconstruction. It predicts BP values using regression algorithms by processing reconstructed PPG signals' spectral features, extracting clinically relevant PPG and its second derivative's (SDPPG) morphological features. The developed method was assessed using a virtual in-silico dataset with more than 4000 subjects and the Multi-Parameter Intelligent Monitoring in Intensive Care Units (MIMIC-II) dataset. Results showed that the method attained a mean absolute error (MAE) of 5.37 and 2.96 mmHg for systolic and diastolic BP, respectively, using the CatBoost algorithm. This approach met the Association for the Advancement of Medical Instrumentation's standard and achieved Grade A for all BP categories in the British Hypertension Society protocol. The proposed framework performs well even when applied to a combined clinically relevant database originating from MIMIC-III and the Queensland dataset. The proposed method's performance is further evaluated in a non-clinical setting with noisy and deformed PPG signals to validate the efficacy of the SCSA method. The noise stress tests further showed that the algorithm maintained its key feature detection, signal reconstruction capability, and estimation accuracy up to a 10 dB SNR ratio. The proposed cuff-less BP estimation technique has the potential to perform well in mobile healthcare devices due to its straightforward implementation approach.
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Affiliation(s)
- Sayan Sarkar
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
| | - Aayushman Ghosh
- Department of Electronics and Telecommunication Engineering, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, 11103, India; Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
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Hwang CS, Kim YH, Hyun JK, Kim JH, Lee SR, Kim CM, Nam JW, Kim EY. Evaluation of the Photoplethysmogram-Based Deep Learning Model for Continuous Respiratory Rate Estimation in Surgical Intensive Care Unit. Bioengineering (Basel) 2023; 10:1222. [PMID: 37892952 PMCID: PMC10604201 DOI: 10.3390/bioengineering10101222] [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: 08/21/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 10/29/2023] Open
Abstract
The respiratory rate (RR) is a significant indicator to evaluate a patient's prognosis and status; however, it requires specific instrumentation or estimates from other monitored signals. A photoplethysmogram (PPG) is extensively used in clinical environments as well as in intensive care units (ICUs) to primarily monitor peripheral circulation while capturing indirect information about intrathoracic pressure changes. This study aims to apply and evaluate several deep learning models using a PPG for the continuous and accurate estimation of the RRs of patients. The dataset was collected twice for 2 min each in 100 patients aged 18 years and older from the surgical intensive care unit of a tertiary referral hospital. The BIDMC and CapnoBase public datasets were also analyzed. The collected dataset was preprocessed and split according to the 5-fold cross-validation. We used seven deep learning models, including our own Dilated Residual Neural Network, to check how accurately the RR estimates match the ground truth using the mean absolute error (MAE). As a result, when validated using the collected dataset, our model showed the best results with a 1.2628 ± 0.2697 MAE on BIDMC and RespNet and with a 3.1268 ± 0.6363 MAE on our dataset, respectively. In conclusion, RR estimation using PPG-derived models is still challenging and has many limitations. However, if there is an equal amount of data from various breathing groups to train, we expect that various models, including our Dilated ResNet model, which showed good results, can achieve better results than the current ones.
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Affiliation(s)
- Chi Shin Hwang
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Yong Hwan Kim
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Jung Kyun Hyun
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Joon Hwang Kim
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Seo Rak Lee
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Choong Min Kim
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Jung Woo Nam
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Eun Young Kim
- Division of Trauma and Surgical Critical Care, Department of Surgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Banpo-daero 222, Seocho-gu, Seoul 06591, Republic of Korea
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Attivissimo F, D’Alessandro VI, De Palma L, Lanzolla AML, Di Nisio A. Non-Invasive Blood Pressure Sensing via Machine Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:8342. [PMID: 37837172 PMCID: PMC10574845 DOI: 10.3390/s23198342] [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: 08/28/2023] [Revised: 09/21/2023] [Accepted: 10/07/2023] [Indexed: 10/15/2023]
Abstract
In this paper, a machine learning (ML) approach to estimate blood pressure (BP) using photoplethysmography (PPG) is presented. The final aim of this paper was to develop ML methods for estimating blood pressure (BP) in a non-invasive way that is suitable in a telemedicine health-care monitoring context. The training of regression models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) was conducted using new extracted features from PPG signals processed using the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the interest was on the use of the most significant features obtained by the Minimum Redundancy Maximum Relevance (MRMR) selection algorithm to train eXtreme Gradient Boost (XGBoost) and Neural Network (NN) models. This aim was satisfactorily achieved by also comparing it with works in the literature; in fact, it was found that XGBoost models are more accurate than NN models in both systolic and diastolic blood pressure measurements, obtaining a Root Mean Square Error (RMSE) for SBP and DBP, respectively, of 5.67 mmHg and 3.95 mmHg. For SBP measurement, this result is an improvement compared to that reported in the literature. Furthermore, the trained XGBoost regression model fulfills the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) as well as grade A of the British Hypertension Society (BHS) standard.
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Affiliation(s)
| | | | | | - Anna Maria Lucia Lanzolla
- Department of Electrical and Information Engineering, Polytechnic University of Bari, 70125 Bari, Italy; (F.A.); (V.I.D.); (L.D.P.); (A.D.N.)
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Pankaj, Kumar A, Komaragiri R, Kumar M. A novel CS-NET architecture based on the unification of CNN, SVM and super-resolution spectrogram to monitor and classify blood pressure using photoplethysmography. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107716. [PMID: 37542944 DOI: 10.1016/j.cmpb.2023.107716] [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: 02/06/2023] [Revised: 06/20/2023] [Accepted: 07/07/2023] [Indexed: 08/07/2023]
Abstract
CONTEXT Continuous blood pressure (BP) monitoring plays an important role while treating various cardiovascular diseases and hypertension. A high correlation between arterial blood pressure (ABP) and Photoplethysmogram (PPG) signal enables using a PPG signal to monitor and classify BP continuously. Control of BP in realtime is the basis for the prevention of hypertension. PROPOSED APPROACH This work proposes a CS-NET architecture by unifying CNN and SVM approaches to classify BP using PPG signals. The main objective of the CS-NET method is to establish an accurate and reliable algorithm for the ABP classification. METHODOLOGY ABP signals are labeled normal and abnormal using the hypertension criteria the American College of Cardiology (ACC)/American Heart Association (AHA) laid down. The proposed CS-NET model incorporates three critical steps in three successive stages. The first stage includes converting a preprocessed PPG signal into a time-frequency (TF) representation called a super-resolution spectrogram by superlet transform. The second stage uses a convolutional neural network (CNN) model with several hidden layers to extract morphological features from every PPG super-resolution spectrogram. The third stage uses a support vector machine (SVM) classifier to classify the PPG signal. RESULTS PPG signals are used to train and test the proposed model. The performance of the proposed CS-NET method is tested using MIMIC-II, MIMIC-III, and PPG-BP-figshare database in terms of accuracy and F1 score. Moreover, the CS-NET method achieves better results with high accuracy when compared with other benchmark approaches that require an electrocardiogram signal for reference. CONCLUSIONS The proposed model achieved an aggregate classification accuracy of 98.21% across a five-fold cross-validation technique, making it a reliable approach for BP classification in clinical settings and realtime monitoring.
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Affiliation(s)
- Pankaj
- Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India
| | - Ashish Kumar
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India
| | - Rama Komaragiri
- Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India
| | - Manjeet Kumar
- Department of Electronics and Communication Engineering, Delhi Technological University, Delhi, India.
