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Varaki ES, Gargiulo GD, Malone M, Breen PP. Arterial and venous peripheral vascular assessment using wearable electro-resistive morphic sensors. Sci Rep 2024; 14:1327. [PMID: 38225286 PMCID: PMC10789795 DOI: 10.1038/s41598-023-50534-1] [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: 01/13/2023] [Accepted: 12/21/2023] [Indexed: 01/17/2024] Open
Abstract
Peripheral vascular diseases (PVDs) represent a significant burden on global human health and healthcare systems. With continued growth in obesity and diabetes, it is likely that the incidence of these conditions will increase. As many PVDs remain undiagnosed, low-cost and easy to use diagnostic methods are required. This work uses newly developed wearable electro-resistive morphic sensors to assess venous and arterial competence in the lower limbs of 36 healthy subjects. Comparison of this HeMo device was made to currently available benchtop light reflection rheography and photoplethymography devices. Results indicate that HeMo can detect the physiological signals of interest for both chronic venous insufficiency and peripheral arterial disease and all subjects were interpreted as healthy by each system. However, measurement repeatability of HeMo was highlighted as an issue that requires further system development. Furthermore, as HeMo captures changes in a section of limb circumference due to changes in underlying blood movement, rather than at a single point, the recorded signal is typically damped by comparison. This factor should be considered in any future developments.
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Affiliation(s)
- Elham Shabani Varaki
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, Australia
| | - Gaetano D Gargiulo
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, Australia
- School of Engineering, Design and Built Environment, Western Sydney University, Sydney, Australia
| | - Matthew Malone
- South Western Sydney Limb Preservation and Wound Research, Liverpool Hospital, South Western Sydney Local Health District, Liverpool, Australia
- Infectious Diseases and Microbiology, School of Medicine, Western Sydney University, Sydney, Australia
| | - Paul P Breen
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, Australia.
- Translational Health Research Institute, Western Sydney University, Sydney, Australia.
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2
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Park J, Seok HS, Kim SS, Shin H. Photoplethysmogram Analysis and Applications: An Integrative Review. Front Physiol 2022; 12:808451. [PMID: 35300400 PMCID: PMC8920970 DOI: 10.3389/fphys.2021.808451] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 12/21/2021] [Indexed: 12/03/2022] Open
Abstract
Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.
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Affiliation(s)
- Junyung Park
- Department of Biomedical Engineering, Chonnam National University, Yeosu, South Korea
| | - Hyeon Seok Seok
- Department of Biomedical Engineering, Chonnam National University, Yeosu, South Korea
| | - Sang-Su Kim
- Department of Biomedical Engineering, Chonnam National University, Yeosu, South Korea
| | - Hangsik Shin
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
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3
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Xing X, Ma Z, Xu S, Zhang M, Zhao W, Song M, Dong WF. Blood pressure assessment with in-ear photoplethysmography. Physiol Meas 2021; 42. [PMID: 34571491 DOI: 10.1088/1361-6579/ac2a71] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/27/2021] [Indexed: 11/11/2022]
Abstract
Objective. In this study, we aimed to estimate blood pressure (BP) from in-ear photoplethysmography (PPG). This novel implementation provided an unobtrusive and steady way of recording PPG, whereas previous PPG measurements were mostly performed at the wrist, finger, or earlobe.Methods. The time between forward and reflected PPG waves was very short at the ear site. To minimize errors introduced by feature extraction, a multi-Gaussian decomposition of in-ear PPG was performed. Both hand-crafted and whole-based features were extracted and the best combination of features was selected using a backward-search wrapper method and evaluated by the Akaike information criteria. Hemodynamic parameters such as compliance and inertance were estimated from a four-element Windkessel (WK4) model, which was used to pre-classify PPG signals and generate different BP estimation algorithms. Calibration was done by using previous measurements from the same class. To validate this novel approach, 53 subjects were recruited for a one-month follow-up study, and 17 subjects were recruited for a two-month follow-up study. Calibrated systolic BP estimation accuracy was significantly improved with inertance-based pre-classification, while diastolic BP showed less improvement.Results. With proper feature selection, pre-classification and calibration, we have achieved a mean absolute error of 5.35 mmHg for SBP estimation, compared to 6.16 mmHg if no pre-classification was carried out. The performance did not deteriorate in two months, showing a decent BP trend-tracking ability.Conclusion. The study demonstrated the feasibility of in-ear PPG to reliably measure BP, which represents an important technological advancement in terms of unobtrusiveness and steadiness.
