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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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Mehta S, Kwatra N, Jain M, McDuff D. Examining the challenges of blood pressure estimation via photoplethysmogram. Sci Rep 2024; 14:18318. [PMID: 39112533 PMCID: PMC11306225 DOI: 10.1038/s41598-024-68862-1] [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: 06/18/2024] [Accepted: 07/29/2024] [Indexed: 08/10/2024] Open
Abstract
The use of observed wearable sensor data (e.g., photoplethysmograms [PPG]) to infer health measures (e.g., glucose level or blood pressure) is a very active area of research. Such technology can have a significant impact on health screening, chronic disease management and remote monitoring. A common approach is to collect sensor data and corresponding labels from a clinical grade device (e.g., blood pressure cuff) and train deep learning models to map one to the other. Although well intentioned, this approach often ignores a principled analysis of whether the input sensor data have enough information to predict the desired metric. We analyze the task of predicting blood pressure from PPG pulse wave analysis. Our review of the prior work reveals that many papers fall prey to data leakage and unrealistic constraints on the task and preprocessing steps. We propose a set of tools to help determine if the input signal in question (e.g., PPG) is indeed a good predictor of the desired label (e.g., blood pressure). Using our proposed tools, we found that blood pressure prediction using PPG has a high multi-valued mapping factor of 33.2% and low mutual information of 9.8%. In comparison, heart rate prediction using PPG, a well-established task, has a very low multi-valued mapping factor of 0.75% and high mutual information of 87.7%. We argue that these results provide a more realistic representation of the current progress toward the goal of wearable blood pressure measurement via PPG pulse wave analysis. For code, see our project page: https://github.com/lirus7/PPG-BP-Analysis.
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Kwon D, Mi Jung Y, Lee HC, Kyong Kim T, Kim K, Lee G, Kim D, Lee SB, Mi Lee S. Non-invasive prediction of massive transfusion during surgery using intraoperative hemodynamic monitoring data. J Biomed Inform 2024; 156:104680. [PMID: 38914411 DOI: 10.1016/j.jbi.2024.104680] [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: 03/21/2024] [Revised: 05/27/2024] [Accepted: 06/21/2024] [Indexed: 06/26/2024]
Abstract
OBJECTIVE Failure to receive prompt blood transfusion leads to severe complications if massive bleeding occurs during surgery. For the timely preparation of blood products, predicting the possibility of massive transfusion (MT) is essential to decrease morbidity and mortality. This study aimed to develop a model for predicting MT 10 min in advance using non-invasive bio-signal waveforms that change in real-time. METHODS In this retrospective study, we developed a deep learning-based algorithm (DLA) to predict intraoperative MT within 10 min. MT was defined as the transfusion of 3 or more units of red blood cells within an hour. The datasets consisted of 18,135 patients who underwent surgery at Seoul National University Hospital (SNUH) for model development and internal validation and 621 patients who underwent surgery at the Boramae Medical Center (BMC) for external validation. We constructed the DLA by using features extracted from plethysmography (collected at 500 Hz) and hematocrit measured during surgery. RESULTS Among 18,135 patients in SNUH and 621 patients in BMC, 265 patients (1.46%) and 14 patients (2.25%) received MT during surgery, respectively. The area under the receiver operating characteristic curve (AUROC) of DLA predicting intraoperative MT before 10 min was 0.962 (95% confidence interval [CI], 0.948-0.974) in internal validation and 0.922 (95% CI, 0.882-0.959) in external validation, respectively. CONCLUSION The DLA can successfully predict intraoperative MT using non-invasive bio-signal waveforms.
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Affiliation(s)
- Doyun Kwon
- Interdisciplinary Program of Medical Informatics, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Young Mi Jung
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Obstetrics and Gynecology, Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Hyung-Chul Lee
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
| | - Tae Kyong Kim
- Department of Anesthesiology and Pain Medicine, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Kwangsoo Kim
- Department of Transdisciplinary Medicine, Institute of Convergence Medicine with Innovative Technology, Seoul National University Hospital, Republic of Korea; Department of Medicine, College of Medicine, Seoul National University, Republic of Korea
| | - Garam Lee
- Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, United States.
| | - Seung-Bo Lee
- Department of Medical Informatics, Keimyung University School of Medicine, Daegu, Republic of Korea.
| | - Seung Mi Lee
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Obstetrics and Gynecology & Innovative Medical Technology Research Institute, Seoul National University Hospital, Republic of Korea; Medical Big Data Research Center & Institute of Reproductive Medicine and Population, Medical Research Center, Seoul National University, Republic of Korea.
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Kamanditya B, Fuadah YN, Mahardika T NQ, Lim KM. Continuous blood pressure prediction system using Conv-LSTM network on hybrid latent features of photoplethysmogram (PPG) and electrocardiogram (ECG) signals. Sci Rep 2024; 14:16450. [PMID: 39014018 PMCID: PMC11252121 DOI: 10.1038/s41598-024-66514-y] [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: 03/29/2024] [Accepted: 07/02/2024] [Indexed: 07/18/2024] Open
Abstract
Continuous blood pressure (BP) monitoring is essential for managing cardiovascular disease. However, existing devices often require expert handling, highlighting the need for alternative methods to simplify the process. Researchers have developed various methods using physiological signals to address this issue. Yet, many of these methods either fall short in accuracy according to the BHS, AAMI, and IEEE standards for BP measurement devices or suffer from low computational efficiency due to the complexity of their models. To solve this problem, we developed a BP prediction system that merges extracted features of PPG and ECG from two pulses of both signals using convolutional and LSTM layers, followed by incorporating the R-to-R interval durations as additional features for predicting systolic (SBP) and diastolic (DBP) blood pressure. Our findings indicate that the prediction accuracies for SBP and DBP were 5.306 ± 7.248 mmHg with a 0.877 correlation coefficient and 3.296 ± 4.764 mmHg with a 0.918 correlation coefficient, respectively. We found that our proposed model achieved a robust performance on the MIMIC III dataset with a minimum architectural design and high-level accuracy compared to existing methods. Thus, our method not only meets the passing category for BHS, AAMI, and IEEE guidelines but also stands out as the most rapidly accurate deep-learning-based BP measurement device currently available.
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Affiliation(s)
- Bharindra Kamanditya
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea
| | - Yunendah Nur Fuadah
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea
- Department of Electrical Engineering, Telkom University, Bandung, 40257, Indonesia
| | - Nurul Qashri Mahardika T
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea
| | - Ki Moo Lim
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea.
- Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, 39253, Republic of Korea.
- Meta Heart Inc., Gumi, 39253, Republic of Korea.
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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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Tian S, Wang L, Zhu R. A flexible multimodal pulse sensor for wearable continuous blood pressure monitoring. MATERIALS HORIZONS 2024; 11:2428-2437. [PMID: 38441176 DOI: 10.1039/d3mh01999c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2024]
Abstract
Monitoring of arterial blood pressure via cuffless pulse waveform measurement at the wrist has an important clinical value for the early diagnosis and prevention of cardiovascular disease. However, accurate measurement of the radial pulse waveform is challenging owing to its subtle, wideband, and preload-dependent variation characteristics. Evidence shows that uncertainties or variations of wearing pressure and skin temperature can cause artifact signals in wrist pulse measurements, thus degrading blood pressure estimate accuracy and hindering precise clinical diagnosis. Herein, we report a flexible multisensory pulse sensor utilizing natural piezo-thermic transduction of human skin in conjunction with thin-film thermistors for the accurately measuring radial artery pulse waves with high fidelity and good anti-artifact performance. The flexible pulse sensor achieved a wide pressure measuring range (228.2 kPa), low detection limit (4 Pa), good linearity (R2 = 0.999), low hysteresis (2.45%), fast response (88 ms), and good durability and stability, thereby enabling accurate pulse measurement with high fidelity. The pulse sensor also monolithically integrated the simultaneous detections of skin temperature and wearing pressure for resisting artifact effects in pulse measurements. Through the fusion of multiple features extracted from the pulse waveform, wearing pressure, skin temperature and user's personal physical characteristics using an efficient multilayer perceptron, blood pressure is accurately estimated and good generalizability is achieved.
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Affiliation(s)
- Shuo Tian
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China.
| | - Liangqi Wang
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China.
| | - Rong Zhu
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China.
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Romero-Ante JD, Chicharro-Luna E, Manrique-Córdoba J, Vicente-Samper JM, Gracia-Sánchez A, Sabater-Navarro JM. Validation of a New Ankle Brachial Index Measurement System Using Pulse Wave Velocity. BIOSENSORS 2024; 14:251. [PMID: 38785725 PMCID: PMC11117512 DOI: 10.3390/bios14050251] [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: 03/05/2024] [Revised: 05/10/2024] [Accepted: 05/14/2024] [Indexed: 05/25/2024]
Abstract
Peripheral artery disease (PAD) is a common circulatory disorder characterized by the accumulation of fats, cholesterol, and other substances in the arteries that restrict blood flow to the extremities, especially the legs. The ankle brachial index (ABI) is a highly reliable and valid non-invasive test for diagnosing PAD. However, the traditional method has limitations. These include the time required, the need for Doppler equipment, the training of clinical staff, and patient discomfort. PWV refers to the speed at which an arterial pressure wave propagates along the arteries, and this speed is conditioned by arterial elasticity and stiffness. To address these limitations, we have developed a system that uses electrocardiogram (ECG) and photoplethysmography (PPG) signals to calculate pulse wave velocity (PWV). We propose determining the ABI based on this calculation. Validation was performed on 22 diabetic patients, and the results demonstrate the accuracy of the system, maintaining a margin of ±0.1 compared with the traditional method. This confirms the correlation between PWV and ABI and positions this technique as a promising alternative to overcome some of the limitations of the conventional method.
