1
|
Zhou L, Guess M, Kim KR, Yeo WH. Skin-interfacing wearable biosensors for smart health monitoring of infants and neonates. COMMUNICATIONS MATERIALS 2024; 5:72. [PMID: 38737724 PMCID: PMC11081930 DOI: 10.1038/s43246-024-00511-6] [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: 12/05/2023] [Accepted: 04/23/2024] [Indexed: 05/14/2024]
Abstract
Health monitoring of infant patients in intensive care can be especially strenuous for both the patient and their caregiver, as testing setups involve a tangle of electrodes, probes, and catheters that keep the patient bedridden. This has typically involved expensive and imposing machines, to track physiological metrics such as heart rate, respiration rate, temperature, blood oxygen saturation, blood pressure, and ion concentrations. However, in the past couple of decades, research advancements have propelled a world of soft, wearable, and non-invasive systems to supersede current practices. This paper summarizes the latest advancements in neonatal wearable systems and the different approaches to each branch of physiological monitoring, with an emphasis on smart skin-interfaced wearables. Weaknesses and shortfalls are also addressed, with some guidelines provided to help drive the further research needed.
Collapse
Affiliation(s)
- Lauren Zhou
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
- IEN Center for Wearable Intelligent Systems and Healthcare, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332 USA
| | - Matthew Guess
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
- IEN Center for Wearable Intelligent Systems and Healthcare, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332 USA
| | - Ka Ram Kim
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
- IEN Center for Wearable Intelligent Systems and Healthcare, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332 USA
| | - Woon-Hong Yeo
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
- IEN Center for Wearable Intelligent Systems and Healthcare, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332 USA
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332 USA
- Parker H. Petit Institute for Bioengineering and Biosciences, Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA 30332 USA
| |
Collapse
|
2
|
Han X, Yang X, Fang S, Chen Y, Chen Q, Li L, Song R. Preserving shape details of pulse signals for video-based blood pressure estimation. BIOMEDICAL OPTICS EXPRESS 2024; 15:2433-2450. [PMID: 38633075 PMCID: PMC11019694 DOI: 10.1364/boe.516388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/02/2024] [Accepted: 02/14/2024] [Indexed: 04/19/2024]
Abstract
In recent years, imaging photoplethysmograph (iPPG) pulse signals have been widely used in the research of non-contact blood pressure (BP) estimation, in which BP estimation based on pulse features is the main research direction. Pulse features are directly related to the shape of pulse signals while iPPG pulse signals are easily disturbed during the extraction process. To mitigate the impact of pulse feature distortion on BP estimation, it is necessary to eliminate interference while retaining valuable shape details in the iPPG pulse signal. Contact photoplethysmograph (cPPG) pulse signals measured at rest can be considered as the undisturbed reference signal. Transforming the iPPG pulse signal to the corresponding cPPG pulse signal is a method to ensure the effectiveness of shape details. However, achieving the required shape accuracy through direct transformation from iPPG to the corresponding cPPG pulse signals is challenging. We propose a method to mitigate this challenge by replacing the reference signal with an average cardiac cycle (ACC) signal, which can approximately represent the shape information of all cardiac cycles in a short time. A neural network using multi-scale convolution and self-attention mechanisms is developed for this transformation. Our method demonstrates a significant improvement in the maximal information coefficient (MIC) between pulse features and BP values, indicating a stronger correlation. Moreover, pulse signals transformed by our method exhibit enhanced performance in BP estimation using different model types. Experiments are conducted on a real-world database with 491 subjects in the hospital, averaging 60 years of age.
