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Maki KA, Goodyke MP, Rasmussen K, Bronas UG. An Integrative Literature Review of Heart Rate Variability Measures to Determine Autonomic Nervous System Responsiveness Using Pharmacological Manipulation. J Cardiovasc Nurs 2024; 39:58-78. [PMID: 37249528 PMCID: PMC10684820 DOI: 10.1097/jcn.0000000000001001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
BACKGROUND Heart rate variability (HRV) is defined as the difference in the timing of intervals between successive heartbeats and is used as a surrogate measure to the responsiveness of the autonomic nervous system. A review and synthesis of HRV as an indicator of autonomic nervous system responsiveness to pharmacologic stimulation/blockade of sympathetic and/or parasympathetic nervous system branches have not been completed. PURPOSE The aim of this integrative review is to synthesize research examining pharmacological modulation of the autonomic nervous system and the response of time domain, frequency domain, and nonlinear measures of HRV. CONCLUSIONS Sympathetic nervous system blockade resulted in a consistent decrease in the standard deviation of normal-normal interval metric across studies. Stimulation of the parasympathetic nervous system was associated with an increase in several time, frequency, and nonlinear HRV indices, whereas blockade of the parasympathetic nervous system led to a decrease in similar indices. CLINICAL IMPLICATIONS Recommendations to improve the reproducibility of future HRV research are provided for standardization of recording, analysis, and metric decisions and more thorough reporting of HRV indices in published studies. Alterations in autonomic nervous system input to the cardiovascular system are associated with an increased risk for adverse patient outcomes and increased mortality; therefore, understanding the influence of pharmacologic autonomic nervous system modulation on HRV indices and important considerations for reproducible HRV research design will inform future translational research on cardiovascular risk reduction.
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Affiliation(s)
- Katherine A. Maki
- Translational Biobehavioral and Health Disparities Branch, National Institutes of Health, Clinical Center, Bethesda, MD, 20814
- University of Illinois at Chicago, College of Nursing, Department of Biobehavioral Nursing Science, Chicago, IL, 60612
| | - Madison P. Goodyke
- University of Illinois at Chicago, College of Nursing, Department of Biobehavioral Nursing Science, Chicago, IL, 60612
| | - Kendra Rasmussen
- The Johns Hopkins Hospital, Nursing Department, Baltimore, MD, 21287
| | - Ulf G. Bronas
- University of Illinois at Chicago, College of Nursing, Department of Biobehavioral Nursing Science, Chicago, IL, 60612
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Plain B, Pielage H, Kramer SE, Richter M, Saunders GH, Versfeld NJ, Zekveld AA, Bhuiyan TA. Combining Cardiovascular and Pupil Features Using k-Nearest Neighbor Classifiers to Assess Task Demand, Social Context, and Sentence Accuracy During Listening. Trends Hear 2024; 28:23312165241232551. [PMID: 38549351 PMCID: PMC10981225 DOI: 10.1177/23312165241232551] [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: 03/05/2023] [Revised: 01/04/2024] [Accepted: 01/25/2024] [Indexed: 04/01/2024] Open
Abstract
In daily life, both acoustic factors and social context can affect listening effort investment. In laboratory settings, information about listening effort has been deduced from pupil and cardiovascular responses independently. The extent to which these measures can jointly predict listening-related factors is unknown. Here we combined pupil and cardiovascular features to predict acoustic and contextual aspects of speech perception. Data were collected from 29 adults (mean = 64.6 years, SD = 9.2) with hearing loss. Participants performed a speech perception task at two individualized signal-to-noise ratios (corresponding to 50% and 80% of sentences correct) and in two social contexts (the presence and absence of two observers). Seven features were extracted per trial: baseline pupil size, peak pupil dilation, mean pupil dilation, interbeat interval, blood volume pulse amplitude, pre-ejection period and pulse arrival time. These features were used to train k-nearest neighbor classifiers to predict task demand, social context and sentence accuracy. The k-fold cross validation on the group-level data revealed above-chance classification accuracies: task demand, 64.4%; social context, 78.3%; and sentence accuracy, 55.1%. However, classification accuracies diminished when the classifiers were trained and tested on data from different participants. Individually trained classifiers (one per participant) performed better than group-level classifiers: 71.7% (SD = 10.2) for task demand, 88.0% (SD = 7.5) for social context, and 60.0% (SD = 13.1) for sentence accuracy. We demonstrated that classifiers trained on group-level physiological data to predict aspects of speech perception generalized poorly to novel participants. Individually calibrated classifiers hold more promise for future applications.