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Affiliation(s)
- Juan David Romero-Ante
- Neuroengineering Biomedical Group, Medical Robotics Unit, Institute of Bioengineering, Miguel Hernández University of Elche, 03202 Elche, Spain
| | - Esther Chicharro-Luna
- Department of Behavioural Sciences and Health, Nursing Area, Faculty of Medicine, Miguel Hernández University of Elche, 03550 San Juan de Alicante, Spain
| | - Juliana Manrique-Córdoba
- Neuroengineering Biomedical Group, Medical Robotics Unit, Institute of Bioengineering, Miguel Hernández University of Elche, 03202 Elche, Spain
| | - José María Vicente-Samper
- Neuroengineering Biomedical Group, Medical Robotics Unit, Institute of Bioengineering, Miguel Hernández University of Elche, 03202 Elche, Spain
| | - Alba Gracia-Sánchez
- Department of Behavioural Sciences and Health, Nursing Area, Faculty of Medicine, Miguel Hernández University of Elche, 03550 San Juan de Alicante, Spain
| | - José María Sabater-Navarro
- Neuroengineering Biomedical Group, Medical Robotics Unit, Institute of Bioengineering, Miguel Hernández University of Elche, 03202 Elche, Spain
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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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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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Park JS, Hong KS. Robust blood pressure measurement from facial videos in diverse environments. Heliyon 2024; 10:e26007. [PMID: 38434043 PMCID: PMC10906170 DOI: 10.1016/j.heliyon.2024.e26007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 02/02/2024] [Accepted: 02/06/2024] [Indexed: 03/05/2024] Open
Abstract
Blood pressure (BP) management is important worldwide, and BP monitoring is a crucial aspect of maintaining good health. Traditional BP meter measures BP independently in various situations, such as at home or work, using a cuff to maintain a stable condition. However, these devices can causes a foreign body sensation and discomfort, and are not always practical for periodic monitoring. As a result, studies have been conducted on the use of photoplethysmography (PPG) for measuring BP. However, PPG also has limitations similar to those of traditional BP meters, as it requires the placement of sensors on two regions of the body (fingers or toes). To address this issue, researchers have conducted studies on non-contact methods for measuring BP using face and hand videos. These studies have utilized two cameras to measure PTT and have focused on internal environments, resulting in low accuracy of BP measurement in external environments. We proposes a method for robust BP measurement using pulse wave velocity (PWV) and PTT calculated from facial videos. PTT is estimated by measuring the phase difference between two different regions of interest (ROIs) and PWV is calculated using PTT and the actual distance between two ROIs. In addition, our proposed method extracts the pulse wave from the ROI to measure BP. The actual distance between the ROIs and PTT are estimated using the two extracted pulse waves, and BP is then measured using PWV and PTT. To evaluate the BP measurement performance, the BP calculated from both BP meters and facial videos (in indoor, outdoor, driving car, and flying drone environments) are compared. Our results reveal that the proposed method can robustly measure BP in diverse environments.
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Affiliation(s)
- Jin-soo Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si, 16419, Republic of Korea
| | - Kwang-seok Hong
- School of Electronic Electrical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si, 16419, Republic of Korea
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Wu BF, Chiu LW, Wu YC, Lai CC, Cheng HM, Chu PH. Contactless Blood Pressure Measurement Via Remote Photoplethysmography With Synthetic Data Generation Using Generative Adversarial Networks. IEEE J Biomed Health Inform 2024; 28:621-632. [PMID: 37037253 DOI: 10.1109/jbhi.2023.3265857] [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: 04/12/2023]
Abstract
Remote photoplethysmography (rPPG) has been used to measure vital signs such as heart rate, heart rate variability, blood pressure (BP), and blood oxygen. Recent studies adopt features developed with photoplethysmography (PPG) to achieve contactless BP measurement via rPPG. These features can be classified into two groups: time or phase differences from multiple signals, or waveform feature analysis from a single signal. Here we devise a solution to extract the time difference information from the rPPG signal captured at 30 FPS. We also propose a deep learning model architecture to estimate BP from the extracted features. To prevent overfitting and compensate for the lack of data, we leverage a multi-model design and generate synthetic data. We also use subject information related to BP to assist in model learning. For real-world usage, the subject information is replaced with values estimated from face images, with performance that is still better than the state-of-the-art. To our best knowledge, the improvements can be achieved because of: 1) the model selection with estimated subject information, 2) replacing the estimated subject information with the real one, 3) the InfoGAN assistance training (synthetic data generation), and 4) the time difference features as model input. To evaluate the performance of the proposed method, we conduct a series of experiments, including dynamic BP measurement for many single subjects and nighttime BP measurement with infrared lighting. Our approach reduces the MAE from 15.49 to 8.78 mmHg for systolic blood pressure (SBP) and 10.56 to 6.16 mmHg for diastolic blood pressure(DBP) on a self-constructed rPPG dataset. On the Taipei Veterans General Hospital(TVGH) dataset for nighttime applications, the MAE is reduced from 21.58 to 11.12 mmHg for SBP and 9.74 to 7.59 mmHg for DBP, with improvement ratios of 48.47% and 22.07% respectively.
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12
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Tang H, Ma G, Qiu L, Zheng L, Bao R, Liu J, Wang L. Blood Pressure Estimation Based on PPG and ECG Signals Using Knowledge Distillation. Cardiovasc Eng Technol 2024; 15:39-51. [PMID: 38191807 DOI: 10.1007/s13239-023-00695-x] [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: 01/20/2023] [Accepted: 10/31/2023] [Indexed: 01/10/2024]
Abstract
OBJECTIVE Easy access bio-signals are useful for alleviating the shortcomings and difficulties associated with cuff-based and invasive blood pressure (BP) measurement techniques. This study proposes a deep learning model, trained using knowledge distillation, based on photoplethysmographic (PPG) and electrocardiogram (ECG) signals to estimate systolic and diastolic blood pressures. METHODS The estimation model comprises convolutional layers followed by one bidirectional recurrent layer and attention layers. The training approach involves knowledge distillation, where a smaller model (student model) is trained by leveraging information from a larger model (teacher model). RESULTS The proposed multistage model was evaluated on 1205 subjects from Medical Information Mart for Intensive Care (MIMIC) III database using the Association for the Advancement of Medical Instrumentation (AAMI) and the standards of the British Hypertension Society (BHS). The results revealed that our model performance achieved grade A in estimating both systolic blood pressure (SBP) and diastolic blood pressure (DBP) and met the requirements of the AAMI standard. After training with knowledge distillation (KD), the model achieved a mean absolute error and standard deviation of 2.94 ± 5.61 mmHg for SBP and 2.02 ± 3.60 mmHg for DBP. CONCLUSION Our results demonstrate the benefits of the knowledge distillation training method in reducing the number of parameters and improving the predictive accuracy of the blood pressure regression model.
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Affiliation(s)
- Hui Tang
- School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, China
| | - 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
| | - Lishen Qiu
- 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
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, Suzhou, 215163, China
| | - Rui Bao
- School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, China
| | - Jing Liu
- School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, China
| | - Lirong Wang
- School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, China.
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, Suzhou, 215163, China.
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Kim J, Chang SA, Park SW. First-in-Human Study for Evaluating the Accuracy of Smart Ring Based Cuffless Blood Pressure Measurement. J Korean Med Sci 2024; 39:e18. [PMID: 38225785 PMCID: PMC10789523 DOI: 10.3346/jkms.2024.39.e18] [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: 08/04/2023] [Accepted: 10/17/2023] [Indexed: 01/17/2024] Open
Abstract
BACKGROUND Recently, a ring-type cuffless blood pressure (BP) measuring device has been developed. This study was a prospective, single arm, first-in-human pivotal trial to evaluate accuracy of BP measurement by the new device. METHODS The ring-type smart wearable monitoring device measures photoplethysmography signals from the proximal phalanx and transmits the data wirelessly to a connected smartphone. For the BP comparison, a cuff was worn on the arm to check the reference BP by auscultatory method, while the test device was worn on the finger of the opposite arm to measure BP simultaneously. Measurements were repeated for up to three sets each on the left and right arms. The primary outcome measure was mean difference and standard deviation of BP differences between the test device and the reference readings. RESULTS We obtained 526 sets of systolic BP (SBP) and 513 sets of diastolic BP (DBP) from 89 subjects, with ranges of 80 to 175 mmHg and 43 to 122 mmHg for SBP and DBP, respectively. In sample-wise comparison, the mean difference between the test device and the reference was 0.16 ± 5.90 mmHg (95% limits of agreement [LOA], -11.41, 11.72) in SBP and -0.07 ± 4.68 (95% LOA, -9.26, 9.10) in DBP. The test device showed a strong correlation with the reference for SBP (r = 0.94, P < 0.001) and DBP (r = 0.95, P < 0.001). There were consistent results in subject-wise comparison. CONCLUSION The new ring-type BP measuring device showed a good correlation for SBP and DBP with minimal bias compared with an auscultatory method.