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Yilmaz G, Lyu X, Ong JL, Ling LH, Penzel T, Yeo BTT, Chee MWL. Nocturnal Blood Pressure Estimation from Sleep Plethysmography Using Machine Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:7931. [PMID: 37765988 PMCID: PMC10537552 DOI: 10.3390/s23187931] [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: 08/27/2023] [Revised: 09/11/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023]
Abstract
BACKGROUND Elevated nocturnal blood pressure (BP) is a risk factor for cardiovascular disease (CVD) and mortality. Cuffless BP assessment aided by machine learning could be a desirable alternative to traditional cuff-based methods for monitoring BP during sleep. We describe a machine-learning-based algorithm for predicting nocturnal BP using single-channel fingertip plethysmography (PPG) in healthy adults. METHODS Sixty-eight healthy adults with no apparent sleep or CVD (53% male), with a median (IQR) age of 29 (23-46 years), underwent overnight polysomnography (PSG) with fingertip PPG and ambulatory blood pressure monitoring (ABPM). Features based on pulse morphology were extracted from the PPG waveforms. Random forest models were used to predict night-time systolic blood pressure (SBP) and diastolic blood pressure (DBP). RESULTS Our model achieved the highest out-of-sample performance with a window length of 7 s across window lengths explored (60 s, 30 s, 15 s, 7 s, and 3 s). The mean absolute error (MAE ± STD) was 5.72 ± 4.51 mmHg for SBP and 4.52 ± 3.60 mmHg for DBP. Similarly, the root mean square error (RMSE ± STD) was 6.47 ± 1.88 mmHg for SBP and 4.62 ± 1.17 mmHg for DBP. The mean correlation coefficient between measured and predicted values was 0.87 for SBP and 0.86 for DBP. Based on Shapley additive explanation (SHAP) values, the most important PPG waveform feature was the stiffness index, a marker that reflects the change in arterial stiffness. CONCLUSION Our results highlight the potential of machine learning-based nocturnal BP prediction using single-channel fingertip PPG in healthy adults. The accuracy of the predictions demonstrated that our cuffless method was able to capture the dynamic and complex relationship between PPG waveform characteristics and BP during sleep, which may provide a scalable, convenient, economical, and non-invasive means to continuously monitor blood pressure.
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Affiliation(s)
- Gizem Yilmaz
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore; (G.Y.); (X.L.); (J.L.O.)
| | - Xingyu Lyu
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore; (G.Y.); (X.L.); (J.L.O.)
- Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore
| | - Ju Lynn Ong
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore; (G.Y.); (X.L.); (J.L.O.)
| | - Lieng Hsi Ling
- Department of Cardiology, National University Heart Centre Singapore, Singapore 119074, Singapore;
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore
| | - Thomas Penzel
- Interdisciplinary Center of Sleep Medicine, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany;
| | - B. T. Thomas Yeo
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore; (G.Y.); (X.L.); (J.L.O.)
- Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117549, Singapore
- N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore 117549, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore 117549, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02114, USA
| | - Michael W. L. Chee
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore; (G.Y.); (X.L.); (J.L.O.)
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Hayashi K, Maeda Y, Yoshimura T, Huang M, Tamura T. Estimating Blood Pressure during Exercise with a Cuffless Sphygmomanometer. SENSORS (BASEL, SWITZERLAND) 2023; 23:7399. [PMID: 37687854 PMCID: PMC10490341 DOI: 10.3390/s23177399] [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: 07/17/2023] [Revised: 08/05/2023] [Accepted: 08/23/2023] [Indexed: 09/10/2023]
Abstract
Accurately measuring blood pressure (BP) is essential for maintaining physiological health, which is commonly achieved using cuff-based sphygmomanometers. Several attempts have been made to develop cuffless sphygmomanometers. To increase their accuracy and long-term variability, machine learning methods can be applied for analyzing photoplethysmogram (PPG) signals. Here, we propose a method to estimate the BP during exercise using a cuffless device. The BP estimation process involved preprocessing signals, feature extraction, and machine learning techniques. To ensure the reliability of the signals extracted from the PPG, we employed the skewness signal quality index and the RReliefF algorithm for signal selection. Thereafter, the BP was estimated using the long short-term memory (LSTM)-based neural network. Seventeen young adult males participated in the experiments, undergoing a structured protocol composed of rest, exercise, and recovery for 20 min. Compared to the BP measured using a non-invasive voltage clamp-type continuous sphygmomanometer, that estimated by the proposed method exhibited a mean error of 0.32 ± 7.76 mmHg, which is equivalent to the accuracy of a cuff-based sphygmomanometer per regulatory standards. By enhancing patient comfort and improving healthcare outcomes, the proposed approach can revolutionize BP monitoring in various settings, including clinical, home, and sports environments.
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Affiliation(s)
- Kenta Hayashi
- Institute of Systems and Information Engineering, University of Tsukuba, Tsukuba 305-8577, Japan;
| | - Yuka Maeda
- Institute of Systems and Information Engineering, University of Tsukuba, Tsukuba 305-8577, Japan;
| | - Takumi Yoshimura
- Department of Medical and Welfare Engineering, Tokyo Metropolitan College of Industrial Technology, Tokyo 116-8523, Japan;
| | - Ming Huang
- School of Data Science, Nagoya City University, Nagoya 467-8501, Japan;
| | - Toshiyo Tamura
- Future Robotics Organization, Waseda University, Tokyo 169-8050, Japan;
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18
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Chu Y, Tang K, Hsu YC, Huang T, Wang D, Li W, Savitz SI, Jiang X, Shams S. Non-invasive arterial blood pressure measurement and SpO 2 estimation using PPG signal: a deep learning framework. BMC Med Inform Decis Mak 2023; 23:131. [PMID: 37480040 PMCID: PMC10362790 DOI: 10.1186/s12911-023-02215-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 06/22/2023] [Indexed: 07/23/2023] Open
Abstract
BACKGROUND Monitoring blood pressure and peripheral capillary oxygen saturation plays a crucial role in healthcare management for patients with chronic diseases, especially hypertension and vascular disease. However, current blood pressure measurement methods have intrinsic limitations; for instance, arterial blood pressure is measured by inserting a catheter in the artery causing discomfort and infection. METHOD Photoplethysmogram (PPG) signals can be collected via non-invasive devices, and therefore have stimulated researchers' interest in exploring blood pressure estimation using machine learning and PPG signals as a non-invasive alternative. In this paper, we propose a Transformer-based deep learning architecture that utilizes PPG signals to conduct a personalized estimation of arterial systolic blood pressure, arterial diastolic blood pressure, and oxygen saturation. RESULTS The proposed method was evaluated with a subset of 1,732 subjects from the publicly available ICU dataset MIMIC III. The mean absolute error is 2.52 ± 2.43 mmHg for systolic blood pressure, 1.37 ± 1.89 mmHg for diastolic blood pressure, and 0.58 ± 0.79% for oxygen saturation, which satisfies the requirements of the Association of Advancement of Medical Instrumentation standard and achieve grades A for the British Hypertension Society standard. CONCLUSIONS The results indicate that our model meets clinical standards and could potentially boost the accuracy of blood pressure and oxygen saturation measurement to deliver high-quality healthcare.
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Affiliation(s)
- Yan Chu
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Kaichen Tang
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yu-Chun Hsu
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Tongtong Huang
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Dulin Wang
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Wentao Li
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Sean I Savitz
- Institute for Stroke and Cerebrovascular Disease, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xiaoqian Jiang
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
- Institute for Stroke and Cerebrovascular Disease, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Shayan Shams
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
- Institute for Stroke and Cerebrovascular Disease, University of Texas Health Science Center at Houston, Houston, TX, USA.
- Department of Applied Data Science, San Jose State University, One Washington Sq, San Jose, CA, 95192, USA.
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Noninvasive continuous blood pressure estimation with fewer parameters based on RA-ReliefF feature selection and MPGA-BPN models. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
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20
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Faris Ali N, Atef M. An efficient hybrid LSTM-ANN joint classification-regression model for PPG based blood pressure monitoring. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
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Xuan Y, Barry C, De Souza J, Wen JH, Antipa N, Moore AA, Wang EJ. Ultra-low-cost mechanical smartphone attachment for no-calibration blood pressure measurement. Sci Rep 2023; 13:8105. [PMID: 37248245 DOI: 10.1038/s41598-023-34431-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/29/2023] [Indexed: 05/31/2023] Open
Abstract
We propose an ultra-low-cost at-home blood pressure monitor that leverages a plastic clip with a spring-loaded mechanism to enable a smartphone with a flash LED and camera to measure blood pressure. Our system, called BPClip, is based on the scientific premise of measuring oscillometry at the fingertip to measure blood pressure. To enable a smartphone to measure the pressure applied to the digital artery, a moveable pinhole projection moves closer to the camera as the user presses down on the clip with increased force. As a user presses on the device with increased force, the spring-loaded mechanism compresses. The size of the pinhole thus encodes the pressure applied to the finger. In conjunction, the brightness fluctuation of the pinhole projection correlates to the arterial pulse amplitude. By capturing the size and brightness of the pinhole projection with the built-in camera, the smartphone can measure a user's blood pressure with only a low-cost, plastic clip and an app. Unlike prior approaches, this system does not require a blood pressure cuff measurement for a user-specific calibration compared to pulse transit time and pulse wave analysis based blood pressure monitoring solutions. Our solution also does not require specialized smartphone models with custom sensors. Our early feasibility finding demonstrates that in a validation study with N = 29 participants with systolic blood pressures ranging from 88 to 157 mmHg, the BPClip system can achieve a mean absolute error of 8.72 and 5.49 for systolic and diastolic blood pressure, respectively. In an estimated cost projection study, we demonstrate that in small-batch manufacturing of 1000 units, the material cost is an estimated $0.80, suggesting that at full-scale production, our proposed BPClip concept can be produced at very low cost compared to existing cuff-based monitors for at-home blood pressure management.
