1
|
Chen G, Zou L, Ji Z. A review: Blood pressure monitoring based on PPG and circadian rhythm. APL Bioeng 2024; 8:031501. [PMID: 39049850 PMCID: PMC11268918 DOI: 10.1063/5.0206980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 06/26/2024] [Indexed: 07/27/2024] Open
Abstract
The demand for ambulatory blood pressure monitoring (ABPM) is increasing due to the global rise in cardiovascular disease patients. However, conventional ABPM methods are discontinuous and can disrupt daily activities and sleep patterns. Photoplethysmography (PPG) is gaining attention from researchers due to its simplicity, portability, affordability, and ease of signal acquisition. This paper critically examines the advancements achieved in the technology of PPG-guided noninvasive blood pressure (BP) monitoring and explores future opportunities. We have performed a literature search using the Web of Science and PubMed search engines, from January 2018 to October 2023, for PPG signal quality assessment (SQA), cuffless BP estimation using single PPG, and associations between circadian rhythm and BP. Based on this foundation, we first examine the impact of PPG signal quality on blood pressure estimation results while focusing on methods for assessing PPG signal quality. Subsequently, the methods documented for estimating cuff-free BP from PPG signals are summarized. Furthermore, the study examines how individual differences affect the accuracy of BP estimation, incorporating the factors that influence arterial blood pressure (ABP) and elucidating the impact of circadian rhythm on blood pressure. Finally, there will be a summary of the study's findings and suggestions for future research directions.
Collapse
Affiliation(s)
- Gang Chen
- College of Bioengineering, Chongqing University, Chongqing 400030, China
| | - Linglin Zou
- Department of oncology, Affiliated Hospital of Southwest Medical University, Luzhou 646000, China
| | - Zhong Ji
- Author to whom correspondence should be addressed:
| |
Collapse
|
2
|
Hashmi SJ, Alabdullah B, Al Mudawi N, Algarni A, Jalal A, Liu H. Enhanced Data Mining and Visualization of Sensory-Graph-Modeled Datasets through Summarization. SENSORS (BASEL, SWITZERLAND) 2024; 24:4554. [PMID: 39065952 PMCID: PMC11280993 DOI: 10.3390/s24144554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 06/11/2024] [Accepted: 07/09/2024] [Indexed: 07/28/2024]
Abstract
The acquisition, processing, mining, and visualization of sensory data for knowledge discovery and decision support has recently been a popular area of research and exploration. Its usefulness is paramount because of its relationship to the continuous involvement in the improvement of healthcare and other related disciplines. As a result of this, a huge amount of data have been collected and analyzed. These data are made available for the research community in various shapes and formats; their representation and study in the form of graphs or networks is also an area of research which many scholars are focused on. However, the large size of such graph datasets poses challenges in data mining and visualization. For example, knowledge discovery from the Bio-Mouse-Gene dataset, which has over 43 thousand nodes and 14.5 million edges, is a non-trivial job. In this regard, summarizing the large graphs provided is a useful alternative. Graph summarization aims to provide the efficient analysis of such complex and large-sized data; hence, it is a beneficial approach. During summarization, all the nodes that have similar structural properties are merged together. In doing so, traditional methods often overlook the importance of personalizing the summary, which would be helpful in highlighting certain targeted nodes. Personalized or context-specific scenarios require a more tailored approach for accurately capturing distinct patterns and trends. Hence, the concept of personalized graph summarization aims to acquire a concise depiction of the graph, emphasizing connections that are closer in proximity to a specific set of given target nodes. In this paper, we present a faster algorithm for the personalized graph summarization (PGS) problem, named IPGS; this has been designed to facilitate enhanced and effective data mining and visualization of datasets from various domains, including biosensors. Our objective is to obtain a similar compression ratio as the one provided by the state-of-the-art PGS algorithm, but in a faster manner. To achieve this, we improve the execution time of the current state-of-the-art approach by using weighted, locality-sensitive hashing, through experiments on eight large publicly available datasets. The experiments demonstrate the effectiveness and scalability of IPGS while providing a similar compression ratio to the state-of-the-art approach. In this way, our research contributes to the study and analysis of sensory datasets through the perspective of graph summarization. We have also presented a detailed study on the Bio-Mouse-Gene dataset, which was conducted to investigate the effectiveness of graph summarization in the domain of biosensors.