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Affiliation(s)
- Jihoon Kim
- Division of Cardiology, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Sung-A Chang
- Division of Cardiology, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Seung Woo Park
- Division of Cardiology, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
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14
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Liu Z, Zhang Y, Zhou C. BiGRU-attention for Continuous blood pressure trends estimation through single channel PPG. Comput Biol Med 2024; 168:107795. [PMID: 38056206 DOI: 10.1016/j.compbiomed.2023.107795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/23/2023] [Accepted: 11/28/2023] [Indexed: 12/08/2023]
Abstract
BACKGROUND Physiological parameter monitoring based on photoplethysmography (PPG) detection has the advantage of fast, portable, and non-invasive. Changes in the morphology of the PPG waveform can reflect the effect of arterial elasticity changes on blood pressure (BP). However, machine learning models and non-recurrent neural network models typically ignore the time-dependency of continuous PPG data, leading to the decrease of accuracy or the increased calibration frequency. OBJECTIVE This paper proposes a BiGRU model with attention to estimate BP trends, which uses a single-channel PPG signal combined with demographic information to estimate continuous BP trends point-by-point and to discuss the impact of calibration cycle. METHODS This paper selects 15 typical subjects from two groups with/without cardiovascular disease (CVD) and evaluates the model performance. Regarding the calibration frequency problem, we set two modes of non-calibration and calibration to validate the results of blood pressure trends estimation. RESULTS In the calibration mode, the estimation errors (ME ± STD) of SBP for CVD/non-CVD groups are 0.91 ± 10.58 mmHg/0.17 ± 10.06 mmHg respectively, and DBP are 0.34 ± 5.28 mmHg/-0.19 ± 5.36 mmHg; in the non-calibration mode, the estimation errors of SBP for CVD/non-CVD groups are 0.27 ± 9.87 mmHg/-0.82 ± 9.92 mmHg respectively, and DBP are -0.63 ± 3.28 mmHg/0.80 ± 4.93 mmHg. CONCLUSIONS The results show that the proposed model has high accuracy in estimating BP levels, which is expected to achieve real-time, long-term continuous BP trends monitoring in wearable devices.
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Affiliation(s)
- Ziyi Liu
- Guangdong Transtek Medical Electronics Co., Ltd., Zhongshan, 52843, People's Republic of China
| | - Yiming Zhang
- Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Congcong Zhou
- Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China; Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, National Engineering Research Center for Innovation and Application of Minimally Invasive Devices, 3 East Qingchun Road, Hangzhou, 310016, Zhejiang Province, People's Republic of China.
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15
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Garcia-Molina G. Feasibility of Unobtrusively Estimating Blood Pressure Using Load Cells under the Legs of a Bed. SENSORS (BASEL, SWITZERLAND) 2023; 24:96. [PMID: 38202958 PMCID: PMC10780971 DOI: 10.3390/s24010096] [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: 11/03/2023] [Revised: 12/08/2023] [Accepted: 12/22/2023] [Indexed: 01/12/2024]
Abstract
The ability to monitor blood pressure unobtrusively and continuously, even during sleep, may promote the prevention of cardiovascular diseases, enable the early detection of cardiovascular risk, and facilitate the timely administration of treatment. Publicly available data from forty participants containing synchronously recorded signals from four force sensors (load cells located under each leg of a bed) and continuous blood pressure waveforms were leveraged in this research. The focus of this study was on using a deep neural network with load-cell data as input composed of three recurrent layers to reconstruct blood pressure (BP) waveforms. Systolic (SBP) and diastolic (DBP) blood pressure values were estimated from the reconstructed BP waveform. The dataset was partitioned into training, validation, and testing sets, such that the data from a given participant were only used in a single set. The BP waveform reconstruction performance resulted in an R2 of 0.61 and a mean absolute error < 0.1 mmHg. The estimation of the mean SBP and DBP values was characterized by Bland-Altman-derived limits of agreement in intervals of [-11.99 to 15.52 mmHg] and [-7.95 to +3.46 mmHg], respectively. These results may enable the detection of abnormally large or small variations in blood pressure, which indicate cardiovascular health degradation. The apparent contrast between the small reconstruction error and the limit-of-agreement width owes to the fact that reconstruction errors manifest more prominently at the maxima and minima, which are relevant for SBP and DBP estimation. While the focus here was on SBD and DBP estimation, reconstructing the entire BP waveform enables the calculation of additional hemodynamic parameters.
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Affiliation(s)
- Gary Garcia-Molina
- Sleep Number Labs, San Jose, CA 95113, USA; or
- Center for Sleep and Consciousness, Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA
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16
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Arrow C, Ward M, Eshraghian J, Dwivedi G. Capturing the pulse: a state-of-the-art review on camera-based jugular vein assessment. BIOMEDICAL OPTICS EXPRESS 2023; 14:6470-6492. [PMID: 38420308 PMCID: PMC10898581 DOI: 10.1364/boe.507418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 11/02/2023] [Accepted: 11/05/2023] [Indexed: 03/02/2024]
Abstract
Heart failure is associated with a rehospitalisation rate of up to 50% within six months. Elevated central venous pressure may serve as an early warning sign. While invasive procedures are used to measure central venous pressure for guiding treatment in hospital, this becomes impractical upon discharge. A non-invasive estimation technique exists, where the clinician visually inspects the pulsation of the jugular veins in the neck, but it is less reliable due to human limitations. Video and signal processing technologies may offer a high-fidelity alternative. This state-of-the-art review analyses existing literature on camera-based methods for jugular vein assessment. We summarize key design considerations and suggest avenues for future research. Our review highlights the neck as a rich imaging target beyond the jugular veins, capturing comprehensive cardiac signals, and outlines factors affecting signal quality and measurement accuracy. Addressing an often quoted limitation in the field, we also propose minimum reporting standards for future studies.
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Affiliation(s)
- Coen Arrow
- School of Medicine, University of Western Australia, Perth, Australia
- Advanced Clinical and Translational Cardiovascular Imaging, Harry Perkins Institute of Medical Research, University of Western Australia, Perth, Australia
| | - Max Ward
- Department of Computer Science and Software Engineering, University of Western Australia, Perth, Australia
| | - Jason Eshraghian
- Department of Electrical and Computer Engineering, University of California (Santa Cruz), California, USA
| | - Girish Dwivedi
- School of Medicine, University of Western Australia, Perth, Australia
- Advanced Clinical and Translational Cardiovascular Imaging, Harry Perkins Institute of Medical Research, University of Western Australia, Perth, Australia
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia
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17
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Pankaj, Kumar A, Komaragiri R, Kumar M. Blood pressure estimation and classification using a reference signal-less photoplethysmography signal: a deep learning framework. Phys Eng Sci Med 2023; 46:1589-1605. [PMID: 37747644 DOI: 10.1007/s13246-023-01322-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 08/21/2023] [Indexed: 09/26/2023]
Abstract
The markers that help to predict th function of a cardiovascular system are hemodynamic parameters like blood pressure (BP), stroke volume, heart rate, and cardiac output. Continuous analysis of hemodynamic parameters such as BP can detect abnormalities earlier, preventing cardiovascular diseases (CVDs). However, sometimes due to motion artifacts, it becomes difficult to monitor the BP accurately and classify it. This work presents an optimized deep learning model having the capability to estimate the systolic blood pressure (SBP) and diastolic blood pressure (DBP) and classify the BP stages simultaneously from the same network using only a single channel photoplethysmography (PPG) signal. The proposed model is designed by exploiting the deep learning framework of a convolutional neural network (CNN), exhibiting the inherent ability to extract features automatically. Moreover, the proposed framework utilizes the superlet transform method to transform a 1-D PPG signal into a 2-D super-resolution time-frequency (TF) spectrogram. A superlet transform separates the peaks related to true PPG signal components and motion artifacts components. Thus, the superlet provides a robust realtime approach to accurately estimating and classifying BP using a single PPG sensor signal and does not require additional ECG and PPG sensor signals for reference. Using a super-resolution spectrogram and CNN model makes the method profitable in motion artifact removal, feature selection, and extraction. Hence the proposed framework becomes less complex for deployment on wearable devices having limited battery resources. The performance of the proposed framework is demonstrated on the publicly available larger dataset MIMIC-III. This work obtained a mean absolute error (MAE) of 2.71 mmHg and 2.42 mmHg for SBP and DBP, respectively. The classification accuracy for the SBP prediction is about 96.79%, whereas it is 98.94% for DBP. From a motion artifact-affected PPG signal, SBP and DBP are estimated. Then the estimated BP is classified into three categories: normotension, prehypertension, and hypertension, and is compared with the state of art methods to show the effectiveness of the proposed optimized framework.