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Affiliation(s)
- Yinan Xuan
- Electrical and Computer Engineering Department, UC San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA.
- The Design Lab, UC San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA.
| | - Colin Barry
- Electrical and Computer Engineering Department, UC San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA
- The Design Lab, UC San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA
| | - Jessica De Souza
- Electrical and Computer Engineering Department, UC San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA
- The Design Lab, UC San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA
| | - Jessica H Wen
- Department of Medicine, UC San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA
| | - Nick Antipa
- Electrical and Computer Engineering Department, UC San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA
| | - Alison A Moore
- Department of Medicine, UC San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA
- The Design Lab, UC San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA
| | - Edward J Wang
- Electrical and Computer Engineering Department, UC San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA.
- The Design Lab, UC San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA.
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Zhao L, Liang C, Huang Y, Zhou G, Xiao Y, Ji N, Zhang YT, Zhao N. Emerging sensing and modeling technologies for wearable and cuffless blood pressure monitoring. NPJ Digit Med 2023; 6:93. [PMID: 37217650 DOI: 10.1038/s41746-023-00835-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 05/05/2023] [Indexed: 05/24/2023] Open
Abstract
Cardiovascular diseases (CVDs) are a leading cause of death worldwide. For early diagnosis, intervention and management of CVDs, it is highly desirable to frequently monitor blood pressure (BP), a vital sign closely related to CVDs, during people's daily life, including sleep time. Towards this end, wearable and cuffless BP extraction methods have been extensively researched in recent years as part of the mobile healthcare initiative. This review focuses on the enabling technologies for wearable and cuffless BP monitoring platforms, covering both the emerging flexible sensor designs and BP extraction algorithms. Based on the signal type, the sensing devices are classified into electrical, optical, and mechanical sensors, and the state-of-the-art material choices, fabrication methods, and performances of each type of sensor are briefly reviewed. In the model part of the review, contemporary algorithmic BP estimation methods for beat-to-beat BP measurements and continuous BP waveform extraction are introduced. Mainstream approaches, such as pulse transit time-based analytical models and machine learning methods, are compared in terms of their input modalities, features, implementation algorithms, and performances. The review sheds light on the interdisciplinary research opportunities to combine the latest innovations in the sensor and signal processing research fields to achieve a new generation of cuffless BP measurement devices with improved wearability, reliability, and accuracy.
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Affiliation(s)
- Lei Zhao
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Cunman Liang
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Yan Huang
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Guodong Zhou
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Yiqun Xiao
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Nan Ji
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Yuan-Ting Zhang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Ni Zhao
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China.
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China.
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23
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du Toit C, Tran TQB, Deo N, Aryal S, Lip S, Sykes R, Manandhar I, Sionakidis A, Stevenson L, Pattnaik H, Alsanosi S, Kassi M, Le N, Rostron M, Nichol S, Aman A, Nawaz F, Mehta D, Tummala R, McCallum L, Reddy S, Visweswaran S, Kashyap R, Joe B, Padmanabhan S. Survey and Evaluation of Hypertension Machine Learning Research. J Am Heart Assoc 2023; 12:e027896. [PMID: 37119074 PMCID: PMC10227215 DOI: 10.1161/jaha.122.027896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 03/27/2023] [Indexed: 04/30/2023]
Abstract
Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision-making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly. In this study, we aimed to survey hypertension research using ML, evaluate the reporting quality, and identify barriers to ML's potential to transform hypertension care. Methods and Results The Harmonious Understanding of Machine Learning Analytics Network survey questionnaire was applied to 63 hypertension-related ML research articles published between January 2019 and September 2021. The most common research topics were blood pressure prediction (38%), hypertension (22%), cardiovascular outcomes (6%), blood pressure variability (5%), treatment response (5%), and real-time blood pressure estimation (5%). The reporting quality of the articles was variable. Only 46% of articles described the study population or derivation cohort. Most articles (81%) reported at least 1 performance measure, but only 40% presented any measures of calibration. Compliance with ethics, patient privacy, and data security regulations were mentioned in 30 (48%) of the articles. Only 14% used geographically or temporally distinct validation data sets. Algorithmic bias was not addressed in any of the articles, with only 6 of them acknowledging risk of bias. Conclusions Recent ML research on hypertension is limited to exploratory research and has significant shortcomings in reporting quality, model validation, and algorithmic bias. Our analysis identifies areas for improvement that will help pave the way for the realization of the potential of ML in hypertension and facilitate its adoption.
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Affiliation(s)
- Clea du Toit
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Tran Quoc Bao Tran
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Neha Deo
- Mayo Clinic Alix School of MedicineRochesterMN
| | - Sachin Aryal
- Center for Hypertension and Precision Medicine, Department of Physiology and PharmacologyUniversity of Toledo College of Medicine and Life SciencesToledoOH
| | - Stefanie Lip
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Robert Sykes
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Ishan Manandhar
- Center for Hypertension and Precision Medicine, Department of Physiology and PharmacologyUniversity of Toledo College of Medicine and Life SciencesToledoOH
| | | | - Leah Stevenson
- Center for Hypertension and Precision Medicine, Department of Physiology and PharmacologyUniversity of Toledo College of Medicine and Life SciencesToledoOH
| | | | - Safaa Alsanosi
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
- Department of Pharmacology and Toxicology, Faculty of MedicineUmm Al Qura UniversityMakkahSaudi Arabia
| | - Maria Kassi
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Ngoc Le
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Maggie Rostron
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Sarah Nichol
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Alisha Aman
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Faisal Nawaz
- College of MedicineMohammed Bin Rashid University of Medicine and Health SciencesDubaiUAE
| | - Dhruven Mehta
- Department of Internal MedicineTriStar Centennial Medical Center, HCA HealthcareNashvilleTN
| | - Ramakumar Tummala
- Center for Hypertension and Precision Medicine, Department of Physiology and PharmacologyUniversity of Toledo College of Medicine and Life SciencesToledoOH
| | - Linsay McCallum
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | | | - Shyam Visweswaran
- Department of Biomedical InformaticsUniversity of PittsburghPittsburghPA
| | - Rahul Kashyap
- Department of Anesthesiology and Critical Care MedicineMayo ClinicRochesterMN
| | - Bina Joe
- Center for Hypertension and Precision Medicine, Department of Physiology and PharmacologyUniversity of Toledo College of Medicine and Life SciencesToledoOH
| | - Sandosh Padmanabhan
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
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Garrett A, Kim B, Sie EJ, Gurel NZ, Marsili F, Boas DA, Roblyer D. Simultaneous photoplethysmography and blood flow measurements towards the estimation of blood pressure using speckle contrast optical spectroscopy. BIOMEDICAL OPTICS EXPRESS 2023; 14:1594-1607. [PMID: 37078049 PMCID: PMC10110303 DOI: 10.1364/boe.482740] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 03/10/2023] [Accepted: 03/12/2023] [Indexed: 05/03/2023]
Abstract
Non-invasive continuous blood pressure monitoring remains elusive. There has been extensive research using the photoplethysmographic (PPG) waveform for blood pressure estimation, but improvements in accuracy are still needed before clinical use. Here we explored the use of an emerging technique, speckle contrast optical spectroscopy (SCOS), for blood pressure estimation. SCOS provides measurements of both blood volume changes (PPG) and blood flow index (BFi) changes during the cardiac cycle, and thus provides a richer set of parameters compared to traditional PPG. SCOS measurements were taken on the finger and wrists of 13 subjects. We investigated the correlations between features extracted from both the PPG and BFi waveforms with blood pressure. Features from the BFi waveforms were more significantly correlated with blood pressure than PPG features ( R = - 0.55, p = 1.1 × 10-4 for the top BFi feature versus R = - 0.53, p = 8.4 × 10-4 for the top PPG feature). Importantly, we also found that features combining BFi and PPG data were highly correlated with changes in blood pressure ( R = - 0.59, p = 1.7 × 10-4 ). These results suggest that the incorporation of BFi measurements should be further explored as a means to improve blood pressure estimation using non-invasive optical techniques.