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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, Jiangsu, People's Republic of China.,Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, People's Republic of China
| | - Zhimin Ma
- The Affiliated Suzhou Science &Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu, People's Republic of China
| | - Shengkai Xu
- The Affiliated Suzhou Science &Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu, People's Republic of China
| | - Mingyou Zhang
- The First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Wei Zhao
- Department of Otolaryngology-Head and Neck Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Mingxuan Song
- Jinan Guoke Medical Technology Development Co., Ltd, Shandong, People's Republic of China
| | - Wen-Fei Dong
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, People's Republic of China
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El Hajj C, Kyriacou PA. Recurrent Neural Network Models for Blood Pressure Monitoring Using PPG Morphological Features. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:1865-1868. [PMID: 34891651 DOI: 10.1109/embc46164.2021.9630319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Continuous non-invasive Blood Pressure (BP) monitoring is vital for the early detection and control of hypertension. However, this is yet not possible as all current non-invasive BP devices are cuff-based devices and hence precluding continuous monitoring. Several methods have been proposed to overcome this challenge, one of which utilises the Photoplethysmograph (PPG) signal in an effort to predict reliable BP values from this signal using various computational approaches. Although, good performance has been reported in the literature, it was mainly achieved on a small inadequate sample size using conventional models that are unable to account for the temporal variations in the input vector. To address these limitations, this paper proposes cuff-less and continuous blood pressure estimation using Long Short-term Memory (LSTM) and Gated Recurrent Units (GRU). The models were evaluated on 942 patients acquired from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC II) dataset. The proposed models produced superior results in comparison with conventional artificial neural network. In particular, the best performance was achieved by the GRU, with mean absolute error and standard deviation of 5.77 ± 8.52 mmHg and 3.33±5.02 mmHg for systolic (SBP) and diastolic blood pressure (DBP), respectively. Furthermore, the results comply with the international standards for cuff-less blood pressure estimation.
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Pelaez-Coca MD, Hernando A, Lozano MT, Sanchez C, Izquierdo D, Gil E. Photoplethysmographic Waveform and Pulse Rate Variability Analysis in Hyperbaric Environments. IEEE J Biomed Health Inform 2021; 25:1550-1560. [PMID: 32870804 DOI: 10.1109/jbhi.2020.3020743] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The main aim of this work is to identify alterations in the morphology of the pulse photoplethysmogram (PPG) signal, due to the exposure of the subjects to a hyperbaric environment. Additionally, their Pulse Rate Variability (PRV) is analysed to characterise the response of their Autonomic Nervous System (ANS). To do that, 28 volunteers are introduced into a hyperbaric chamber and five sequential stages with different atmospheric pressures from 1 atm to 5 atm are performed. In this work, nineteen morphological parameters of the PPG signal are analysed: the pulse amplitude; eight parameters related to pulse width; eight parameters related to pulse area; and the two two pulse slopes. Also, classical time and frequency parameters of PRV are computed. Notable widening of the pulses width is observed in the stages analysed. The PPG area increases with pressure, with no significant changes when the initial pressure is recovered. These changes in PPG waveform may be caused by an increase in the systemic vascular resistance as a consequence of of vasoconstriction in the extremities, suggesting a sympathetic activation. However, the PRV results show an augmented parasympathetic activity and a reduction in the parameters that characterise the sympathetic response. So, only a sympathetic activation is detected in the peripheral region, as reflected by PPG morphology. The information regarding the ANS and the cardiovascular response that can be extracted from the PPG signal, as well as its compatibility with wet conditions make this signal the most suitable for studying the physiological response in hyperbaric environments.
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Yang S, Sohn J, Lee S, Lee J, Kim HC. Estimation and Validation of Arterial Blood Pressure Using Photoplethysmogram Morphology Features in Conjunction With Pulse Arrival Time in Large Open Databases. IEEE J Biomed Health Inform 2021; 25:1018-1030. [PMID: 32750963 DOI: 10.1109/jbhi.2020.3009658] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Although various predictors and methods for BP estimation have been proposed, differences in study designs have led to difficulties in determining the optimal method. This study presents analyses of BP estimation methods using 2.4 million cardiac cycles of two commonly used non-invasive biosignals, electrocardiogram (ECG) and photoplethysmogram (PPG), from 1376 surgical patients. Feature selection methods were used to determine the best subset of predictors from a total of 42 including PAT, heart rate (HR), and various PPG morphology features, and BP estimation models constructed using linear regression (LR), random forest (RF), artificial neural network (ANN), and recurrent neural network (RNN) were evaluated. 28 features out of 42 were determined as suitable for BP estimation, in particular two PPG morphology features outperformed PAT, which has been conventionally seen as the best non-invasive indicator of BP. By modelling the low frequency component of BP using ANN and the high frequency component using RNN with the selected predictors, mean errors of 0.05 ± 6.92 mmHg for systolic BP, and -0.05 ± 3.99 mmHg for diastolic BP were achieved. External validation of the model using another biosignal database consisting of 334 intensive care unit patients led to similar results, satisfying three standards for accuracy of BP monitors. The results indicate that the proposed method can contribute to the realization of ubiquitous non-invasive continuous BP monitoring.