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Affiliation(s)
- Pankaj
- Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India
| | - Ashish Kumar
- Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India
- School of Computer science engineering and technology, Bennett University, Greater Noida, 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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18
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Xing X, Dong WF, Xiao R, Song M, Jiang C. Analysis of the Chaotic Component of Photoplethysmography and Its Association with Hemodynamic Parameters. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1582. [PMID: 38136462 PMCID: PMC10742563 DOI: 10.3390/e25121582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 12/24/2023]
Abstract
Wearable technologies face challenges due to signal instability, hindering their usage. Thus, it is crucial to comprehend the connection between dynamic patterns in photoplethysmography (PPG) signals and cardiovascular health. In our study, we collected 401 multimodal recordings from two public databases, evaluating hemodynamic conditions like blood pressure (BP), cardiac output (CO), vascular compliance (C), and peripheral resistance (R). Using irregular-resampling auto-spectral analysis (IRASA), we quantified chaotic components in PPG signals and employed different methods to measure the fractal dimension (FD) and entropy. Our findings revealed that in surgery patients, the power of chaotic components increased with vascular stiffness. As the intensity of CO fluctuations increased, there was a notable strengthening in the correlation between most complexity measures of PPG and these parameters. Interestingly, some conventional morphological features displayed a significant decrease in correlation, indicating a shift from a static to dynamic scenario. Healthy subjects exhibited a higher percentage of chaotic components, and the correlation between complexity measures and hemodynamics in this group tended to be more pronounced. Causal analysis showed that hemodynamic fluctuations are main influencers for FD changes, with observed feedback in most cases. In conclusion, understanding chaotic patterns in PPG signals is vital for assessing cardiovascular health, especially in individuals with unstable hemodynamics or during ambulatory testing. These insights can help overcome the challenges faced by wearable technologies and enhance their usage in real-world scenarios.
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Affiliation(s)
- Xiaoman Xing
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Sciences and Technology of China, Suzhou 215163, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Wen-Fei Dong
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Renjie Xiao
- Medical Health Information Center, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Mingxuan Song
- Suzhou GK Medtech Science and Technology Development (Group) Co., Ltd., Suzhou 215163, China
| | - Chenyu Jiang
- Jinan Guoke Medical Technology Development Co., Ltd., Jinan 250100, China
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19
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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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20
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Lin B, Tao J, Xu J, He L, Liu N, Zhang X. Estimation of vital signs from facial videos via video magnification and deep learning. iScience 2023; 26:107845. [PMID: 37790274 PMCID: PMC10542939 DOI: 10.1016/j.isci.2023.107845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/27/2023] [Accepted: 09/05/2023] [Indexed: 10/05/2023] Open
Abstract
The continuous monitoring of vital signs is one of the hottest topics in healthcare. Recent technological advances in sensors, signal processing, and image processing spawned the development of no-contact techniques such as remote photoplethysmography (rPPG). To solve the common problems of rPPG including weak extracted signals, body movements, and generalization with limited data resources, we proposed a dual-path estimation method based on video magnification and deep learning. First, image processes are applied to detect, track, and magnificate facial ROIs automatically. Then, the steady part of the wave of each processed ROI is used for the extraction of features including heart rate, PTT, and features of pulse wave waveform. The blood pressures are estimated from the features via a small CNN. Results comply with the current standard and promise potential clinical applications in the future.
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Affiliation(s)
- Bin Lin
- Key Laboratory of Opto-Electronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, Fujian 350117, China
| | - Jing Tao
- Key Laboratory of Opto-Electronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, Fujian 350117, China
| | - Jingjing Xu
- Key Laboratory of Opto-Electronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, Fujian 350117, China
| | - Liang He
- Key Laboratory of Opto-Electronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, Fujian 350117, China
| | - Nenrong Liu
- Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials, Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering, College of Physics and Energy, Fujian Normal University, Fuzhou, Fujian 350117, China
| | - Xianzeng Zhang
- Key Laboratory of Opto-Electronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, Fujian 350117, China
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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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22
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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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23
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Wang L, Tian S, Zhu R. A new method of continuous blood pressure monitoring using multichannel sensing signals on the wrist. MICROSYSTEMS & NANOENGINEERING 2023; 9:117. [PMID: 37744263 PMCID: PMC10511443 DOI: 10.1038/s41378-023-00590-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 07/06/2023] [Accepted: 07/31/2023] [Indexed: 09/26/2023]
Abstract
Hypertension is a worldwide health problem and a primary risk factor for cardiovascular disease. Continuous monitoring of blood pressure has important clinical value for the early diagnosis and prevention of cardiovascular disease. However, existing technologies for wearable continuous blood pressure monitoring are usually inaccurate, rely on subject-specific calibration and have poor generalization across individuals, which limit their practical applications. Here, we report a new blood pressure measurement method and develop an associated wearable device to implement continuous blood pressure monitoring for new subjects. The wearable device detects cardiac output and pulse waveform features through dual photoplethysmography (PPG) sensors worn on the palmar and dorsal sides of the wrist, incorporating custom-made interface sensors to detect the wearing contact pressure and skin temperature. The detected multichannel signals are fused using a machine-learning algorithm to estimate continuous blood pressure in real time. This dual PPG sensing method effectively eliminates the personal differences in PPG signals caused by different people and different wearing conditions. The proposed wearable device enables continuous blood pressure monitoring with good generalizability across individuals and demonstrates promising potential in personal health care applications.
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Affiliation(s)
- Liangqi Wang
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, 100084 Beijing, China
| | - Shuo Tian
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, 100084 Beijing, China
| | - Rong Zhu
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, 100084 Beijing, China
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24
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Zhou Y, Tan Z, Liu Y, Cheng H. Fully convolutional neural network and PPG signal for arterial blood pressure waveform estimation. Physiol Meas 2023; 44:075007. [PMID: 37402386 DOI: 10.1088/1361-6579/ace414] [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: 02/11/2023] [Accepted: 07/04/2023] [Indexed: 07/06/2023]
Abstract
Objective. The quality of the arterial blood pressure (ABP) waveform is crucial for predicting the value of blood pressure. The ABP waveform is predicted through experiments, and then Systolic blood pressure (SBP), Diastolic blood pressure, (DBP), and Mean arterial pressure (MAP) information are estimated from the ABP waveform.Approach. To ensure the quality of the predicted ABP waveform, this paper carefully designs the network structure, input signal, loss function, and structural parameters. A fully convolutional neural network (CNN) MultiResUNet3+ is used as the core architecture of ABP-MultiNet3+. In addition to performing Kalman filtering on the original photoplethysmogram (PPG) signal, its first-order derivative and second-order derivative signals are used as ABP-MultiNet3+ enter. The model's loss function uses a combination of mean absolute error (MAE) and means square error (MSE) loss to ensure that the predicted ABP waveform matches the reference waveform.Main results. The proposed ABP-MultiNet3+ model was tested on the public MIMIC II databases, MAE of MAP, DBP, and SBP was 1.88 mmHg, 3.11 mmHg, and 4.45 mmHg, respectively, indicating a small model error. It experiment fully meets the standards of the AAMI standard and obtains level A in the DBP and MAP prediction standard test under the BHS standard. For SBP prediction, it obtains level B in the BHS standard test. Although it does not reach level A, it has a certain improvement compared with the existing methods.Significance. The results show that this algorithm can achieve sleeveless blood pressure estimation, which may enable mobile medical devices to continuously monitor blood pressure and greatly reduce the harm caused by Cardiovascular disease (CVD).
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Affiliation(s)
- Yongan Zhou
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, 100044, People's Republic of China
| | - Zhi Tan
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, 100044, People's Republic of China
| | - Yuhong Liu
- College of Pulmonary & Critical Care Medicine, 8th Medical Center, Chinese PLA General Hospital, People's Republic of China
- Beijing IROT Key Laboratory, People's Republic of China
| | - Haibo Cheng
- Jiangsu Future Network Group Co., Ltd, People's Republic of China
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25
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Li J, Jia H, Zhou J, Huang X, Xu L, Jia S, Gao Z, Yao K, Li D, Zhang B, Liu Y, Huang Y, Hu Y, Zhao G, Xu Z, Li J, Yiu CK, Gao Y, Wu M, Jiao Y, Zhang Q, Tai X, Chan RH, Zhang Y, Ma X, Yu X. Thin, soft, wearable system for continuous wireless monitoring of artery blood pressure. Nat Commun 2023; 14:5009. [PMID: 37591881 PMCID: PMC10435523 DOI: 10.1038/s41467-023-40763-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 08/07/2023] [Indexed: 08/19/2023] Open
Abstract
Continuous monitoring of arterial blood pressure (BP) outside of a clinical setting is crucial for preventing and diagnosing hypertension related diseases. However, current continuous BP monitoring instruments suffer from either bulky systems or poor user-device interfacial performance, hampering their applications in continuous BP monitoring. Here, we report a thin, soft, miniaturized system (TSMS) that combines a conformal piezoelectric sensor array, an active pressure adaptation unit, a signal processing module, and an advanced machine learning method, to allow real wearable, continuous wireless monitoring of ambulatory artery BP. By optimizing the materials selection, control/sampling strategy, and system integration, the TSMS exhibits improved interfacial performance while maintaining Grade A level measurement accuracy. Initial trials on 87 volunteers and clinical tracking of two hypertension individuals prove the capability of the TSMS as a reliable BP measurement product, and its feasibility and practical usability in precise BP control and personalized diagnosis schemes development.
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Affiliation(s)
- Jian Li
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Huiling Jia
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Jingkun Zhou
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Xingcan Huang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Long Xu
- School of Mechanical and Aerospace Engineering, Jilin University, 130012, Changchun, China
| | - Shengxin Jia
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Zhan Gao
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Kuanming Yao
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Dengfeng Li
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Binbin Zhang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Yiming Liu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Ya Huang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Yue Hu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Guangyao Zhao
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Zitong Xu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Jiyu Li
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Chun Ki Yiu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Yuyu Gao
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Mengge Wu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China (UESTC), 610054, Chengdu, China
| | - Yanli Jiao
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Qiang Zhang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Xuecheng Tai
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
- Department of Mathematics, Hong Kong Baptist University, Hong Kong, China
| | - Raymond H Chan
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Yuanting Zhang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Xiaohui Ma
- Department of vascular and endovascular surgery, The first medical center of Chinese PLA General Hospital, 100853, Beijing, China.
| | - Xinge Yu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China.