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Affiliation(s)
- Ariane Garrett
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Byungchan Kim
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Edbert J. Sie
- Reality Labs, Meta Platforms Inc., Menlo Park, CA 94025, USA
| | - Nil Z. Gurel
- Reality Labs, Meta Platforms Inc., Menlo Park, CA 94025, USA
| | | | - David A. Boas
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Darren Roblyer
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
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25
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Ma G, Zhang J, Liu J, Wang L, Yu Y. A Multi-Parameter Fusion Method for Cuffless Continuous Blood Pressure Estimation Based on Electrocardiogram and Photoplethysmogram. MICROMACHINES 2023; 14:804. [PMID: 37421037 DOI: 10.3390/mi14040804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 03/27/2023] [Accepted: 03/29/2023] [Indexed: 07/09/2023]
Abstract
Blood pressure (BP) is an essential physiological indicator to identify and determine health status. Compared with the isolated BP measurement conducted by traditional cuff approaches, cuffless BP monitoring can reflect the dynamic changes in BP values and is more helpful to evaluate the effectiveness of BP control. In this paper, we designed a wearable device for continuous physiological signal acquisition. Based on the collected electrocardiogram (ECG) and photoplethysmogram (PPG), we proposed a multi-parameter fusion method for noninvasive BP estimation. An amount of 25 features were extracted from processed waveforms and Gaussian copula mutual information (MI) was introduced to reduce feature redundancy. After feature selection, random forest (RF) was trained to realize systolic BP (SBP) and diastolic BP (DBP) estimation. Moreover, we used the records in public MIMIC-III as the training set and private data as the testing set to avoid data leakage. The mean absolute error (MAE) and standard deviation (STD) for SBP and DBP were reduced from 9.12 ± 9.83 mmHg and 8.31 ± 9.23 mmHg to 7.93 ± 9.12 mmHg and 7.63 ± 8.61 mmHg by feature selection. After calibration, the MAE was further reduced to 5.21 mmHg and 4.15 mmHg. The result showed that MI has great potential in feature selection during BP prediction and the proposed multi-parameter fusion method can be used for long-term BP monitoring.
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Affiliation(s)
- Gang Ma
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, Suzhou 215163, China
| | - Jie Zhang
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, Suzhou 215163, China
| | - Jing Liu
- School of Electronics and Information Technology, Soochow University, Suzhou 215031, China
| | - Lirong Wang
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, Suzhou 215163, China
- School of Electronics and Information Technology, Soochow University, Suzhou 215031, China
| | - Yong Yu
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, Suzhou 215163, China
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26
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Nour M, Polat K, Şentürk Ü, Arıcan M. A Novel Cuffless Blood Pressure Prediction: Uncovering New Features and New Hybrid ML Models. Diagnostics (Basel) 2023; 13:diagnostics13071278. [PMID: 37046499 PMCID: PMC10093721 DOI: 10.3390/diagnostics13071278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/15/2023] [Accepted: 03/23/2023] [Indexed: 03/31/2023] Open
Abstract
This paper investigates new feature extraction and regression methods for predicting cuffless blood pressure from PPG signals. Cuffless blood pressure is a technology that measures blood pressure without needing a cuff. This technology can be used in various medical applications, including home health monitoring, clinical uses, and portable devices. The new feature extraction method involves extracting meaningful features (time and chaotic features) from the PPG signals in the prediction of systolic blood pressure (SBP) and diastolic blood pressure (DBP) values. These extracted features are then used as inputs to regression models, which are used to predict cuffless blood pressure. The regression model performances were evaluated using root mean squared error (RMSE), R2, mean square error (MSE), and the mean absolute error (MAE). The obtained RMSE was 4.277 for systolic blood pressure (SBP) values using the Matérn 5/2 Gaussian process regression model. The obtained RMSE was 2.303 for diastolic blood pressure (DBP) values using the rational quadratic Gaussian process regression model. The results of this study have shown that the proposed feature extraction and regression models can predict cuffless blood pressure with reasonable accuracy. This study provides a novel approach for predicting cuffless blood pressure and can be used to develop more accurate models in the future.
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Affiliation(s)
- Majid Nour
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Kemal Polat
- Department of Electrical and Electronics Engineering, Faculty of Engineering, Bolu Abant Izzet Baysal University, 14280 Bolu, Turkey
- Correspondence:
| | - Ümit Şentürk
- Department of Computer Engineering, Faculty of Engineering, Bolu Abant Izzet Baysal University, 14280 Bolu, Turkey
| | - Murat Arıcan
- Department of Electrical and Electronics Engineering, Faculty of Engineering, Bolu Abant Izzet Baysal University, 14280 Bolu, Turkey
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27
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Xuan Y, Barry C, De Souza J, Wen J, Antipa N, Moore A, Wang E. Ultra-low-cost Mechanical Smartphone Attachment for No-Calibration Blood Pressure Measurement. RESEARCH SQUARE 2023. [PMID: 36909577 PMCID: PMC10002832 DOI: 10.21203/rs.3.rs-2561946/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
We propose BPClip, a less than $ 1 USD blood pressure monitor that leverages a plastic clip with a spring-loaded mechanism to enable any smartphone with a flash LED and a camera to measure blood pressure. Unlike prior approaches, our system measured systolic, mean, and diastolic blood pressure using oscillometric measurements that avoid cumbersome per-user calibrations and does not require specialized smartphone models with custom sensors.
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28
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Nazmul Islam Shuzan M, Chowdhury ME, Bin Ibne Reaz M, Khandakar A, Fuad Abir F, Ahasan Atick Faisal M, Hamid Md Ali S, Bakar AAA, Hossain Chowdhury M, Mahbub ZB, Monir Uddin M, Alhatou M. Machine learning-based classification of healthy and impaired gaits using 3D-GRF signals. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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29
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Attivissimo F, De Palma L, Di Nisio A, Scarpetta M, Lanzolla AML. Photoplethysmography Signal Wavelet Enhancement and Novel Features Selection for Non-Invasive Cuff-Less Blood Pressure Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:2321. [PMID: 36850919 PMCID: PMC9960464 DOI: 10.3390/s23042321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 01/11/2023] [Accepted: 02/09/2023] [Indexed: 06/18/2023]
Abstract
In this paper, new features relevant to blood pressure (BP) estimation using photoplethysmography (PPG) are presented. A total of 195 features, including the proposed ones and those already known in the literature, have been calculated on a set composed of 50,000 pulses from 1080 different patients. Three feature selection methods, namely Correlation-based Feature Selection (CFS), RReliefF and Minimum Redundancy Maximum Relevance (MRMR), have then been applied to identify the most significant features for BP estimation. Some of these features have been extracted through a novel PPG signal enhancement method based on the use of the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the enhanced signal leads to a reliable identification of the characteristic points of the PPG signal (e.g., systolic, diastolic and dicrotic notch points) by simple means, obtaining results comparable with those from purposely defined algorithms. For systolic points, mean and std of errors computed as the difference between the locations obtained using a purposely defined already known algorithm and those using the MODWT enhancement are, respectively, 0.0097 s and 0.0202 s; for diastolic points they are, respectively, 0.0441 s and 0.0486 s; for dicrotic notch points they are 0.0458 s and 0.0896 s. Hence, this study leads to the selection of several new features from the MODWT enhanced signal on every single pulse extracted from PPG signals, in addition to features already known in the literature. These features can be employed to train machine learning (ML) models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) in a non-invasive way, which is suitable for telemedicine health-care monitoring.