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Affiliation(s)
- Bethany Plain
- Otolaryngology Head and Neck Surgery, Ear & Hearing, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- Eriksholm Research Centre, Snekkersten, Denmark
| | - Hidde Pielage
- Otolaryngology Head and Neck Surgery, Ear & Hearing, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- Eriksholm Research Centre, Snekkersten, Denmark
| | - Sophia E. Kramer
- Otolaryngology Head and Neck Surgery, Ear & Hearing, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Michael Richter
- School of Psychology, Liverpool John Moores University, Liverpool, UK
| | - Gabrielle H. Saunders
- Manchester Centre for Audiology and Deafness (ManCAD), University of Manchester, Manchester, UK
| | - Niek J. Versfeld
- Otolaryngology Head and Neck Surgery, Ear & Hearing, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Adriana A. Zekveld
- Otolaryngology Head and Neck Surgery, Ear & Hearing, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
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Rinkevičius M, Charlton PH, Bailón R, Marozas V. Influence of Photoplethysmogram Signal Quality on Pulse Arrival Time during Polysomnography. SENSORS (BASEL, SWITZERLAND) 2023; 23:2220. [PMID: 36850820 PMCID: PMC9967654 DOI: 10.3390/s23042220] [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: 12/30/2022] [Revised: 02/05/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
Intervals of low-quality photoplethysmogram (PPG) signals might lead to significant inaccuracies in estimation of pulse arrival time (PAT) during polysomnography (PSG) studies. While PSG is considered to be a "gold standard" test for diagnosing obstructive sleep apnea (OSA), it also enables tracking apnea-related nocturnal blood pressure fluctuations correlated with PAT. Since the electrocardiogram (ECG) is recorded synchronously with the PPG during PSG, it makes sense to use the ECG signal for PPG signal-quality assessment. (1) Objective: to develop a PPG signal-quality assessment algorithm for robust PAT estimation, and investigate the influence of signal quality on PAT during various sleep stages and events such as OSA. (2) Approach: the proposed algorithm uses R and T waves from the ECG to determine approximate locations of PPG pulse onsets. The MESA database of 2055 PSG recordings was used for this study. (3) Results: the proportions of high-quality PPG were significantly lower in apnea-related oxygen desaturation (matched-pairs rc = 0.88 and rc = 0.97, compared to OSA and hypopnea, respectively, when p < 0.001) and arousal (rc = 0.93 and rc = 0.98, when p < 0.001) than in apnea events. The significantly large effect size of interquartile ranges of PAT distributions was between low- and high-quality PPG (p < 0.001, rc = 0.98), and regular and irregular pulse waves (p < 0.001, rc = 0.74), whereas a lower quality of the PPG signal was found to be associated with a higher interquartile range of PAT across all subjects. Suggested PPG signal quality-based PAT evaluation reduced deviations (e.g., rc = 0.97, rc = 0.97, rc = 0.99 in hypopnea, oxygen desaturation, and arousal stages, respectively, when p < 0.001) and allowed obtaining statistically larger differences between different sleep stages and events. (4) Significance: the implemented algorithm has the potential to increase the robustness of PAT estimation in PSG studies related to nocturnal blood pressure monitoring.
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Affiliation(s)
- Mantas Rinkevičius
- Biomedical Engineering Institute, Kaunas University of Technology, K. Baršausko Str. 59, LT-51423 Kaunas, Lithuania
| | - Peter H. Charlton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 1TN, UK
- Research Centre for Biomedical Engineering, University of London, London WC1E 7HU, UK
| | - Raquel Bailón
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, 50009 Zaragoza, Spain
- Biomedical Research Networking Center (CIBER), 50018 Zaragoza, Spain
| | - Vaidotas Marozas
- Biomedical Engineering Institute, Kaunas University of Technology, K. Baršausko Str. 59, LT-51423 Kaunas, Lithuania
- Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, Studentų Str. 50, LT-51368 Kaunas, Lithuania
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Moscato S, Palmerini L, Palumbo P, Chiari L. Quality Assessment and Morphological Analysis of Photoplethysmography in Daily Life. Front Digit Health 2022; 4:912353. [PMID: 35873348 PMCID: PMC9300860 DOI: 10.3389/fdgth.2022.912353] [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: 04/04/2022] [Accepted: 06/08/2022] [Indexed: 11/13/2022] Open
Abstract
The photoplethysmographic (PPG) signal has been applied in various research fields, with promising results for its future clinical application. However, there are several sources of variability that, if not adequately controlled, can hamper its application in pervasive monitoring contexts. This study assessed and characterized the impact of several sources of variability, such as physical activity, age, sex, and health state on PPG signal quality and PPG waveform parameters (Rise Time, Pulse Amplitude, Pulse Time, Reflection Index, Delta T, and DiastolicAmplitude). We analyzed 31 24 h recordings by as many participants (19 healthy subjects and 12 oncological patients) with a wristband wearable device, selecting a set of PPG pulses labeled with three different quality levels. We implemented a Multinomial Logistic Regression (MLR) model to evaluate the impact of the aforementioned factors on PPG signal quality. We then extracted six parameters only on higher-quality PPG pulses and evaluated the influence of physical activity, age, sex, and health state on these parameters with Generalized Linear Mixed Effects Models (GLMM). We found that physical activity has a detrimental effect on PPG signal quality quality (94% of pulses with good quality when the subject is at rest vs. 9% during intense activity), and that health state affects the percentage of available PPG pulses of the best quality (at rest, 44% for healthy subjects vs. 13% for oncological patients). Most of the extracted parameters are influenced by physical activity and health state, while age significantly impacts two parameters related to arterial stiffness. These results can help expand the awareness that accurate, reliable information extracted from PPG signals can be reached by tackling and modeling different sources of inaccuracy.
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Affiliation(s)
- Serena Moscato
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi” – DEI, University of Bologna, Bologna, Italy
- *Correspondence: Serena Moscato
| | - Luca Palmerini
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi” – DEI, University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Pierpaolo Palumbo
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi” – DEI, University of Bologna, Bologna, Italy
| | - Lorenzo Chiari
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi” – DEI, University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
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Grinevich AA, Gharamyan BG, Chemeris NK. Localization of Mechanisms of Amplitude–Frequency Modulation of Pulse Blood Perfusion of the Soft Tissue Microvasculature. Pilot Study. DOKL BIOCHEM BIOPHYS 2022; 504:118-122. [DOI: 10.1134/s1607672922030036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 02/04/2022] [Accepted: 02/04/2022] [Indexed: 11/23/2022]
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