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China.
- City University of Hong Kong Shenzhen Research Institute, 518057, Shenzhen, China.
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26
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Mahardika T NQ, Fuadah YN, Jeong DU, Lim KM. PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNN-LSTM. Diagnostics (Basel) 2023; 13:2566. [PMID: 37568929 PMCID: PMC10417316 DOI: 10.3390/diagnostics13152566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/24/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
Researchers commonly use continuous noninvasive blood-pressure measurement (cNIBP) based on photoplethysmography (PPG) signals to monitor blood pressure conveniently. However, the performance of the system still needs to be improved. Accuracy and precision in blood-pressure measurements are critical factors in diagnosing and managing patients' health conditions. Therefore, we propose a convolutional long short-term memory neural network (CNN-LSTM) with grid search ability, which provides a robust blood-pressure estimation system by extracting meaningful information from PPG signals and reducing the complexity of hyperparameter optimization in the proposed model. The multiparameter intelligent monitoring for intensive care III (MIMIC III) dataset obtained PPG and arterial-blood-pressure (ABP) signals. We obtained 75,226 signal segments, with 60,180 signals allocated for training data, 12,030 signals allocated for the validation set, and 15,045 signals allocated for the test data. During training, we applied five-fold cross-validation with a grid-search method to select the best model and determine the optimal hyperparameter settings. The optimized configuration of the CNN-LSTM layers consisted of five convolutional layers, one long short-term memory (LSTM) layer, and two fully connected layers for blood-pressure estimation. This study successfully achieved good accuracy in assessing both systolic blood pressure (SBP) and diastolic blood pressure (DBP) by calculating the standard deviation (SD) and the mean absolute error (MAE), resulting in values of 7.89 ± 3.79 and 5.34 ± 2.89 mmHg, respectively. The optimal configuration of the CNN-LSTM provided satisfactory performance according to the standards set by the British Hypertension Society (BHS), the Association for the Advancement of Medical Instrumentation (AAMI), and the Institute of Electrical and Electronics Engineers (IEEE) for blood-pressure monitoring devices.
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Affiliation(s)
- Nurul Qashri Mahardika T
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Gyeongbuk, Republic of Korea; (N.Q.M.T.); (Y.N.F.); (D.U.J.)
| | - Yunendah Nur Fuadah
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Gyeongbuk, Republic of Korea; (N.Q.M.T.); (Y.N.F.); (D.U.J.)
- School of Electrical Engineering, Telkom University, Bandung 40257, Indonesia
| | - Da Un Jeong
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Gyeongbuk, Republic of Korea; (N.Q.M.T.); (Y.N.F.); (D.U.J.)
| | - Ki Moo Lim
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Gyeongbuk, Republic of Korea; (N.Q.M.T.); (Y.N.F.); (D.U.J.)
- Computational Medicine Lab, Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Gyeongbuk, Republic of Korea
- Meta Heart Co., Ltd., Gumi 39177, Gyeongbuk, Republic of Korea
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27
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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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28
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Singh L, You S, Jeong BJ, Koo C, Kim Y. Remote Estimation of Blood Pressure Using Millimeter-Wave Frequency-Modulated Continuous-Wave Radar. SENSORS (BASEL, SWITZERLAND) 2023; 23:6517. [PMID: 37514810 PMCID: PMC10383350 DOI: 10.3390/s23146517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 07/07/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023]
Abstract
This paper proposes to remotely estimate a human subject's blood pressure using a millimeter-wave radar system. High blood pressure is a critical health threat that can lead to diseases including heart attacks, strokes, kidney disease, and vision loss. The commonest method of measuring blood pressure is based on a cuff that is contact-based, non-continuous, and cumbersome to wear. Continuous remote monitoring of blood pressure can facilitate early detection and treatment of heart disease. This paper investigates the possibility of using millimeter-wave frequency-modulated continuous-wave radar to measure the heart blood pressure by means of pulse wave velocity (PWV). PWV is known to be highly correlated with blood pressure, which can be measured by pulse transit time. We measured PWV using a two-millimeter wave radar focused on the subject's chest and wrist. The measured time delay provided the PWV given the length from the chest to the wrist. In addition, we analyzed the measured radar signal from the wrist because the shape of the pulse wave purveyed information on blood pressure. We investigated the area under the curve (AUC) as a feature and found that AUC is strongly correlated with blood pressure. In the experiment, five human subjects were measured 50 times each after performing different activities intended to influence blood pressure. We used artificial neural networks to estimate systolic blood pressure (SBP) and diastolic blood pressure (SBP) with both PWV and AUC as inputs. The resulting root mean square errors of estimated blood pressure were 3.33 mmHg for SBP and 3.14 mmHg for DBP.
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Affiliation(s)
- Lovedeep Singh
- Department of Electrical and Computer Engineering, California State University, Fresno, CA 93740, USA
| | - Sungjin You
- Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea
| | - Byung Jang Jeong
- Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea
| | - Chiwan Koo
- Department of Electronic Engineering, Hanbat National University, Daejeon 34158, Republic of Korea
| | - Youngwook Kim
- Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea
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29
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Manga S, Muthavarapu N, Redij R, Baraskar B, Kaur A, Gaddam S, Gopalakrishnan K, Shinde R, Rajagopal A, Samaddar P, Damani DN, Shivaram S, Dey S, Mitra D, Roy S, Kulkarni K, Arunachalam SP. Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives. SENSORS (BASEL, SWITZERLAND) 2023; 23:5744. [PMID: 37420919 DOI: 10.3390/s23125744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 05/25/2023] [Accepted: 06/12/2023] [Indexed: 07/09/2023]
Abstract
The measurement of physiologic pressure helps diagnose and prevent associated health complications. From typical conventional methods to more complicated modalities, such as the estimation of intracranial pressures, numerous invasive and noninvasive tools that provide us with insight into daily physiology and aid in understanding pathology are within our grasp. Currently, our standards for estimating vital pressures, including continuous BP measurements, pulmonary capillary wedge pressures, and hepatic portal gradients, involve the use of invasive modalities. As an emerging field in medical technology, artificial intelligence (AI) has been incorporated into analyzing and predicting patterns of physiologic pressures. AI has been used to construct models that have clinical applicability both in hospital settings and at-home settings for ease of use for patients. Studies applying AI to each of these compartmental pressures were searched and shortlisted for thorough assessment and review. There are several AI-based innovations in noninvasive blood pressure estimation based on imaging, auscultation, oscillometry and wearable technology employing biosignals. The purpose of this review is to provide an in-depth assessment of the involved physiologies, prevailing methodologies and emerging technologies incorporating AI in clinical practice for each type of compartmental pressure measurement. We also bring to the forefront AI-based noninvasive estimation techniques for physiologic pressure based on microwave systems that have promising potential for clinical practice.
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Affiliation(s)
- Sharanya Manga
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Neha Muthavarapu
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Renisha Redij
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Avneet Kaur
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Sunil Gaddam
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Keerthy Gopalakrishnan
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Rutuja Shinde
- Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Poulami Samaddar
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Devanshi N Damani
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Internal Medicine, Texas Tech University Health Science Center, El Paso, TX 79995, USA
| | - Suganti Shivaram
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Shuvashis Dey
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Electrical and Computer Engineering, North Dakota State University, Fargo, ND 58105, USA
| | - Dipankar Mitra
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Computer Science, University of Wisconsin-La Crosse, La Crosse, WI 54601, USA
| | - Sayan Roy
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Electrical Engineering and Computer Science, South Dakota Mines, Rapid City, SD 57701, USA
| | - Kanchan Kulkarni
- Centre de Recherche Cardio-Thoracique de Bordeaux, University of Bordeaux, INSERM, U1045, 33000 Bordeaux, France
- IHU Liryc, Heart Rhythm Disease Institute, Fondation Bordeaux Université, Bordeaux, 33600 Pessac, France
| | - Shivaram P Arunachalam
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
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30
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Cheng H, Xiong J, Chen Z, Chen J. Deep Learning-Based Non-Contact IPPG Signal Blood Pressure Measurement Research. SENSORS (BASEL, SWITZERLAND) 2023; 23:5528. [PMID: 37420695 DOI: 10.3390/s23125528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 05/30/2023] [Accepted: 06/05/2023] [Indexed: 07/09/2023]
Abstract
In this paper, a multi-stage deep learning blood pressure prediction model based on imaging photoplethysmography (IPPG) signals is proposed to achieve accurate and convenient monitoring of human blood pressure. A camera-based non-contact human IPPG signal acquisition system is designed. The system can perform experimental acquisition under ambient light, effectively reducing the cost of non-contact pulse wave signal acquisition while simplifying the operation process. The first open-source dataset IPPG-BP for IPPG signal and blood pressure data is constructed by this system, and a multi-stage blood pressure estimation model combining a convolutional neural network and bidirectional gated recurrent neural network is designed. The results of the model conform to both BHS and AAMI international standards. Compared with other blood pressure estimation methods, the multi-stage model automatically extracts features through a deep learning network and combines different morphological features of diastolic and systolic waveforms, which reduces the workload while improving accuracy.