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30
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Visco V, Izzo C, Mancusi C, Rispoli A, Tedeschi M, Virtuoso N, Giano A, Gioia R, Melfi A, Serio B, Rusciano MR, Di Pietro P, Bramanti A, Galasso G, D’Angelo G, Carrizzo A, Vecchione C, Ciccarelli M. Artificial Intelligence in Hypertension Management: An Ace up Your Sleeve. J Cardiovasc Dev Dis 2023; 10:jcdd10020074. [PMID: 36826570 PMCID: PMC9963880 DOI: 10.3390/jcdd10020074] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 02/05/2023] [Accepted: 02/07/2023] [Indexed: 02/11/2023] Open
Abstract
Arterial hypertension (AH) is a progressive issue that grows in importance with the increased average age of the world population. The potential role of artificial intelligence (AI) in its prevention and treatment is firmly recognized. Indeed, AI application allows personalized medicine and tailored treatment for each patient. Specifically, this article reviews the benefits of AI in AH management, pointing out diagnostic and therapeutic improvements without ignoring the limitations of this innovative scientific approach. Consequently, we conducted a detailed search on AI applications in AH: the articles (quantitative and qualitative) reviewed in this paper were obtained by searching journal databases such as PubMed and subject-specific professional websites, including Google Scholar. The search terms included artificial intelligence, artificial neural network, deep learning, machine learning, big data, arterial hypertension, blood pressure, blood pressure measurement, cardiovascular disease, and personalized medicine. Specifically, AI-based systems could help continuously monitor BP using wearable technologies; in particular, BP can be estimated from a photoplethysmograph (PPG) signal obtained from a smartphone or a smartwatch using DL. Furthermore, thanks to ML algorithms, it is possible to identify new hypertension genes for the early diagnosis of AH and the prevention of complications. Moreover, integrating AI with omics-based technologies will lead to the definition of the trajectory of the hypertensive patient and the use of the most appropriate drug. However, AI is not free from technical issues and biases, such as over/underfitting, the "black-box" nature of many ML algorithms, and patient data privacy. In conclusion, AI-based systems will change clinical practice for AH by identifying patient trajectories for new, personalized care plans and predicting patients' risks and necessary therapy adjustments due to changes in disease progression and/or therapy response.
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Affiliation(s)
- Valeria Visco
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Carmine Izzo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Costantino Mancusi
- Department of Advanced Biomedical Sciences, Federico II University of Naples, 80138 Naples, Italy
| | - Antonella Rispoli
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Michele Tedeschi
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Nicola Virtuoso
- Cardiology Unit, University Hospital “San Giovanni di Dio e Ruggi d’Aragona”, 84131 Salerno, Italy
| | - Angelo Giano
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Renato Gioia
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Americo Melfi
- Cardiology Unit, University Hospital “San Giovanni di Dio e Ruggi d’Aragona”, 84131 Salerno, Italy
| | - Bianca Serio
- Hematology and Transplant Center, University Hospital “San Giovanni di Dio e Ruggi d’Aragona”, 84131 Salerno, Italy
| | - Maria Rosaria Rusciano
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Paola Di Pietro
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Alessia Bramanti
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Gennaro Galasso
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Gianni D’Angelo
- Department of Computer Science, University of Salerno, 84084 Fisciano, Italy
| | - Albino Carrizzo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
- Vascular Physiopathology Unit, IRCCS Neuromed, 86077 Pozzilli, Italy
| | - Carmine Vecchione
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
- Vascular Physiopathology Unit, IRCCS Neuromed, 86077 Pozzilli, Italy
| | - Michele Ciccarelli
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
- Correspondence:
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Aguet C, Jorge J, Van Zaen J, Proença M, Bonnier G, Frossard P, Lemay M. Blood pressure monitoring during anesthesia induction using PPG morphology features and machine learning. PLoS One 2023; 18:e0279419. [PMID: 36735652 PMCID: PMC9897516 DOI: 10.1371/journal.pone.0279419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 12/06/2022] [Indexed: 02/04/2023] Open
Abstract
Blood pressure (BP) is a crucial biomarker giving valuable information regarding cardiovascular diseases but requires accurate continuous monitoring to maximize its value. In the effort of developing non-invasive, non-occlusive and continuous BP monitoring devices, photoplethysmography (PPG) has recently gained interest. Researchers have attempted to estimate BP based on the analysis of PPG waveform morphology, with promising results, yet often validated on a small number of subjects with moderate BP variations. This work presents an accurate BP estimator based on PPG morphology features. The method first uses a clinically-validated algorithm (oBPM®) to perform signal preprocessing and extraction of physiological features. A subset of features that best reflects BP changes is automatically identified by Lasso regression, and a feature relevance analysis is conducted. Three machine learning (ML) methods are then investigated to translate this subset of features into systolic BP (SBP) and diastolic BP (DBP) estimates; namely Lasso regression, support vector regression and Gaussian process regression. The accuracy of absolute BP estimates and trending ability are evaluated. Such an approach considerably improves the performance for SBP estimation over previous oBPM® technology, with a reduction in the standard deviation of the error of over 20%. Furthermore, rapid BP changes assessed by the PPG-based approach demonstrates concordance rate over 99% with the invasive reference. Altogether, the results confirm that PPG morphology features can be combined with ML methods to accurately track BP variations generated during anesthesia induction. They also reinforce the importance of adding a calibration measure to obtain an absolute BP estimate.
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Affiliation(s)
- Clémentine Aguet
- Signal Processing Group, Swiss Center for Electronics and Microtechnology (CSEM), Neuchâtel, Switzerland
- Signal Processing Laboratory (LTS4), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- * E-mail:
| | - João Jorge
- Signal Processing Group, Swiss Center for Electronics and Microtechnology (CSEM), Neuchâtel, Switzerland
| | - Jérôme Van Zaen
- Signal Processing Group, Swiss Center for Electronics and Microtechnology (CSEM), Neuchâtel, Switzerland
| | - Martin Proença
- Signal Processing Group, Swiss Center for Electronics and Microtechnology (CSEM), Neuchâtel, Switzerland
| | - Guillaume Bonnier
- Signal Processing Group, Swiss Center for Electronics and Microtechnology (CSEM), Neuchâtel, Switzerland
| | - Pascal Frossard
- Signal Processing Laboratory (LTS4), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Mathieu Lemay
- Signal Processing Group, Swiss Center for Electronics and Microtechnology (CSEM), Neuchâtel, Switzerland
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32
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Shuzan MNI, Chowdhury MH, Chowdhury MEH, Murugappan M, Hoque Bhuiyan E, Arslane Ayari M, Khandakar A. Machine Learning-Based Respiration Rate and Blood Oxygen Saturation Estimation Using Photoplethysmogram Signals. Bioengineering (Basel) 2023; 10:bioengineering10020167. [PMID: 36829661 PMCID: PMC9952751 DOI: 10.3390/bioengineering10020167] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/22/2023] [Accepted: 01/23/2023] [Indexed: 02/03/2023] Open
Abstract
The continuous monitoring of respiratory rate (RR) and oxygen saturation (SpO2) is crucial for patients with cardiac, pulmonary, and surgical conditions. RR and SpO2 are used to assess the effectiveness of lung medications and ventilator support. In recent studies, the use of a photoplethysmogram (PPG) has been recommended for evaluating RR and SpO2. This research presents a novel method of estimating RR and SpO2 using machine learning models that incorporate PPG signal features. A number of established methods are used to extract meaningful features from PPG. A feature selection approach was used to reduce the computational complexity and the possibility of overfitting. There were 19 models trained for both RR and SpO2 separately, from which the most appropriate regression model was selected. The Gaussian process regression model outperformed all the other models for both RR and SpO2 estimation. The mean absolute error (MAE) for RR was 0.89, while the root-mean-squared error (RMSE) was 1.41. For SpO2, the model had an RMSE of 0.98 and an MAE of 0.57. The proposed system is a state-of-the-art approach for estimating RR and SpO2 reliably from PPG. If RR and SpO2 can be consistently and effectively derived from the PPG signal, patients can monitor their RR and SpO2 at a cheaper cost and with less hassle.
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Affiliation(s)
- Md Nazmul Islam Shuzan
- Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Moajjem Hossain Chowdhury
- Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Muhammad E. H. Chowdhury
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
- Correspondence: (M.E.H.C.); or (M.M.)
| | - Murugappan Murugappan
- Intelligent Signal Processing (ISP) Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Block 4, Doha 13133, Kuwait
- Department of Electronics and Communication Engineering, School of Engineering, Vels Institute of Sciences, Technology and Advanced Studies, Chennai 600117, Tamil Nadu, India
- Center for Excellence for Unmanned Aerial Systems, Universiti Malaysia Perlis, Perlis 02600, Malaysia
- Correspondence: (M.E.H.C.); or (M.M.)
| | - Enamul Hoque Bhuiyan
- BioMedical Engineering and Imaging Institute (BMEII), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Mohamed Arslane Ayari
- Department of Civil and Architectural Engineering, Qatar University, Doha 2713, Qatar
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
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Wong MKF, Hei H, Lim SZ, Ng EYK. Applied machine learning for blood pressure estimation using a small, real-world electrocardiogram and photoplethysmogram dataset. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:975-997. [PMID: 36650798 DOI: 10.3934/mbe.2023045] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Applying machine learning techniques to electrocardiography and photoplethysmography signals and their multivariate-derived waveforms is an ongoing effort to estimate non-occlusive blood pressure. Unfortunately, real ambulatory electrocardiography and photoplethysmography waveforms are inevitably affected by motion and noise artifacts, so established machine learning architectures perform poorly when trained on data of the Multiparameter Intelligent Monitoring in Intensive Care II type, a publicly available ICU database. Our study addresses this problem by applying four well-established machine learning methods, i.e., random forest regression, support vector regression, Adaboost regression and artificial neural networks, to a small, self-sampled electrocardiography-photoplethysmography dataset (n = 54) to improve the robustness of machine learning to real-world BP estimates. We evaluated the performance using a selection of optimal feature morphologies of waveforms by using pulse arrival time, morphological and frequency photoplethysmography parameters and heart rate variability as characterization data. On the basis of the root mean square error and mean absolute error, our study showed that support vector regression gave the best performance for blood pressure estimation from noisy data, achieving an mean absolute error of 6.97 mmHg, which meets the level C criteria set by the British Hypertension Society. We demonstrate that ambulatory electrocardiography- photoplethysmography signals acquired by mobile discrete devices can be used to estimate blood pressure.