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7
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Cuff-less blood pressure estimation from photoplethysmography signal and electrocardiogram. Phys Eng Sci Med 2021; 44:397-408. [PMID: 33738778 DOI: 10.1007/s13246-021-00989-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Accepted: 03/04/2021] [Indexed: 10/21/2022]
Abstract
In recent studies, the physiological parameters derived from human vital signals are found as the status response of the heart and arteries. In this paper, we therefore firstly attempt to extract abundant vital features from photoplethysmography(PPG) signal, its multivariate derivative signals and Electrocardiogram(ECG) signal, which are verified its statistical significance in BP estimation through statistical analysis t-test. Afterwards, the optimal feature set are obtained by usnig mutual information coefficient analysis, which could investigate the potential associations with blood pressure. The optimized feature set are aid as an input to various machine learning strategies for BP estimation. The results indicates that AdaBoost based BP estimation model outperforms other regression methods. Concurrently, AdaBoost-based model is further analyzed by using the Histograms of Estimation Error and Bland-Altman Plot. The results also indicate the great BP estimation performance of the proposed BP estimation method, and it stays within the Advancement of Medical Instrumention(AAMI) standard. Regarding the British Hypertension Society (BHS), it achieves the grade A for DBP and grade B for MAP. Besides, the experimental result illustrated that our proposed BP estimation method could reduce the MAE and the STD, and improve the r for SBP, MAP and DBP estimation, respectively, which further demonstrates the feasibility of our proposed BP estimation method in this paper.
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8
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Deep learning models for cuffless blood pressure monitoring from PPG signals using attention mechanism. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102301] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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9
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Shin H, Park J, Seok HS, Kim SS. Photoplethysmogram analysis and applications: An Integrative Review (Preprint). JMIR BIOMEDICAL ENGINEERING 2020. [DOI: 10.2196/25567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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10
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Papini GB, Fonseca P, Eerikäinen LM, Overeem S, Bergmans JWM, Vullings R. Sinus or not: a new beat detection algorithm based on a pulse morphology quality index to extract normal sinus rhythm beats from wrist-worn photoplethysmography recordings. Physiol Meas 2018; 39:115007. [PMID: 30475748 DOI: 10.1088/1361-6579/aae7f8] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Wrist-worn photoplethysmography (PPG) can enable free-living physiological monitoring of people during diverse activities, ranging from sleep to physical exercise. In many applications, it is important to remove the pulses not related to sinus rhythm beats from the PPG signal before using it as a cardiovascular descriptor. In this manuscript, we propose an algorithm to assess the morphology of the PPG signal in order to reject non-sinus rhythm pulses, such as artefacts or pulses related to arrhythmic beats. APPROACH The algorithm segments the PPG signal into individual pulses and dynamically evaluates their morphological likelihood of being normal sinus rhythm pulses via a template-matching approach that accounts for the physiological variability of the signal. The normal sinus rhythm likelihood of each pulse is expressed as a quality index that can be employed to reject artefacts and pulses related to arrhythmic beats. MAIN RESULTS Thresholding the pulse quality index enables near-perfect detection of normal sinus rhythm beats by rejecting most of the non-sinus rhythm pulses (positive predictive value 98%-99%), both in healthy subjects and arrhythmic patients. The rejection of arrhythmic beats is almost complete (sensitivity to arrhythmic beats 7%-3%), while the sensitivity to sinus rhythm beats is not compromised (96%-91%). SIGNIFICANCE The developed algorithm consistently detects normal sinus rhythm beats in a PPG signal by rejecting artefacts and, as a first of its kind, arrhythmic beats. This increases the reliability in the extraction of features which are adversely influenced by the presence of non-sinus pulses, whether related to inter-beat intervals or to pulse morphology, from wrist-worn PPG signals recorded in free-living conditions.