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Affiliation(s)
- Hanquan Cheng
- College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321000, China
| | - Jiping Xiong
- College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321000, China
| | - Zehui Chen
- College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321000, China
| | - Jingwei Chen
- College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321000, China
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31
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Seo Y, Kwon S, Sunarya U, Park S, Park K, Jung D, Cho Y, Park C. Blood pressure estimation and its recalibration assessment using wrist cuff blood pressure monitor. Biomed Eng Lett 2023; 13:221-233. [PMID: 37124108 PMCID: PMC10130301 DOI: 10.1007/s13534-023-00271-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 01/02/2023] [Accepted: 02/16/2023] [Indexed: 05/02/2023] Open
Abstract
The rapid evolution of wearable technology in healthcare sectors has created the opportunity for people to measure their blood pressure (BP) using a smartwatch at any time during their daily activities. Several commercially-available wearable devices have recently been equipped with a BP monitoring feature. However, concerns about recalibration remain. Pulse transit time (PTT)-based estimation is required for initial calibration, followed by periodic recalibration. Recalibration using arm-cuff BP monitors is not practical during everyday activities. In this study, we investigated recalibration using PTT-based BP monitoring aided by a deep neural network (DNN) and validated the performance achieved with more practical wrist-cuff BP monitors. The PTT-based prediction produced a mean absolute error (MAE) of 4.746 ± 1.529 mmHg for systolic blood pressure (SBP) and 3.448 ± 0.608 mmHg for diastolic blood pressure (DBP) when tested with an arm-cuff monitor employing recalibration. Recalibration clearly improved the performance of both DNN and conventional linear regression approaches. We established that the periodic recalibration performed by a wrist-worn BP monitor could be as accurate as that obtained with an arm-worn monitor, confirming the suitability of wrist-worn devices for everyday use. This is the first study to establish the potential of wrist-cuff BP monitors as a means to calibrate BP monitoring devices that can reliably substitute for arm-cuff BP monitors. With the use of wrist-cuff BP monitoring devices, continuous BP estimation, as well as frequent calibrations to ensure accurate BP monitoring, are now feasible.
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Affiliation(s)
- Youjung Seo
- Department of Computer Engineering, Kwangwoon University, Seoul, 01897 Korea
| | - Saehim Kwon
- Department of Artificial Intelligence, Kwangwoon University, Seoul, 01897 Korea
| | - Unang Sunarya
- Department of Computer Engineering, Kwangwoon University, Seoul, 01897 Korea
- School of Applied Science, Telkom University, Bandung, 40257 Indonesia
| | - Sungmin Park
- Department of Convergence IT Engineering and the Department of Electrical Engineering, Pohang University of Science and Technology, Pohang, 37673 Korea
| | - Kwangsuk Park
- Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul, 03080 Korea
| | - Dawoon Jung
- Center for Artificial Intelligence, Korea Institute of Science and Technology, Seoul, 13916 Korea
| | - Youngho Cho
- Department of Electrical and Communication Engineering, University of Daelim, Anyang, 13916 Korea
| | - Cheolsoo Park
- Department of Computer Engineering, Kwangwoon University, Seoul, 01897 Korea
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32
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Samimi H, Dajani HR. A PPG-Based Calibration-Free Cuffless Blood Pressure Estimation Method Using Cardiovascular Dynamics. SENSORS (BASEL, SWITZERLAND) 2023; 23:4145. [PMID: 37112490 PMCID: PMC10146008 DOI: 10.3390/s23084145] [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: 03/15/2023] [Revised: 04/18/2023] [Accepted: 04/19/2023] [Indexed: 06/19/2023]
Abstract
Traditional cuff-based sphygmomanometers for measuring blood pressure can be uncomfortable and particularly unsuitable to use during sleep. A proposed alternative method uses dynamic changes in the pulse waveform over short intervals and replaces calibration with information from photoplethysmogram (PPG) morphology to provide a calibration-free approach using a single sensor. Results from 30 patients show a high correlation of 73.64% for systolic blood pressure (SBP) and 77.72% for diastolic blood pressure (DBP) between blood pressure estimated with the PPG morphology features and the calibration method. This suggests that the PPG morphology features could replace the calibration stage for a calibration-free method with similar accuracy. Applying the proposed methodology on 200 patients and testing on 25 new patients resulted in a mean error (ME) of -0.31 mmHg, a standard deviation of error (SDE) of 4.89 mmHg, a mean absolute error (MAE) of 3.32 mmHg for DBP and an ME of -4.02 mmHg, an SDE of 10.40 mmHg, and an MAE of 7.41 mmHg for SBP. These results support the potential for using a PPG signal for calibration-free cuffless blood pressure estimation and improving accuracy by adding information from cardiovascular dynamics to different methods in the cuffless blood pressure monitoring field.
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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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Qin C, Li Y, Liu C, Ma X. Cuff-Less Blood Pressure Prediction Based on Photoplethysmography and Modified ResNet. Bioengineering (Basel) 2023; 10:bioengineering10040400. [PMID: 37106587 PMCID: PMC10135940 DOI: 10.3390/bioengineering10040400] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 03/20/2023] [Accepted: 03/20/2023] [Indexed: 04/29/2023] Open
Abstract
Cardiovascular disease (CVD) has become a common health problem of mankind, and the prevalence and mortality of CVD are rising on a year-to-year basis. Blood pressure (BP) is an important physiological parameter of the human body and also an important physiological indicator for the prevention and treatment of CVD. Existing intermittent measurement methods do not fully indicate the real BP status of the human body and cannot get rid of the restraining feeling of a cuff. Accordingly, this study proposed a deep learning network based on the ResNet34 framework for continuous prediction of BP using only the promising PPG signal. The high-quality PPG signals were first passed through a multi-scale feature extraction module after a series of pre-processing to expand the perceptive field and enhance the perception ability on features. Subsequently, useful feature information was then extracted by stacking multiple residual modules with channel attention to increase the accuracy of the model. Lastly, in the training stage, the Huber loss function was adopted to stabilize the iterative process and obtain the optimal solution of the model. On a subset of the MIMIC dataset, the errors of both SBP and DBP predicted by the model met the AAMI standards, while the accuracy of DBP reached Grade A of the BHS standard, and the accuracy of SBP almost reached Grade A of the BHS standard. The proposed method verifies the potential and feasibility of PPG signals combined with deep neural networks in the field of continuous BP monitoring. Furthermore, the method is easy to deploy in portable devices, and it is more consistent with the future trend of wearable blood-pressure-monitoring devices (e.g., smartphones and smartwatches).
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Affiliation(s)
- Caijie Qin
- Institute of Information Engineering, Sanming University, Sanming 365004, China
- CBSR&NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing 100049, China
| | - Yong Li
- Institute of Information Engineering, Sanming University, Sanming 365004, China
| | - Chibiao Liu
- Institute of Information Engineering, Sanming University, Sanming 365004, China
| | - Xibo Ma
- CBSR&NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing 100049, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
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González S, Hsieh WT, Chen TPC. A benchmark for machine-learning based non-invasive blood pressure estimation using photoplethysmogram. Sci Data 2023; 10:149. [PMID: 36944668 PMCID: PMC10030661 DOI: 10.1038/s41597-023-02020-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 02/14/2023] [Indexed: 03/23/2023] Open
Abstract
Blood Pressure (BP) is an important cardiovascular health indicator. BP is usually monitored non-invasively with a cuff-based device, which can be bulky and inconvenient. Thus, continuous and portable BP monitoring devices, such as those based on a photoplethysmography (PPG) waveform, are desirable. In particular, Machine Learning (ML) based BP estimation approaches have gained considerable attention as they have the potential to estimate intermittent or continuous BP with only a single PPG measurement. Over the last few years, many ML-based BP estimation approaches have been proposed with no agreement on their modeling methodology. To ease the model comparison, we designed a benchmark with four open datasets with shared preprocessing, the right validation strategy avoiding information shift and leak, and standard evaluation metrics. We also adapted Mean Absolute Scaled Error (MASE) to improve the interpretability of model evaluation, especially across different BP datasets. The proposed benchmark comes with open datasets and codes. We showcase its effectiveness by comparing 11 ML-based approaches of three different categories.
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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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Wang W, Mohseni P, Kilgore KL, Najafizadeh L. PulseDB: A large, cleaned dataset based on MIMIC-III and VitalDB for benchmarking cuff-less blood pressure estimation methods. Front Digit Health 2023; 4:1090854. [PMID: 36844249 PMCID: PMC9944565 DOI: 10.3389/fdgth.2022.1090854] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Accepted: 12/28/2022] [Indexed: 02/10/2023] Open
Abstract
There has been a growing interest in developing cuff-less blood pressure (BP) estimation methods to enable continuous BP monitoring from electrocardiogram (ECG) and/or photoplethysmogram (PPG) signals. The majority of these methods have been evaluated using publicly-available datasets, however, there exist significant discrepancies across studies with respect to the size, the number of subjects, and the applied pre-processing steps for the data that is eventually used for training and testing the models. Such differences make conducting performance comparison across models largely unfair, and mask the generalization capability of various BP estimation methods. To fill this important gap, this paper presents "PulseDB," the largest cleaned dataset to date, for benchmarking BP estimation models that also fulfills the requirements of standardized testing protocols. PulseDB contains 1) 5,245,454 high-quality 10 -s segments of ECG, PPG, and arterial BP (ABP) waveforms from 5,361 subjects retrieved from the MIMIC-III waveform database matched subset and the VitalDB database; 2) subjects' identification and demographic information, that can be utilized as additional input features to improve the performance of BP estimation models, or to evaluate the generalizability of the models to data from unseen subjects; and 3) positions of the characteristic points of the ECG/PPG signals, making PulseDB directly usable for training deep learning models with minimal data pre-processing. Additionally, using this dataset, we conduct the first study to provide insights about the performance gap between calibration-based and calibration-free testing approaches for evaluating generalizability of the BP estimation models. We expect PulseDB, as a user-friendly, large, comprehensive and multi-functional dataset, to be used as a reliable source for the evaluation of cuff-less BP estimation methods.