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Affiliation(s)
- Mark Kei Fong Wong
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore
| | - Hao Hei
- School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore
| | - Si Zhou Lim
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore
| | - Eddie Yin-Kwee Ng
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore
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Blood glucose prediction based on imagingphotoplethysmography in combination with Machine learning. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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35
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Pilz N, Patzak A, Bothe TL. Continuous cuffless and non-invasive measurement of arterial blood pressure—concepts and future perspectives. Blood Press 2022; 31:254-269. [DOI: 10.1080/08037051.2022.2128716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Affiliation(s)
- Niklas Pilz
- Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute for Translational Physiology, Berlin, Germany
| | - Andreas Patzak
- Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute for Translational Physiology, Berlin, Germany
| | - Tomas L. Bothe
- Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute for Translational Physiology, Berlin, Germany
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36
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Bibay Thakkar P, Talwekar RH. Estimation of human vital signs and analysis of heart attack risk using EDENN. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2022. [DOI: 10.1080/20479700.2022.2161150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Priyanka Bibay Thakkar
- Department of Electronics and Telecommunication Engineering, Rungta College of Engineering and Technology, Chhattisgarh Swami Vivekanand Technical University Bhilai, Bhilai, India
| | - R. H. Talwekar
- Department of Electronics and Telecommunication Engineering, Government Engineering College Raipur, Chhattisgarh Swami Vivekanand Technical University Bhilai, Bhilai, India
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Qin K, Huang W, Zhang T, Tang S. Machine learning and deep learning for blood pressure prediction: a methodological review from multiple perspectives. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10353-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Blood Pressure Prediction Using Ensemble Rules during Isometric Sustained Weight Test. J Cardiovasc Dev Dis 2022; 9:jcdd9120440. [PMID: 36547437 PMCID: PMC9781478 DOI: 10.3390/jcdd9120440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 11/07/2022] [Accepted: 11/25/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Predicting beat-to-beat blood pressure has several clinical applications. While most machine learning models focus on accuracy, it is necessary to build models that explain the relationships of hemodynamical parameters with blood pressure without sacrificing accuracy, especially during exercise. OBJECTIVE The aim of this study is to use the RuleFit model to measure the importance, interactions, and relationships among several parameters extracted from photoplethysmography (PPG) and electrocardiography (ECG) signals during a dynamic weight-bearing test (WBT) and to assess the accuracy and interpretability of the model results. METHODS RuleFit was applied to hemodynamical ECG and PPG parameters during rest and WBT in six healthy young subjects. The WBT involves holding a 500 g weight in the left hand for 2 min. Blood pressure is taken in the opposite arm before and during exercise thereof. RESULTS The root mean square error of the model residuals was 4.72 and 2.68 mmHg for systolic blood pressure and diastolic blood pressure, respectively, during rest and 4.59 and 4.01 mmHg, respectively, during the WBT. Furthermore, the blood pressure measurements appeared to be nonlinear, and interaction effects were observed. Moreover, blood pressure predictions based on PPG parameters showed a strong correlation with individual characteristics and responses to exercise. CONCLUSION The RuleFit model is an excellent tool to study interactions among variables for predicting blood pressure. Compared to other models, the RuleFit model showed superior performance. RuleFit can be used for predicting and interpreting relationships among predictors extracted from PPG and ECG signals.
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Fuadah YN, Lim KM. Classification of Blood Pressure Levels Based on Photoplethysmogram and Electrocardiogram Signals with a Concatenated Convolutional Neural Network. Diagnostics (Basel) 2022; 12:diagnostics12112886. [PMID: 36428946 PMCID: PMC9689744 DOI: 10.3390/diagnostics12112886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/04/2022] [Accepted: 11/16/2022] [Indexed: 11/23/2022] Open
Abstract
Hypertension is a severe public health issue worldwide that significantly increases the risk of cardiac vascular disease, stroke, brain hemorrhage, and renal dysfunction. Early screening of blood pressure (BP) levels is essential to prevent the dangerous complication associated with hypertension as the leading cause of death. Recent studies have focused on employing photoplethysmograms (PPG) with machine learning to classify BP levels. However, several studies claimed that electrocardiograms (ECG) also strongly correlate with blood pressure. Therefore, we proposed a concatenated convolutional neural network which integrated the features extracted from PPG and ECG signals. This study used the MIMIC III dataset, which provided PPG, ECG, and arterial blood pressure (ABP) signals. A total of 14,298 signal segments were obtained from 221 patients, which were divided into 9150 signals of train data, 2288 signals of validation data, and 2860 signals of test data. In the training process, five-fold cross-validation was applied to select the best model with the highest classification performance. The proposed concatenated CNN architecture using PPG and ECG obtained the highest test accuracy of 94.56-95.15% with a 95% confidence interval in classifying BP levels into hypotension, normotension, prehypertension, hypertension stage 1, and hypertension stage 2. The result shows that the proposed method is a promising solution to categorize BP levels effectively, assisting medical personnel in making a clinical diagnosis.
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Affiliation(s)
- Yunendah Nur Fuadah
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea
- School of Electrical Engineering, Telkom University, Bandung 40257, Indonesia
| | - Ki Moo Lim
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea
- Computational Medicine Lab, Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea
- Correspondence:
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Man PK, Cheung KL, Sangsiri N, Shek WJ, Wong KL, Chin JW, Chan TT, So RHY. Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring. Healthcare (Basel) 2022; 10:2113. [PMID: 36292560 PMCID: PMC9601911 DOI: 10.3390/healthcare10102113] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 09/26/2022] [Accepted: 10/02/2022] [Indexed: 11/04/2022] Open
Abstract
Blood pressure (BP) determines whether a person has hypertension and offers implications as to whether he or she could be affected by cardiovascular disease. Cuff-based sphygmomanometers have traditionally provided both accuracy and reliability, but they require bulky equipment and relevant skills to obtain precise measurements. BP measurement from photoplethysmography (PPG) signals has become a promising alternative for convenient and unobtrusive BP monitoring. Moreover, the recent developments in remote photoplethysmography (rPPG) algorithms have enabled new innovations for contactless BP measurement. This paper illustrates the evolution of BP measurement techniques from the biophysical theory, through the development of contact-based BP measurement from PPG signals, and to the modern innovations of contactless BP measurement from rPPG signals. We consolidate knowledge from a diverse background of academic research to highlight the importance of multi-feature analysis for improving measurement accuracy. We conclude with the ongoing challenges, opportunities, and possible future directions in this emerging field of research.
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Affiliation(s)
- Ping-Kwan Man
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China
| | - Kit-Leong Cheung
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Nawapon Sangsiri
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Wilfred Jin Shek
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China
- Department of Biomedical Sciences, King’s College London, London WC2R 2LS, UK
| | - Kwan-Long Wong
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Jing-Wei Chin
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Tsz-Tai Chan
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Richard Hau-Yue So
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
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Chowdhury MH, Shuzan MNI, Chowdhury MEH, Reaz MBI, Mahmud S, Al Emadi N, Ayari MA, Ali SHM, Bakar AAA, Rahman SM, Khandakar A. Lightweight End-to-End Deep Learning Solution for Estimating the Respiration Rate from Photoplethysmogram Signal. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 9:bioengineering9100558. [PMID: 36290527 PMCID: PMC9598342 DOI: 10.3390/bioengineering9100558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 09/28/2022] [Accepted: 09/29/2022] [Indexed: 11/05/2022]
Abstract
Respiratory ailments are a very serious health issue and can be life-threatening, especially for patients with COVID. Respiration rate (RR) is a very important vital health indicator for patients. Any abnormality in this metric indicates a deterioration in health. Hence, continuous monitoring of RR can act as an early indicator. Despite that, RR monitoring equipment is generally provided only to intensive care unit (ICU) patients. Recent studies have established the feasibility of using photoplethysmogram (PPG) signals to estimate RR. This paper proposes a deep-learning-based end-to-end solution for estimating RR directly from the PPG signal. The system was evaluated on two popular public datasets: VORTAL and BIDMC. A lightweight model, ConvMixer, outperformed all of the other deep neural networks. The model provided a root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficient (R) of 1.75 breaths per minute (bpm), 1.27 bpm, and 0.92, respectively, for VORTAL, while these metrics were 1.20 bpm, 0.77 bpm, and 0.92, respectively, for BIDMC. The authors also showed how fine-tuning a small subset could increase the performance of the model in the case of an out-of-distribution dataset. In the fine-tuning experiments, the models produced an average R of 0.81. Hence, this lightweight model can be deployed to mobile devices for real-time monitoring of patients.