Collapse
Affiliation(s)
- Syed Jalaluddin Hashmi
- School of Computing, National University of Computer and Emerging Science, Islamabad 44000, Pakistan;
| | - Bayan Alabdullah
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Naif Al Mudawi
- Department of Computer Science, College of Computer Science and Information System, Najran University, Najran 55461, Saudi Arabia;
| | - Asaad Algarni
- Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia;
| | - Ahmad Jalal
- Faculty of Computing and AI, Air University, E-9, Islamabad 44000, Pakistan
| | - Hui Liu
- Cognitive Systems Lab, University of Bremen, 28359 Bremen, Germany
| |
Collapse
|
3
|
Elgendi M, Fletcher R, Liang Y, Howard N, Lovell NH, Abbott D, Lim K, Ward R. The use of photoplethysmography for assessing hypertension. NPJ Digit Med 2019; 2:60. [PMID: 31388564 PMCID: PMC6594942 DOI: 10.1038/s41746-019-0136-7] [Citation(s) in RCA: 207] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Accepted: 06/05/2019] [Indexed: 12/13/2022] Open
Abstract
The measurement of blood pressure (BP) is critical to the treatment and management of many medical conditions. High blood pressure is associated with many chronic disease conditions, and is a major source of mortality and morbidity around the world. For outpatient care as well as general health monitoring, there is great interest in being able to accurately and frequently measure BP outside of a clinical setting, using mobile or wearable devices. One possible solution is photoplethysmography (PPG), which is most commonly used in pulse oximetry in clinical settings for measuring oxygen saturation. PPG technology is becoming more readily available, inexpensive, convenient, and easily integrated into portable devices. Recent advances include the development of smartphones and wearable devices that collect pulse oximeter signals. In this article, we review (i) the state-of-the-art and the literature related to PPG signals collected by pulse oximeters, (ii) various theoretical approaches that have been adopted in PPG BP measurement studies, and (iii) the potential of PPG measurement devices as a wearable application. Past studies on changes in PPG signals and BP are highlighted, and the correlation between PPG signals and BP are discussed. We also review the combined use of features extracted from PPG and other physiological signals in estimating BP. Although the technology is not yet mature, it is anticipated that in the near future, accurate, continuous BP measurements may be available from mobile and wearable devices given their vast potential.
Collapse
Affiliation(s)
- Mohamed Elgendi
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada
- Department of Obstetrics & Gynecology, University of British Columbia, Vancouver, Canada
- BC Children’s & Women’s Hospital, Vancouver, Canada
| | - Richard Fletcher
- D-Lab, Massachusetts Institute of Technology, Cambridge, MA USA
- Department of Psychiatry, University of Massachusetts Medical School, Worcester, MA USA
| | - Yongbo Liang
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada
| | - Newton Howard
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- Howard Brain Sciences Foundation, Providence, Rhode Island USA
| | - Nigel H. Lovell
- Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW Australia
| | - Derek Abbott
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA Australia
- Centre for Biomedical Engineering, The University of Adelaide, Adelaide, SA Australia
| | - Kenneth Lim
- Department of Obstetrics & Gynecology, University of British Columbia, Vancouver, Canada
- BC Children’s & Women’s Hospital, Vancouver, Canada
| | - Rabab Ward
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada
| |
Collapse
|
4
|
Elgendi M, Menon C. Assessing Anxiety Disorders Using Wearable Devices: Challenges and Future Directions. Brain Sci 2019; 9:E50. [PMID: 30823690 PMCID: PMC6468793 DOI: 10.3390/brainsci9030050] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Revised: 02/14/2019] [Accepted: 02/26/2019] [Indexed: 02/07/2023] Open
Abstract
Wearable devices (WD) are starting to increasingly be used for interventions to promote well-being by reducing anxiety disorders (AD). Electrocardiogram (ECG) signal is one of the most commonly used biosignals for assessing the cardiovascular system as it significantly reflects the activity of the autonomic nervous system during emotional changes. Little is known about the accuracy of using ECG features for detecting ADs. Moreover, during our literature review, a limited number of studies were found that involve ECG collection using WD for promoting mental well-being. Thus, for the sake of validating the reliability of ECG features for detecting anxiety in WD, we screened 1040 articles, and only 22 were considered for our study; specifically 6 on panic, 4 on post-traumatic stress, 4 on generalized anxiety, 3 on social, 3 on mixed, and 2 on obsessive-compulsive anxiety disorder articles. Most experimental studies had controversial results. Upon reviewing each of these papers, it became apparent that the use of ECG features for detecting different types of anxiety is controversial, and the use of ECG-WD is an emerging area of research, with limited evidence suggesting its reliability. Due to the clinical nature of most studies, it is difficult to determine the specific impact of ECG features on detecting ADs, suggesting the need for more robust studies following our proposed recommendations.