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Affiliation(s)
- Gabriele B Papini
- Department of Electrical Engineering, TU/e, Den Dolech 2, 5612 AZ Eindhoven, Netherlands. Philips Research, High Tech Campus, 5656 AE Eindhoven, Netherlands. Kempenhaeghe Foundation, Sleep Medicine Centre, PO Box 61, 5590 AB Heeze, Netherlands
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11
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Pelaez MDC, Albalate MTL, Sanz AH, Valles MA, Gil E. Photoplethysmographic Waveform Versus Heart Rate Variability to Identify Low-Stress States: Attention Test. IEEE J Biomed Health Inform 2018; 23:1940-1951. [PMID: 30452382 DOI: 10.1109/jbhi.2018.2882142] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Our long-term goal is the development of an automatic identifier of attentional states. In order to accomplish it, we should first be able to identify different states based on physiological signals. So, the first aim of this paper is to identify the most appropriate features to detect a subject's high performance state. For that, a database of electrocardiographic (ECG) and photoplethysmographic (PPG) signals is recorded in two unequivocally defined states (rest and attention task) from up to 50 subjects as a sample of the population. Time and frequency parameters of heart/pulse rate variability have been computed from the ECG/PPG signals, respectively. Additionally, the respiratory rate has been estimated from both signals and also six morphological parameters from PPG. In total, 26 features are obtained for each subject. They provide information about the autonomic nervous system and the physiological response of the subject to an attention demand task. Results show an increase of sympathetic activation when the subjects perform the attention test. The amplitude and width of the PPG pulse were more sensitive than the classical sympathetic markers ([Formula: see text] and [Formula: see text]) for identifying this attentional state. State classification accuracy reaches a mean of [Formula: see text], a maximum of [Formula: see text], and a minimum of 85%, in the 100 classifications made by only selecting four parameters extracted from the PPG signal (pulse amplitude, pulsewidth, pulse downward slope, and mean pulse rate). These results suggest that attentional states could be identified by PPG.
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12
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Shukla SN. Estimation of blood pressure from non-invasive data. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:1772-1775. [PMID: 29060231 DOI: 10.1109/embc.2017.8037187] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Blood pressure (BP) is one of the most important physiological parameter that can provide crucial information for health care. The widely used cuff based technology is not very convenient or comfortable as it occludes the blood flow in the arteries during the time of measurement. In past, Phonocardiogram (PCG), Electrocardiogram (ECG) and Photoplethysmogram (PPG) signals have been used to predict the BP values. In this paper, we propose to estimate the blood pressure from PPG using Multi Task Gaussian Processes (MTGPs) and compare with Artificial Neural networks (ANNs). Both MTGPs and ANNs are evaluated on the clinical data obtained from MIMIC Database. The performance of the proposed method is found to be comparable or better than the existing methods of computing BP from non-invasive data.
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13
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A Fast Multimodal Ectopic Beat Detection Method Applied for Blood Pressure Estimation Based on Pulse Wave Velocity Measurements in Wearable Sensors. SENSORS 2017; 17:s17010158. [PMID: 28098831 PMCID: PMC5298731 DOI: 10.3390/s17010158] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Revised: 01/07/2017] [Accepted: 01/09/2017] [Indexed: 01/08/2023]
Abstract
Automatic detection of ectopic beats has become a thoroughly researched topic, with literature providing manifold proposals typically incorporating morphological analysis of the electrocardiogram (ECG). Although being well understood, its utilization is often neglected, especially in practical monitoring situations like online evaluation of signals acquired in wearable sensors. Continuous blood pressure estimation based on pulse wave velocity considerations is a prominent example, which depends on careful fiducial point extraction and is therefore seriously affected during periods of increased occurring extrasystoles. In the scope of this work, a novel ectopic beat discriminator with low computational complexity has been developed, which takes advantage of multimodal features derived from ECG and pulse wave relating measurements, thereby providing additional information on the underlying cardiac activity. Moreover, the blood pressure estimations’ vulnerability towards ectopic beats is closely examined on records drawn from the Physionet database as well as signals recorded in a small field study conducted in a geriatric facility for the elderly. It turns out that a reliable extrasystole identification is essential to unsupervised blood pressure estimation, having a significant impact on the overall accuracy. The proposed method further convinces by its applicability to battery driven hardware systems with limited processing power and is a favorable choice when access to multimodal signal features is given anyway.