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Affiliation(s)
- Moajjem Hossain Chowdhury
- Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Md Nazmul Islam Shuzan
- Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Muhammad E. H. Chowdhury
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
- Correspondence: (M.E.H.C.); (M.B.I.R.); (M.A.A.)
| | - Mamun Bin Ibne Reaz
- Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
- Correspondence: (M.E.H.C.); (M.B.I.R.); (M.A.A.)
| | - Sakib Mahmud
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Nasser Al Emadi
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Mohamed Arselene Ayari
- Department of Civil and Architectural Engineering, Qatar University, Doha 2713, Qatar
- Technology Innovation and Engineering Education Unit (TIEE), Qatar University, Doha 2713, Qatar
- Correspondence: (M.E.H.C.); (M.B.I.R.); (M.A.A.)
| | - Sawal Hamid Md Ali
- Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Ahmad Ashrif A. Bakar
- Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Syed Mahfuzur Rahman
- Department of Biomedical Engineering, Military Institute of Science and Technology, Mirpur Cantonment, Dhaka 1216, Bangladesh
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
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Shoaib M, Hussain T, Shah B, Ullah I, Shah SM, Ali F, Park SH. Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease. FRONTIERS IN PLANT SCIENCE 2022; 13:1031748. [PMID: 36275583 PMCID: PMC9585275 DOI: 10.3389/fpls.2022.1031748] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 09/15/2022] [Indexed: 05/27/2023]
Abstract
Plants contribute significantly to the global food supply. Various Plant diseases can result in production losses, which can be avoided by maintaining vigilance. However, manually monitoring plant diseases by agriculture experts and botanists is time-consuming, challenging and error-prone. To reduce the risk of disease severity, machine vision technology (i.e., artificial intelligence) can play a significant role. In the alternative method, the severity of the disease can be diminished through computer technologies and the cooperation of humans. These methods can also eliminate the disadvantages of manual observation. In this work, we proposed a solution to detect tomato plant disease using a deep leaning-based system utilizing the plant leaves image data. We utilized an architecture for deep learning based on a recently developed convolutional neural network that is trained over 18,161 segmented and non-segmented tomato leaf images-using a supervised learning approach to detect and recognize various tomato diseases using the Inception Net model in the research work. For the detection and segmentation of disease-affected regions, two state-of-the-art semantic segmentation models, i.e., U-Net and Modified U-Net, are utilized in this work. The plant leaf pixels are binary and classified by the model as Region of Interest (ROI) and background. There is also an examination of the presentation of binary arrangement (healthy and diseased leaves), six-level classification (healthy and other ailing leaf groups), and ten-level classification (healthy and other types of ailing leaves) models. The Modified U-net segmentation model outperforms the simple U-net segmentation model by 98.66 percent, 98.5 IoU score, and 98.73 percent on the dice. InceptionNet1 achieves 99.95% accuracy for binary classification problems and 99.12% for classifying six segmented class images; InceptionNet outperformed the Modified U-net model to achieve higher accuracy. The experimental results of our proposed method for classifying plant diseases demonstrate that it outperforms the methods currently available in the literature.
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Affiliation(s)
- Muhammad Shoaib
- Department of Computer Science, CECOS University of Information Technology (IT) and Emerging Sciences, Peshawar, Pakistan
| | - Tariq Hussain
- High Performance Computing and Networking Institute, National Research Council (ICAR-CNR), Naples, Italy
| | - Babar Shah
- College of Technological Innovation, Zayed University, Dubai, United Arab Emirates
| | - Ihsan Ullah
- Department of Robotics and Mechatronics Engineering, Daegu Gyeonbuk Institute of Science and Engineering (DGIST), Daegu, South Korea
| | - Sayyed Mudassar Shah
- Institute of Computer Science & Information Technology, The University of Agriculture Peshawar, Peshawar, Pakistan
| | - Farman Ali
- Department of Software, Sejong University, Seoul, South Korea
| | - Sang Hyun Park
- Department of Robotics and Mechatronics Engineering, Daegu Gyeonbuk Institute of Science and Engineering (DGIST), Daegu, South Korea
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43
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Abiri A, Chou EF, Qian C, Rinehart J, Khine M. Intra-beat biomarker for accurate continuous non-invasive blood pressure monitoring. Sci Rep 2022; 12:16772. [PMID: 36202815 PMCID: PMC9537243 DOI: 10.1038/s41598-022-19096-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 08/24/2022] [Indexed: 11/10/2022] Open
Abstract
Accurate continuous non-invasive blood pressure (CNIBP) monitoring is the holy grail of digital medicine but remains elusive largely due to significant drifts in signal and motion artifacts that necessitate frequent device recalibration. To address these challenges, we developed a unique approach by creating a novel intra-beat biomarker (Diastolic Transit Time, DTT) to achieve highly accurate blood pressure (BP) estimations. We demonstrated our approach’s superior performance, compared to other common signal processing techniques, in eliminating stochastic baseline wander, while maintaining signal integrity and measurement accuracy, even during significant hemodynamic changes. We applied this new algorithm to BP data collected using non-invasive sensors from a diverse cohort of high acuity patients and demonstrated that we could achieve close agreement with the gold standard invasive arterial line BP measurements, for up to 20 min without recalibration. We established our approach's generalizability by successfully applying it to pulse waveforms obtained from various sensors, including photoplethysmography and capacitive-based pressure sensors. Our algorithm also maintained signal integrity, enabling reliable assessments of BP variability. Moreover, our algorithm demonstrated tolerance to both low- and high-frequency motion artifacts during abrupt hand movements and prolonged periods of walking. Thus, our approach shows promise in constituting a necessary advance and can be applied to a wide range of wearable sensors for CNIBP monitoring in the ambulatory and inpatient settings.
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Affiliation(s)
- Arash Abiri
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA, 92697, USA
| | - En-Fan Chou
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA, 92697, USA
| | - Chengyang Qian
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA, 92697, USA
| | - Joseph Rinehart
- Department of Anesthesiology & Perioperative Care, University of California, Irvine Medical Center, Orange, CA, USA
| | - Michelle Khine
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA, 92697, USA.
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44
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Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals. BIOMED RESEARCH INTERNATIONAL 2022; 2022:8094351. [PMID: 36217389 PMCID: PMC9547685 DOI: 10.1155/2022/8094351] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 07/30/2022] [Indexed: 11/17/2022]
Abstract
Objective. To review the progress of research on photoplethysmography- (PPG-) based cuffless continuous blood pressure monitoring technologies and prospect the challenges that need to be addressed in the future. Methods. Using Web of Science and PubMed as search engines, the literature on cuffless continuous blood pressure studies using PPG signals in the recent five years were searched. Results. Based on the retrieved literature, this paper describes the available open datasets, commonly used signal preprocessing methods, and model evaluation criteria. Early researches employed multisite PPG signals to calculate pulse wave velocity or time and predicted blood pressure by a simple linear equation. Later, extensive researches were dedicated to mine the features of PPG signals related to blood pressure and regressed blood pressure by machine learning models. Most recently, many researches have emerged to experiment with complex deep learning models for blood pressure prediction with the raw PPG signal as input. Conclusion. This paper summarized the methods in the retrieved literature, provided insight into the artificial intelligence algorithms employed in the literature, and concluded with a discussion of the challenges and opportunities for the development of cuffless continuous blood pressure monitoring technologies.