Collapse
Affiliation(s)
- Mohamed Elgendi
- Menrva Research Group, Schools of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, Surrey, BC V3T 0A3, Canada.
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
- Faculty of Medicine, University of British Columbia, Vancouver, BC V1Y 1T3, Canada.
- BC Children's & Women's Hospital, Vancouver, BC V6H 3N1, Canada.
| | - Carlo Menon
- Menrva Research Group, Schools of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, Surrey, BC V3T 0A3, Canada.
| |
Collapse
|
5
|
Hypertension Assessment Using Photoplethysmography: A Risk Stratification Approach. J Clin Med 2018; 8:jcm8010012. [PMID: 30577637 PMCID: PMC6352119 DOI: 10.3390/jcm8010012] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 11/29/2018] [Accepted: 12/14/2018] [Indexed: 12/01/2022] Open
Abstract
Hypertension is a common chronic cardiovascular disease (CVD). Early screening and diagnosis of hypertension plays a major role in its prevention and in the control of CVDs. Our study discusses the early screening of hypertension while using the morphological features of photoplethysmography (PPG). Numerous morphological features of PPG and its derivative waves were defined and extracted. Six types of feature selection methods were chosen to screen and evaluate these PPG morphological features. The optimal features were comprehensively analyzed in relation to the physiological processes of the cardiovascular circulatory system. Particularly, the intrinsic relation and physiological significance between the formation process of systolic blood pressure (SBP) and PPG morphology features were analyzed in depth. A variety of linear and nonlinear classification models were established for the comparison trials. The F1 scores for the normotension versus prehypertension, normotension and prehypertension versus hypertension, and normotension versus hypertension trials were 72.97%, 81.82%, and 92.31%, respectively. In summary, this study established a PPG characteristic analysis model and established the intrinsic relationship between SBP and PPG characteristics. Finally, the risk stratification of hypertension at different stages was examined and compared based on the optimal feature subset.
Collapse
|
6
|
Liang Y, Chen Z, Ward R, Elgendi M. Hypertension Assessment via ECG and PPG Signals: An Evaluation Using MIMIC Database. Diagnostics (Basel) 2018; 8:E65. [PMID: 30201887 PMCID: PMC6163274 DOI: 10.3390/diagnostics8030065] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 09/07/2018] [Accepted: 09/07/2018] [Indexed: 12/13/2022] Open
Abstract
Cardiovascular diseases (CVDs) have become the biggest threat to human health, and they are accelerated by hypertension. The best way to avoid the many complications of CVDs is to manage and prevent hypertension at an early stage. However, there are no symptoms at all for most types of hypertension, especially for prehypertension. The awareness and control rates of hypertension are extremely low. In this study, a novel hypertension management method based on arterial wave propagation theory and photoplethysmography (PPG) morphological theory was researched to explore the physiological changes in different blood pressure (BP) levels. Pulse Arrival Time (PAT) and photoplethysmogram (PPG) features were extracted from electrocardiogram (ECG) and PPG signals to represent the arterial wave propagation theory and PPG morphological theory, respectively. Three feature sets, one containing PAT only, one containing PPG features only, and one containing both PAT and PPG features, were used to classify the different BP categories, defined as normotension, prehypertension, and hypertension. PPG features were shown to classify BP categories more accurately than PAT. Furthermore, PAT and PPG combined features improved the BP classification performance. The F1 scores to classify normotension versus prehypertension reached 84.34%, the scores for normotension versus hypertension reached 94.84%, and the scores for normotension plus prehypertension versus hypertension reached 88.49%. This indicates that the simultaneous collection of ECG and PPG signals could detect hypertension.
Collapse
Affiliation(s)
- Yongbo Liang
- School of Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China.
| | - Zhencheng Chen
- School of Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China.
| | - Rabab Ward
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
| | - Mohamed Elgendi
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
- Department of Obstetrics & Gynecology, University of British Columbia, Vancouver, BC V6Z 2K8, Canada.
- BC Children's & Women's Hospital, Vancouver, BC V6H 3N1, Canada.
| |
Collapse
|
7
|
Liang Y, Elgendi M, Chen Z, Ward R. An optimal filter for short photoplethysmogram signals. Sci Data 2018; 5:180076. [PMID: 29714722 PMCID: PMC5928853 DOI: 10.1038/sdata.2018.76] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 03/27/2018] [Indexed: 12/16/2022] Open
Abstract
A photoplethysmogram (PPG) contains a wealth of cardiovascular system information, and with the development of wearable technology, it has become the basic technique for evaluating cardiovascular health and detecting diseases. However, due to the varying environments in which wearable devices are used and, consequently, their varying susceptibility to noise interference, effective processing of PPG signals is challenging. Thus, the aim of this study was to determine the optimal filter and filter order to be used for PPG signal processing to make the systolic and diastolic waves more salient in the filtered PPG signal using the skewness quality index. Nine types of filters with 10 different orders were used to filter 219 (2.1s) short PPG signals. The signals were divided into three categories by PPG experts according to their noise levels: excellent, acceptable, or unfit. Results show that the Chebyshev II filter can improve the PPG signal quality more effectively than other types of filters and that the optimal order for the Chebyshev II filter is the 4th order.