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14
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Madhan Mohan P, Nagarajan V, Vignesh JC. Spot measurement of heart rate based on morphology of PhotoPlethysmoGraphic (PPG) signals. J Med Eng Technol 2016; 41:87-96. [DOI: 10.1080/03091902.2016.1223198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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15
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Zaidi SN, Collins SM. Orthostatic stress and area under the curve of photoplethysmography waveform. Biomed Phys Eng Express 2016. [DOI: 10.1088/2057-1976/2/4/045006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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16
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Singh RR, Conjeti S, Banerjee R. Assessment of Driver Stress from Physiological Signals collected under Real-Time Semi-Urban Driving Scenarios. INT J COMPUT INT SYS 2014. [DOI: 10.1080/18756891.2013.864478] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
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17
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Singh RR, Conjeti S, Banerjee R. A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals. Biomed Signal Process Control 2013. [DOI: 10.1016/j.bspc.2013.06.014] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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18
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Elgendi M. On the analysis of fingertip photoplethysmogram signals. Curr Cardiol Rev 2013; 8:14-25. [PMID: 22845812 PMCID: PMC3394104 DOI: 10.2174/157340312801215782] [Citation(s) in RCA: 411] [Impact Index Per Article: 37.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2012] [Revised: 04/12/2012] [Accepted: 04/13/2012] [Indexed: 11/22/2022] Open
Abstract
Photoplethysmography (PPG) is used to estimate the skin blood flow using infrared light. Researchers from different domains of science have become increasingly interested in PPG because of its advantages as non-invasive, inexpensive, and convenient diagnostic tool. Traditionally, it measures the oxygen saturation, blood pressure, cardiac output, and for assessing autonomic functions. Moreover, PPG is a promising technique for early screening of various atherosclerotic pathologies and could be helpful for regular GP-assessment but a full understanding of the diagnostic value of the different features is still lacking. Recent studies emphasise the potential information embedded in the PPG waveform signal and it deserves further attention for its possible applications beyond pulse oximetry and heart-rate calculation. Therefore, this overview discusses different types of artifact added to PPG signal, characteristic features of PPG waveform, and existing indexes to evaluate for diagnoses.
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Affiliation(s)
- Mohamed Elgendi
- School of Engineering and Information Technology, Charles Darwin University, Australia.
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19
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Hsiu H, Chao PT, Chiang WR, Hsu CL, Jan MY, Wang WK, Wang YYL. EFFECT OF PULSE NUMBER ON THE PULSE-BASED SYNCHRONIZED-AVERAGING ANALYSIS IN LASER DOPPLER SIGNAL. BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS 2012. [DOI: 10.4015/s1016237207000239] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Pulse parameters calculated from the LDF waveform based on a time-domain synchronized averaging analysis were shown to be able to discriminate the difference in microvascular resistance. However, its applicability may depend on the validation of signal stationarity. In this study, our aim is to investigate the effect of pulse number, which may destroy the signal stationarity, on various pulse LDF parameters. Analysis was performed in data obtained on healthy volunteers. When one pulse parameter is deviated from the standard value for more than 10%, it was regarded as an error; EP (error probability) was then defined as the occurring probability of error. It was revealed in this study that average parameter deviations for FDT and PDT were smaller than 5% for all tested pulse numbers. If we set 10% as the parameter-deviation criterion as well as the acceptable EP range, there should be at least 120 pulses for FDT and PDT, and 210 pulses for FNA and PW. The study presented here has established the criteria for appropriate pulse number to achieve the signal stationarity; we can thus get accurate pulse parameters so that the microcirculatory discriminability of the pulse-based time-averaging analysis on LDF signal can be improved. The proposed quantitative method to verify the validation of signal stationarity when utilizing time-averaging can also be applied to analysis of other bio-signals.