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Fleischhauer V, Feldheiser A, Zaunseder S. Beat-to-Beat Blood Pressure Estimation by Photoplethysmography and Its Interpretation. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22187037. [PMID: 36146386 PMCID: PMC9506534 DOI: 10.3390/s22187037] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 08/29/2022] [Accepted: 09/13/2022] [Indexed: 05/28/2023]
Abstract
Blood pressure (BP) is among the most important vital signals. Estimation of absolute BP solely using photoplethysmography (PPG) has gained immense attention over the last years. Available works differ in terms of used features as well as classifiers and bear large differences in their results. This work aims to provide a machine learning method for absolute BP estimation, its interpretation using computational methods and its critical appraisal in face of the current literature. We used data from three different sources including 273 subjects and 259,986 single beats. We extracted multiple features from PPG signals and its derivatives. BP was estimated by xgboost regression. For interpretation we used Shapley additive values (SHAP). Absolute systolic BP estimation using a strict separation of subjects yielded a mean absolute error of 9.456mmHg and correlation of 0.730. The results markedly improve if data separation is changed (MAE: 6.366mmHg, r: 0.874). Interpretation by means of SHAP revealed four features from PPG, its derivation and its decomposition to be most relevant. The presented approach depicts a general way to interpret multivariate prediction algorithms and reveals certain features to be valuable for absolute BP estimation. Our work underlines the considerable impact of data selection and of training/testing separation, which must be considered in detail when algorithms are to be compared. In order to make our work traceable, we have made all methods available to the public.
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Affiliation(s)
- Vincent Fleischhauer
- Faculty of Information Technology, University of Applied Sciences and Arts Dortmund, 44139 Dortmund, Germany
- TU Dresden, Institute for Biomedical Engineering, 01069 Dresden, Germany
| | - Aarne Feldheiser
- Department of Anaesthesiology, Intensive Care Medicine and Pain Therapy, Evang. Kliniken Essen-Mitte, Huyssens-Stiftung/Knappschaft, 45136 Essen, Germany
- Department of Anaesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 13353 Berlin, Germany
| | - Sebastian Zaunseder
- Faculty of Information Technology, University of Applied Sciences and Arts Dortmund, 44139 Dortmund, Germany
- TU Dresden, Institute for Biomedical Engineering, 01069 Dresden, Germany
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Estimation of blood pressure waveform from facial video using a deep U-shaped network and the wavelet representation of imaging photoplethysmographic signals. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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47
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Hu Q, Wang D, Yang C. PPG-based blood pressure estimation can benefit from scalable multi-scale fusion neural networks and multi-task learning. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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48
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Ma G, Chen Y, Zhu W, Zheng L, Tang H, Yu Y, Wang L. Evaluating and Visualizing the Contribution of ECG Characteristic Waveforms for PPG-Based Blood Pressure Estimation. MICROMACHINES 2022; 13:1438. [PMID: 36144060 PMCID: PMC9502729 DOI: 10.3390/mi13091438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 08/27/2022] [Accepted: 08/27/2022] [Indexed: 06/16/2023]
Abstract
Non-invasive continuous blood pressure monitoring is of great significance for the preventing, diagnosing, and treating of cardiovascular diseases (CVDs). Studies have demonstrated that photoplethysmogram (PPG) and electrocardiogram (ECG) signals can effectively and continuously predict blood pressure (BP). However, most of the BP estimation models focus on the waveform features of the PPG signal, while the peak value of R-wave in ECG is only used as a time reference, and few references investigated the ECG waveforms. This paper aims to evaluate the influence of three characteristic waveforms in ECG on the improvement of BP estimation. PPG is the primary signal, and five input combinations are formed by adding ECG, P wave, QRS complex, T wave, and none. We employ five common convolutional neural networks (CNN) to validate the consistency of the contribution. Meanwhile, with the visualization of Gradient-weighted class activation mapping (Grad-CAM), we generate the heat maps and further visualize the distribution of CNN's attention to each waveform of PPG and ECG. The heat maps show that networks pay more attention to the QRS complex and T wave. In the comparison results, the QRS complex and T wave have more contribution to minimizing errors than P wave. By separately adding P wave, QRS complex, and T wave, the average MAE of these networks reaches 7.87 mmHg, 6.57 mmHg, and 6.21 mmHg for systolic blood pressure (SBP), and 4.27 mmHg, 3.65 mmHg, and 3.73 mmHg, respectively, for diastolic blood pressure (DBP). The results of the experiment show that QRS complex and T wave deserves more attention and feature extraction like PPG waveform features in the continuous BP estimation.
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Affiliation(s)
- Gang Ma
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, Suzhou 215163, China
| | - Yuhang Chen
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, Suzhou 215163, China
| | - Wenliang Zhu
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, Suzhou 215163, China
| | - Lesong Zheng
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Hui Tang
- School of Electronics and Information Technology, Soochow University, Suzhou 215006, China
| | - Yong Yu
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, Suzhou 215163, China
| | - Lirong Wang
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, Suzhou 215163, China
- School of Electronics and Information Technology, Soochow University, Suzhou 215006, China
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Liu Z, Zhou C, Wang H, He Y. Blood pressure monitoring techniques in the natural state of multi-scenes: A review. Front Med (Lausanne) 2022; 9:851172. [PMID: 36091712 PMCID: PMC9462511 DOI: 10.3389/fmed.2022.851172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 08/11/2022] [Indexed: 11/17/2022] Open
Abstract
Blood pressure is one of the basic physiological parameters of human physiology. Frequent and repeated measurement of blood pressure along with recording of environmental or other physiological parameters when measuring blood pressure may reveal important cardiovascular risk factors that can predict occurrence of cardiovascular events. Currently, wearable non-invasive blood pressure measurement technology has attracted much research attention. Several different technical routes have been proposed to solve the challenge between portability or continuity of measurement methods and medical level accuracy of measurement results. The accuracy of blood pressure measurement technology based on auscultation and oscillography has been clinically verified, while majority of other technical routes are being explored at laboratory or multi-center clinical demonstration stage. Normally, Blood pressure measurement based on oscillographic method outside the hospital can only be measured at intervals. There is a need to develop techniques for frequent and high-precision blood pressure measurement under natural conditions outside the hospital. In this paper, we discussed the current status of blood pressure measurement technology and development trends of blood pressure measurement technology in different scenarios. We focuses on the key technical challenges and the latest advances in the study of miniaturization devices based on oscillographic method at wrist and PTT related method at finger positions as well as technology processes. This study is of great significance to the application of high frequency blood pressure measurement technology.
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Affiliation(s)
- Ziyi Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Guangdong Transtek Medical Electronics Co., Ltd., Zhongshan, China
| | - Congcong Zhou
- Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Hongwei Wang
- Tongde Hospital of Zhejiang Province, Hangzhou, China
- *Correspondence: Hongwei Wang,
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Yong He,
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Nandi P, Rao M. A Novel CNN-LSTM Model Based Non-Invasive Cuff-Less Blood Pressure Estimation System. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:832-836. [PMID: 36086017 DOI: 10.1109/embc48229.2022.9871777] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
PPG (Photoplethysmography) and ECG (Electro-cardiogram) physiological signals have been known to have certain indicators for establishing blood pressure (BP) levels. Continuous monitoring of blood pressure (BP) is highly valuable for cardiovascular patients; however the existing non-invasive cuff-based blood pressure monitoring system is discreet and applies artificial pressure on patients' arms that is uncomfortable. The other invasive method is highly interventional in nature and is highly disturbing when the patient is recuperating in the hospital wards or elsewhere. A non-invasive cuff-less, non-disturbing, and continuous BP measurement system targeted toward surgical, clinical, and domestic usage are proposed in this work. A convolutional neural network (CNN) followed by a long short-term memory layer (LSTM) was designed and applied to ECG and PPG signals to present accurate systolic blood pressure (SBP), and diastolic blood pressure (DBP). For developing the CNN-LSTM layers, a novel and open-source dataset was compiled that consisted of PPG and ECG signals from 30 healthy participants and is made publicly available for further usage to the research community. The novel CNN-LSTM based cuff-less blood pressure evaluation system presented a mean absolute error (MAE) of 2.57 mmHg and 3.44 mmHg for SBP and DBP respectively with similar standard-deviation (SD) metrics. The characterized error metrics of the proposed system are the lowest to date when compared to other prior work. Clinical Relevance- A cuff-less BP estimation system allows patients to have easy access to blood pressure evaluation as well as aid in determining unsafe health ailments like hypertension. Ready access to such system will not only allow practitioners to continuously monitor BP in hospitals but also help patients to regularly monitor BP more frequently at their convenience.
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