Collapse
Affiliation(s)
- Yongbo Liang
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, V6T 1Z4, Canada.,School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, PR China.,School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, PR China
| | - Mohamed Elgendi
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, V6T 1Z4, Canada.,Faculty of Medicine, University of British Columbia, Vancouver, V6T 1Z3, Canada.,BC Children's & Women's Hospital, Vancouver, V6H 3N1, Canada
| | - Zhencheng Chen
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, PR China.,School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, PR China
| | - Rabab Ward
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, V6T 1Z4, Canada
| |
Collapse
|
8
|
Elgendi M. TERMA Framework for Biomedical Signal Analysis: An Economic-Inspired Approach. BIOSENSORS 2016; 6:E55. [PMID: 27827852 PMCID: PMC5192375 DOI: 10.3390/bios6040055] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Revised: 10/20/2016] [Accepted: 10/25/2016] [Indexed: 11/17/2022]
Abstract
Biomedical signals contain features that represent physiological events, and each of these events has peaks. The analysis of biomedical signals for monitoring or diagnosing diseases requires the detection of these peaks, making event detection a crucial step in biomedical signal processing. Many researchers have difficulty detecting these peaks to investigate, interpret and analyze their corresponding events. To date, there is no generic framework that captures these events in a robust, efficient and consistent manner. A new method referred to for the first time as two event-related moving averages ("TERMA") involves event-related moving averages and detects events in biomedical signals. The TERMA framework is flexible and universal and consists of six independent LEGO building bricks to achieve high accuracy detection of biomedical events. Results recommend that the window sizes for the two moving averages ( W 1 and W 2 ) have to follow the inequality ( 8 × W 1 ) ≥ W 2 ≥ ( 2 × W 1 ) . Moreover, TERMA is a simple yet efficient event detector that is suitable for wearable devices, point-of-care devices, fitness trackers and smart watches, compared to more complex machine learning solutions.
Collapse
Affiliation(s)
- Mohamed Elgendi
- Department of Obstetrics & Gynecology, University of British Columbia, Vancouver, BC V6Z 2K5, Canada.
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
| |
Collapse
|
9
|
Elgendi M, Meo M, Abbott D. A Proof-of-Concept Study: Simple and Effective Detection of P and T Waves in Arrhythmic ECG Signals. Bioengineering (Basel) 2016; 3:bioengineering3040026. [PMID: 28952588 PMCID: PMC5597269 DOI: 10.3390/bioengineering3040026] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 10/12/2016] [Accepted: 10/14/2016] [Indexed: 11/16/2022] Open
Abstract
A robust and numerically-efficient method based on two moving average filters, followed by a dynamic event-related threshold, has been developed to detect P and T waves in electrocardiogram (ECG) signals as a proof-of-concept. Detection of P and T waves is affected by the quality and abnormalities in ECG recordings; the proposed method can detect P and T waves simultaneously through a unique algorithm despite these challenges. The algorithm was tested on arrhythmic ECG signals extracted from the MIT-BIH arrhythmia database with 21,702 beats. These signals typically suffer from: (1) non-stationary effects; (2) low signal-to-noise ratio; (3) premature atrial complexes; (4) premature ventricular complexes; (5) left bundle branch blocks; and (6) right bundle branch blocks. Interestingly, our algorithm obtained a sensitivity of 98.05% and a positive predictivity of 97.11% for P waves, and a sensitivity of 99.86% and a positive predictivity of 99.65% for T waves. These results, combined with the simplicity of the method, demonstrate that an efficient and simple algorithm can suit portable, wearable, and battery-operated ECG devices.
Collapse
Affiliation(s)
- Mohamed Elgendi
- Department of Obstetrics & Gynecology, University of British Columbia and BC Children's & Women's Hospital, Vancouver, BC V6H 3N1, Canada.
| | - Marianna Meo
- Electrophysiology and Heart Modeling Institute, (IHU LIRYC), Bordeaux 33604, France.
| | - Derek Abbott
- School of Electrical and Electronics Engineering, University of Adelaide, Adelaide SA 5005, Australia.
| |
Collapse
|