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Affiliation(s)
- Hsin Hsiu
- Department of Electrical Engineering, Yuan Ze University, Taoyuan, Taiwan
| | - Pin-Tsun Chao
- Biophysics Lab, Institute of Physics, Academia Sinica, Taipei, Taiwan
| | - Wen-Rei Chiang
- Department of Electrical Engineering, Yuan Ze University, Taoyuan, Taiwan
| | - Chia-Liang Hsu
- Department of Electrical Engineering, Yuan Ze University, Taoyuan, Taiwan
| | - Ming-Yie Jan
- Biophysics Lab, Institute of Physics, Academia Sinica, Taipei, Taiwan
| | - Wei-Kung Wang
- Biophysics Lab, Institute of Physics, Academia Sinica, Taipei, Taiwan
| | - Yuh-Ying Lin Wang
- Biophysics Lab, Institute of Physics, Academia Sinica, Taipei, Taiwan
- Department of Physics, National Taiwan Normal University, Taipei, Taiwan
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van Houdt PJ, Ossenblok PPW, Boon PAJM, Leijten FSS, Velis DN, Stam CJ, de Munck JC. Correction for pulse height variability reduces physiological noise in functional MRI when studying spontaneous brain activity. Hum Brain Mapp 2010; 31:311-25. [PMID: 19662656 DOI: 10.1002/hbm.20866] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
EEG correlated functional MRI (EEG-fMRI) allows the delineation of the areas corresponding to spontaneous brain activity, such as epileptiform spikes or alpha rhythm. A major problem of fMRI analysis in general is that spurious correlations may occur because fMRI signals are not only correlated with the phenomena of interest, but also with physiological processes, like cardiac and respiratory functions. The aim of this study was to reduce the number of falsely detected activated areas by taking the variation in physiological functioning into account in the general linear model (GLM). We used the photoplethysmogram (PPG), since this signal is based on a linear combination of oxy- and deoxyhemoglobin in the arterial blood, which is also the basis of fMRI. We derived a regressor from the variation in pulse height (VIPH) of PPG and added this regressor to the GLM. When this regressor was used as predictor it appeared that VIPH explained a large part of the variance of fMRI signals acquired from five epilepsy patients and thirteen healthy volunteers. As a confounder VIPH reduced the number of activated voxels by 30% for the healthy volunteers, when studying the generators of the alpha rhythm. Although for the patients the number of activated voxels either decreased or increased, the identification of the epileptogenic zone was substantially enhanced in one out of five patients, whereas for the other patients the effects were smaller. In conclusion, applying VIPH as a confounder diminishes physiological noise and allows a more reliable interpretation of fMRI results.
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Affiliation(s)
- Petra J van Houdt
- Department of Research and Development, Kempenhaeghe, Heeze, The Netherlands.
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21
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Sensorik und Monitoring. BIOMED ENG-BIOMED TE 2010. [DOI: 10.1515/bmt.2010.713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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22
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Xu L, Meng MQH, Liu R, Wang K. Robust peak detection of pulse waveform using height ratio. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2009; 2008:3856-9. [PMID: 19163554 DOI: 10.1109/iembs.2008.4650051] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The automatic recognition of pulse waveform is the key in the research of computerized pulse diagnosis. The peak detection is important in the extraction of the features of pulse waveform in time domain. In this paper, we proposed a robust peak detection algorithm of pulse waveform based on the ratio of peak height. The results demonstrate that the proposed algorithm is robust against unspecific noise and baseline drift of pulse waveform. The proposed algorithm can be applied into other quasi-periodic physiological signals too.
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Affiliation(s)
- Lisheng Xu
- School of Control Science and Engineering, Shandong University, Jinan, China.
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23
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Abstract
Photoplethysmography (PPG) is a simple and low-cost optical technique that can be used to detect blood volume changes in the microvascular bed of tissue. It is often used non-invasively to make measurements at the skin surface. The PPG waveform comprises a pulsatile ('AC') physiological waveform attributed to cardiac synchronous changes in the blood volume with each heart beat, and is superimposed on a slowly varying ('DC') baseline with various lower frequency components attributed to respiration, sympathetic nervous system activity and thermoregulation. Although the origins of the components of the PPG signal are not fully understood, it is generally accepted that they can provide valuable information about the cardiovascular system. There has been a resurgence of interest in the technique in recent years, driven by the demand for low cost, simple and portable technology for the primary care and community based clinical settings, the wide availability of low cost and small semiconductor components, and the advancement of computer-based pulse wave analysis techniques. The PPG technology has been used in a wide range of commercially available medical devices for measuring oxygen saturation, blood pressure and cardiac output, assessing autonomic function and also detecting peripheral vascular disease. The introductory sections of the topical review describe the basic principle of operation and interaction of light with tissue, early and recent history of PPG, instrumentation, measurement protocol, and pulse wave analysis. The review then focuses on the applications of PPG in clinical physiological measurements, including clinical physiological monitoring, vascular assessment and autonomic function.
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Affiliation(s)
- John Allen
- Regional Medical Physics Department, Freeman Hospital, Newcastle upon Tyne, UK.
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