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Affiliation(s)
- Weinan Wang
- Integrated Systems and NeuroImaging Laboratory, Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, United States
| | - Pedram Mohseni
- Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Kevin L. Kilgore
- Department of Physical Medicine & Rehabilitation, Case Western Reserve University and The MetroHealth System, Cleveland, OH, United States
| | - Laleh Najafizadeh
- Integrated Systems and NeuroImaging Laboratory, Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, United States
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Hamoud B, Kashevnik A, Othman W, Shilov N. Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation. SENSORS (BASEL, SWITZERLAND) 2023; 23:1753. [PMID: 36850349 PMCID: PMC9959092 DOI: 10.3390/s23041753] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 01/23/2023] [Accepted: 02/02/2023] [Indexed: 06/18/2023]
Abstract
One of the most effective vital signs of health conditions is blood pressure. It has such an impact that changes your state from completely relaxed to extremely unpleasant, which makes the task of blood pressure monitoring a main procedure that almost everyone undergoes whenever there is something wrong or suspicious with his/her health condition. The most popular and accurate ways to measure blood pressure are cuff-based, inconvenient, and pricey, but on the bright side, many experimental studies prove that changes in the color intensities of the RGB channels represent variation in the blood that flows beneath the skin, which is strongly related to blood pressure; hence, we present a novel approach to blood pressure estimation based on the analysis of human face video using hybrid deep learning models. We deeply analyzed proposed approaches and methods to develop combinations of state-of-the-art models that were validated by their testing results on the Vision for Vitals (V4V) dataset compared to the performance of other available proposed models. Additionally, we came up with a new metric to evaluate the performance of our models using Pearson's correlation coefficient between the predicted blood pressure of the subjects and their respiratory rate at each minute, which is provided by our own dataset that includes 60 videos of operators working on personal computers for almost 20 min in each video. Our method provides a cuff-less, fast, and comfortable way to estimate blood pressure with no need for any equipment except the camera of your smartphone.
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Affiliation(s)
- Batol Hamoud
- Information Technology and Programming Faculty, ITMO University, St. Petersburg 197101, Russia
| | - Alexey Kashevnik
- St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg 199178, Russia
- Department of Applied Mathematics and Cybernetics, Institute of Mathematics and Information Technologies, Petrozavodsk State University (PetrSU), Petrozavodsk 185910, Russia
| | - Walaa Othman
- Information Technology and Programming Faculty, ITMO University, St. Petersburg 197101, Russia
| | - Nikolay Shilov
- St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg 199178, Russia
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Rastegar S, Gholam Hosseini H, Lowe A. Hybrid CNN-SVR Blood Pressure Estimation Model Using ECG and PPG Signals. SENSORS (BASEL, SWITZERLAND) 2023; 23:1259. [PMID: 36772300 PMCID: PMC9921259 DOI: 10.3390/s23031259] [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: 12/28/2022] [Revised: 01/14/2023] [Accepted: 01/20/2023] [Indexed: 06/18/2023]
Abstract
Continuous blood pressure (BP) measurement is vital in monitoring patients' health with a high risk of cardiovascular disease. The complex and dynamic nature of the cardiovascular system can influence BP through many factors, such as cardiac output, blood vessel wall elasticity, circulated blood volume, peripheral resistance, respiration, and emotional behavior. Yet, traditional BP measurement methods in continuously estimating the BP are cumbersome and inefficient. This paper presents a novel hybrid model by integrating a convolutional neural network (CNN) as a trainable feature extractor and support vector regression (SVR) as a regression model. This model can automatically extract features from the electrocardiogram (ECG) and photoplethysmography (PPG) signals and continuously estimates the systolic blood pressure (SBP) and diastolic blood pressure (DBP). The CNN takes the correct topology of input data and establishes the relationship between ECG and PPG features and BP. A total of 120 patients with available ECG, PPG, SBP, and DBP data are selected from the MIMIC III database to evaluate the performance of the proposed model. This novel model achieves an overall Mean Absolute Error (MAE) of 1.23 ± 2.45 mmHg (MAE ± STD) for SBP and 3.08 ± 5.67 for DBP, all of which comply with the accuracy requirements of the AAMI SP10 standard.
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Affiliation(s)
| | - Hamid Gholam Hosseini
- Institute of Biomedical Technologies, School of Engineering, Computing and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
| | - Andrew Lowe
- Institute of Biomedical Technologies, School of Engineering, Computing and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
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40
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Features from the photoplethysmogram and the electrocardiogram for estimating changes in blood pressure. Sci Rep 2023; 13:986. [PMID: 36653426 PMCID: PMC9849280 DOI: 10.1038/s41598-022-27170-2] [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: 10/11/2022] [Accepted: 12/27/2022] [Indexed: 01/19/2023] Open
Abstract
There is a growing emphasis being placed on the potential for cuffless blood pressure (BP) estimation through modelling of morphological features from the photoplethysmogram (PPG) and electrocardiogram (ECG). However, the appropriate features and models to use remain unclear. We investigated the best features available from the PPG and ECG for BP estimation using both linear and non-linear machine learning models. We conducted a clinical study in which changes in BP ([Formula: see text]BP) were induced by an infusion of phenylephrine in 30 healthy volunteers (53.8% female, 28.0 (9.0) years old). We extracted a large and diverse set of features from both the PPG and the ECG and assessed their individual importance for estimating [Formula: see text]BP through Shapley additive explanation values and a ranking coefficient. We trained, tuned, and evaluated linear (ordinary least squares, OLS) and non-linear (random forest, RF) machine learning models to estimate [Formula: see text]BP in a nested leave-one-subject-out cross-validation framework. We reported the results as correlation coefficient ([Formula: see text]), root mean squared error (RMSE), and mean absolute error (MAE). The non-linear RF model significantly ([Formula: see text]) outperformed the linear OLS model using both the PPG and the ECG signals across all performance metrics. Estimating [Formula: see text]SBP using the PPG alone ([Formula: see text] = 0.86 (0.23), RMSE = 5.66 (4.76) mmHg, MAE = 4.86 (4.29) mmHg) performed significantly better than using the ECG alone ([Formula: see text] = 0.69 (0.45), RMSE = 6.79 (4.76) mmHg, MAE = 5.28 (4.57) mmHg), all [Formula: see text]. The highest ranking features from the PPG largely modelled increasing reflected wave interference driven by changes in arterial stiffness. This finding was supported by changes observed in the PPG waveform in response to the phenylephrine infusion. However, a large number of features were required for accurate BP estimation, highlighting the high complexity of the problem. We conclude that the PPG alone may be further explored as a potential single source, cuffless, blood pressure estimator. The use of the ECG alone is not justified. Non-linear models may perform better as they are able to incorporate interactions between feature values and demographics. However, demographics may not adequately account for the unique and individualised relationship between the extracted features and BP.
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41
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Hossain S, Kim KD. Non-Invasive In Vivo Estimation of HbA1c Using Monte Carlo Photon Propagation Simulation: Application of Tissue-Segmented 3D MRI Stacks of the Fingertip and Wrist for Wearable Systems. SENSORS (BASEL, SWITZERLAND) 2023; 23:540. [PMID: 36617136 PMCID: PMC9824266 DOI: 10.3390/s23010540] [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: 10/21/2022] [Revised: 12/16/2022] [Accepted: 12/30/2022] [Indexed: 06/17/2023]
Abstract
The early diagnosis of diabetes mellitus in normal people or maintaining stable blood sugar concentrations in diabetic patients requires frequent monitoring of the blood sugar levels. However, regular monitoring of the sugar levels is problematic owing to the pain and inconvenience associated with pricking the fingertip or using minimally invasive patches. In this study, we devise a noninvasive method to estimate the percentage of the in vivo glycated hemoglobin (HbA1c) values from Monte Carlo photon propagation simulations, based on models of the wrist using 3D magnetic resonance (MR) image data. The MR image slices are first segmented for several different tissue types, and the proposed Monte Carlo photon propagation system with complex composite tissue support is then used to derive several models for the fingertip and wrist sections with different wavelengths of light sources and photodetector arrangements. The Pearson r values for the estimated percent HbA1c values are 0.94 and 0.96 for the fingertip transmission- and reflection-type measurements, respectively. This is found to be the best among the related studies. Furthermore, a single-detector multiple-source arrangement resulted in a Pearson r value of 0.97 for the wrist. The Bland-Altman bias values were found to be -0.003 ± 0.36, 0.01 ± 0.25, and 0.01 ± 0.21, for the two fingertip and wrist models, respectively, which conform to the standards of the current state-of-the-art invasive point-of-care devices. The implementation of these algorithms will be a suitable alternative to the invasive state-of-the-art methods.