Collapse
Affiliation(s)
- Xuesong Han
- School of Computer and Information, Hefei University of Technology, Hefei, 230009, China
- Anhui Key Laboratory of Industry Safety and Emergency Technology, Hefei, 230009, China
| | - Xuezhi Yang
- School of Computer and Information, Hefei University of Technology, Hefei, 230009, China
- Anhui Key Laboratory of Industry Safety and Emergency Technology, Hefei, 230009, China
| | - Shuai Fang
- School of Computer and Information, Hefei University of Technology, Hefei, 230009, China
- Anhui Key Laboratory of Industry Safety and Emergency Technology, Hefei, 230009, China
| | - Yawei Chen
- School of Computer and Information, Hefei University of Technology, Hefei, 230009, China
- Anhui Key Laboratory of Industry Safety and Emergency Technology, Hefei, 230009, China
| | - Qin Chen
- School of Computer and Information, Hefei University of Technology, Hefei, 230009, China
- Anhui Key Laboratory of Industry Safety and Emergency Technology, Hefei, 230009, China
| | - Longwei Li
- The First Affiliated Hospital of the University of Science and Technology of China, Hefei, 230036, China
| | - RenCheng Song
- School of Instrument Science and Opto-electronics Engineering, Hefei University of Technology, Hefei, 230009, China
| |
Collapse
|
3
|
Jiang X, Zhang J, Mu W, Wang K, Li L, Zhang L. TRCCBP: Transformer Network for Radar-Based Contactless Continuous Blood Pressure Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:9680. [PMID: 38139525 PMCID: PMC10747831 DOI: 10.3390/s23249680] [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: 09/19/2023] [Revised: 11/10/2023] [Accepted: 11/28/2023] [Indexed: 12/24/2023]
Abstract
Contactless continuous blood pressure (BP) monitoring is of great significance for daily healthcare. Radar-based continuous monitoring methods typically extract time-domain features manually such as pulse transit time (PTT) to calculate the BP. However, breathing and slight body movements usually distort the features extracted from pulse-wave signals, especially in long-term continuous monitoring, and manually extracted features may have limited performance for BP estimation. This article proposes a Transformer network for Radar-based Contactless Continuous Blood Pressure monitoring (TRCCBP). A heartbeat signal-guided single-beat pulse wave extraction method is designed to obtain pure pulse-wave signals. A transformer network-based blood pressure estimation network is proposed to estimate BP, which utilizes convolutional layers with different scales, a gated recurrent unit (GRU) to capture time-dependence in continuous radar signal and multi-head attention modules to capture deep temporal domain characteristics. A radar signal dataset captured in an indoor environment containing 31 persons and a real medical situation containing five persons is set up to evaluate the performance of TRCCBP. Compared with the state-of-the-art method, the average accuracy of diastolic blood pressure (DBP) and systolic blood pressure (SBP) is 4.49 mmHg and 4.73 mmHg, improved by 12.36 mmHg and 8.80 mmHg, respectively. The proposed TRCCBP source codes and radar signal dataset have been made open-source online for further research.
Collapse
Affiliation(s)
- Xikang Jiang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China; (X.J.)
| | - Jinhui Zhang
- Logistic Support Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Wenyao Mu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China; (X.J.)
| | - Kun Wang
- Logistic Support Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Lei Li
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China; (X.J.)
| | - Lin Zhang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China; (X.J.)
| |
Collapse
|
4
|
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.
Collapse
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
| |
Collapse
|
5
|
Khan Mamun MMR, Sherif A. Advancement in the Cuffless and Noninvasive Measurement of Blood Pressure: A Review of the Literature and Open Challenges. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 10:bioengineering10010027. [PMID: 36671599 PMCID: PMC9854981 DOI: 10.3390/bioengineering10010027] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 12/20/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022]
Abstract
Hypertension is a chronic condition that is one of the prominent reasons behind cardiovascular disease, brain stroke, and organ failure. Left unnoticed and untreated, the deterioration in a health condition could even result in mortality. If it can be detected early, with proper treatment, undesirable outcomes can be avoided. Until now, the gold standard is the invasive way of measuring blood pressure (BP) using a catheter. Additionally, the cuff-based and noninvasive methods are too cumbersome or inconvenient for frequent measurement of BP. With the advancement of sensor technology, signal processing techniques, and machine learning algorithms, researchers are trying to find the perfect relationships between biomedical signals and changes in BP. This paper is a literature review of the studies conducted on the cuffless noninvasive measurement of BP using biomedical signals. Relevant articles were selected using specific criteria, then traditional techniques for BP measurement were discussed along with a motivation for cuffless measurement use of biomedical signals and machine learning algorithms. The review focused on the progression of different noninvasive cuffless techniques rather than comparing performance among different studies. The literature survey concluded that the use of deep learning proved to be the most accurate among all the cuffless measurement techniques. On the other side, this accuracy has several disadvantages, such as lack of interpretability, computationally extensive, standard validation protocol, and lack of collaboration with health professionals. Additionally, the continuing work by researchers is progressing with a potential solution for these challenges. Finally, future research directions have been provided to encounter the challenges.
Collapse
Affiliation(s)
| | - Ahmed Sherif
- School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, Hattiesburg, MS 39406, USA
| |
Collapse
|