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Affiliation(s)
- Shifat Hossain
- Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA
| | - Ki-Doo Kim
- Department of Electronics Engineering, Kookmin University, Seoul 02707, Republic of Korea
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Hu X, Yin S, Zhang X, Menon C, Fang C, Chen Z, Elgendi M, Liang Y. Blood pressure stratification using photoplethysmography and light gradient boosting machine. Front Physiol 2023; 14:1072273. [PMID: 36891146 PMCID: PMC9986584 DOI: 10.3389/fphys.2023.1072273] [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: 10/17/2022] [Accepted: 02/06/2023] [Indexed: 02/22/2023] Open
Abstract
Introduction: Globally, hypertension (HT) is a substantial risk factor for cardiovascular disease and mortality; hence, rapid identification and treatment of HT is crucial. In this study, we tested the light gradient boosting machine (LightGBM) machine learning method for blood pressure stratification based on photoplethysmography (PPG), which is used in most wearable devices. Methods: We used 121 records of PPG and arterial blood pressure (ABP) signals from the Medical Information Mart for Intensive Care III public database. PPG, velocity plethysmography, and acceleration plethysmography were used to estimate blood pressure; the ABP signals were used to determine the blood pressure stratification categories. Seven feature sets were established and used to train the Optuna-tuned LightGBM model. Three trials compared normotension (NT) vs. prehypertension (PHT), NT vs. HT, and NT + PHT vs. HT. Results: The F1 scores for these three classification trials were 90.18%, 97.51%, and 92.77%, respectively. The results showed that combining multiple features from PPG and its derivative led to a more accurate classification of HT classes than using features from only the PPG signal. Discussion: The proposed method showed high accuracy in stratifying HT risks, providing a noninvasive, rapid, and robust method for the early detection of HT, with promising applications in the field of wearable cuffless blood pressure measurement.
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Affiliation(s)
- Xudong Hu
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China
| | - Shimin Yin
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China
| | - Xizhuang Zhang
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, China
| | - Carlo Menon
- Biomedical and Mobile Health Technology Lab, ETH Zurich, Zurich, Switzerland
| | - Cheng Fang
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China
| | - Zhencheng Chen
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China.,Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin, China.,Guangxi Engineering Technology Research Center of Human Physiological Information Noninvasive Detection, Guilin, China
| | - Mohamed Elgendi
- Biomedical and Mobile Health Technology Lab, ETH Zurich, Zurich, Switzerland
| | - Yongbo Liang
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China.,Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin, China.,Guangxi Engineering Technology Research Center of Human Physiological Information Noninvasive Detection, Guilin, China
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43
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Weber-Boisvert G, Gosselin B, Sandberg F. Intensive care photoplethysmogram datasets and machine-learning for blood pressure estimation: Generalization not guarantied. Front Physiol 2023; 14:1126957. [PMID: 36935753 PMCID: PMC10017741 DOI: 10.3389/fphys.2023.1126957] [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: 12/18/2022] [Accepted: 02/17/2023] [Indexed: 03/06/2023] Open
Abstract
The large MIMIC waveform dataset, sourced from intensive care units, has been used extensively for the development of Photoplethysmography (PPG) based blood pressure (BP) estimation algorithms. Yet, because the data comes from patients in severe conditions-often under the effect of drugs-it is regularly noted that the relationship between BP and PPG signal characteristics may be anomalous, a claim that we investigate here. A sample of 12,000 records from the MIMIC waveform dataset was stacked up against the 219 records of the PPG-BP dataset, an alternative public dataset obtained under controlled experimental conditions. The distribution of systolic and diastolic BP data and 31 PPG pulse morphological features was first compared between datasets. Then, the correlation between features and BP, as well as between the features themselves, was analysed. Finally, regression models were trained for each dataset and validated against the other. Statistical analysis showed significant p < 0.001 differences between the datasets in diastolic BP and in 20 out of 31 features when adjusting for heart rate differences. The eight features showing the highest rank correlation ρ > 0.40 to systolic BP in PPG-BP all displayed muted correlation levels ρ < 0.10 in MIMIC. Regression tests showed twice higher baseline predictive power with PPG-BP than with MIMIC. Cross-dataset regression displayed a practically complete loss of predictive power for all models. The differences between the MIMIC and PPG-BP dataset exposed in this study suggest that BP estimation models based on the MIMIC dataset have reduced predictive power on the general population.
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Affiliation(s)
- Guillaume Weber-Boisvert
- Department of Electrical and Computer Engineering, Université Laval, Quebec, QC, Canada
- Correspondence: Guillaume Weber-Boisvert, ; Frida Sandberg,
| | - Benoit Gosselin
- Department of Electrical and Computer Engineering, Université Laval, Quebec, QC, Canada
| | - Frida Sandberg
- Department of Biomedical Engineering, Lund University, Lund, Sweden
- Correspondence: Guillaume Weber-Boisvert, ; Frida Sandberg,
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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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45
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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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Ibtehaz N, Mahmud S, Chowdhury MEH, Khandakar A, Salman Khan M, Ayari MA, Tahir AM, Rahman MS. PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms. Bioengineering (Basel) 2022; 9:692. [PMID: 36421093 PMCID: PMC9687508 DOI: 10.3390/bioengineering9110692] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/08/2022] [Accepted: 11/11/2022] [Indexed: 08/13/2023] Open
Abstract
Cardiovascular diseases are one of the most severe causes of mortality, annually taking a heavy toll on lives worldwide. Continuous monitoring of blood pressure seems to be the most viable option, but this demands an invasive process, introducing several layers of complexities and reliability concerns due to non-invasive techniques not being accurate. This motivates us to develop a method to estimate the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using Photoplethysmogram (PPG) signals. We explore the advantage of deep learning, as it would free us from sticking to ideally shaped PPG signals only by making handcrafted feature computation irrelevant, which is a shortcoming of the existing approaches. Thus, we present PPG2ABP, a two-stage cascaded deep learning-based method that manages to estimate the continuous ABP waveform from the input PPG signal with a mean absolute error of 4.604 mmHg, preserving the shape, magnitude, and phase in unison. However, the more astounding success of PPG2ABP turns out to be that the computed values of Diastolic Blood Pressure (DBP), Mean Arterial Pressure (MAP), and Systolic Blood Pressure (SBP) from the estimated ABP waveform outperform the existing works under several metrics (mean absolute error of 3.449 ± 6.147 mmHg, 2.310 ± 4.437 mmHg, and 5.727 ± 9.162 mmHg, respectively), despite that PPG2ABP is not explicitly trained to do so. Notably, both for DBP and MAP, we achieve Grade A in the BHS (British Hypertension Society) Standard and satisfy the AAMI (Association for the Advancement of Medical Instrumentation) standard.
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Affiliation(s)
- Nabil Ibtehaz
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Sakib Mahmud
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | | | - Amith Khandakar
- 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
| | - Anas M. Tahir
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - M. Sohel Rahman
- Department of CSE, BUET, ECE Building, West Palashi, Dhaka 1205, Bangladesh
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Hemorrhagic risk prediction in coronary artery disease patients based on photoplethysmography and machine learning. Sci Rep 2022; 12:19190. [PMID: 36357443 PMCID: PMC9649686 DOI: 10.1038/s41598-022-22719-7] [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: 06/23/2022] [Accepted: 10/18/2022] [Indexed: 11/11/2022] Open
Abstract
Hemorrhagic events are the main focus of attention during antithrombosis therapy in patients with coronary artery disease (CAD). This study aims to investigate the potential of using photoplethysmography (PPG) and machine learning techniques to assess hemorrhagic risk in patients with CAD. A total of 1638 patients with CAD were enrolled from January 2018 to October 2019, among which 114 patients were observed to have at least one positive event. Importantly, 102 patients with 9933 records were finally retained for analysis in this study. Participants were required to collect data using the portable PPG acquisition device and the specially designed Android APP. The data was collected and uploaded to a remote server. Based on collected PPG signals, we extracted features in a total of 30 dimensions from time-domain, frequency-domain, and wavelet packet decomposition. Logistic regression, support vector regression, random forest, and XGBoost regression models were established to achieve hemorrhagic risk evaluation, and then, their performances were compared. In total, 10 features extracted from PPG showed statistical significance (p < 0.01) between negative and positive groups. The newly established XGBoost model performed best in the hemorrhagic risk evaluation experiment, wherein the mean area under the curve (AUC) with tenfold cross-validation was 0.762 ± 0.024 and the sensitivity and specificity were 0.679 ± 0.051 and 0.714 ± 0.014, respectively. We established a data acquisition system for PPG signal collection, and demonstrated that a set of features extracted from PPG and the proposed machine learning model are promising in the evaluation of hemorrhagic risk among patients with CAD. In comparison with the traditional HAS-BLED score, the proposed method can obtain the quantitative risk prediction probability from a single PPG record, which has the advantages of dynamics and continuity, and can provide timely feedback for doctors' antithrombotic treatment, which is of great significance for doctors to quickly determine the effectiveness of the treatment and adjust the timely treatment plans accordingly.
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Ku CJ, Wang Y, Chang CY, Wu MT, Dai ST, Liao LD. Noninvasive blood oxygen, heartbeat rate, and blood pressure parameter monitoring by photoplethysmography signals. Heliyon 2022; 8:e11698. [DOI: 10.1016/j.heliyon.2022.e11698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 08/02/2022] [Accepted: 11/10/2022] [Indexed: 11/19/2022] Open
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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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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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