Relevant bibliographies by topics / Heart Rate Variability

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Heart Rate Variability'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 September 2024

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Journal articles on the topic "Heart Rate Variability"

1

van Ravenswaaij-Arts, Conny M. A. "Heart Rate Variability." Annals of Internal Medicine 118, no. 6 (March 15, 1993): 436. http://dx.doi.org/10.7326/0003-4819-118-6-199303150-00008.

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Ziegler, Michael G. "Heart Rate Variability." Psychosomatic Medicine 83, no. 7 (July 9, 2021): 813–14. http://dx.doi.org/10.1097/psy.0000000000000971.

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Kara, Tomas, Jiri Nykodym, and Virend K. Somers. "Heart Rate Variability:." Journal of Cardiovascular Electrophysiology 14, no. 8 (July 22, 2003): 800–802. http://dx.doi.org/10.1046/j.1540-8167.2003.03258.x.

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Rottman, Jeffrey N., Robert E. Kleiger, and Phyllis K. Stein. "Heart Rate Variability." Cardiology in Review 4, no. 2 (March 1996): 101–11. http://dx.doi.org/10.1097/00045415-199603000-00010.

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Malik, Marek. "Heart rate variability." Current Opinion in Cardiology 13, no. 1 (January 1998): 36–44. http://dx.doi.org/10.1097/00001573-199801000-00006.

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Stauss, Harald M. "Heart rate variability." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 285, no. 5 (November 2003): R927—R931. http://dx.doi.org/10.1152/ajpregu.00452.2003.

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Malik, Marek. "Heart Rate Variability." Annals of Noninvasive Electrocardiology 1, no. 2 (April 1996): 151–81. http://dx.doi.org/10.1111/j.1542-474x.1996.tb00275.x.

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Young, Hayley A., and David Benton. "Heart-rate variability." Behavioural Pharmacology 29 (April 2018): 140–51. http://dx.doi.org/10.1097/fbp.0000000000000383.

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BILCHICK, KENNETH C., and RONALD D. BERGER. "Heart Rate Variability." Journal of Cardiovascular Electrophysiology 17, no. 6 (June 2006): 691–94. http://dx.doi.org/10.1111/j.1540-8167.2006.00501.x.

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Stauss, Harald M. "Heart Rate Variability." Hypertension 64, no. 6 (December 2014): 1184–86. http://dx.doi.org/10.1161/hypertensionaha.114.03949.

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Dissertations / Theses on the topic "Heart Rate Variability"

1

Uhlig, Stefan. "Heart Rate Variability." Doctoral thesis, Universitätsbibliothek Chemnitz, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-233101.

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Ein gesunder Herzschlag zeichnet sich nicht dadurch aus, dass er besonders regelmäßig ist. Vielmehr sollte ein gesunder Herzschlag, selbst in Phasen augenscheinlicher körperlicher Inaktivität, variabel sein (z.B. Appelhans & Luecken, 2006; Berntson et al., 1997; Shaffer, McCraty, & Zerr, 2014). Historisch gesehen ist dies keine völlig neue Erkenntnis – bereits in der frühen chinesischen und griechischen Medizin konnte dieses Phänomen beobachtet werden (einen schönen Überblick hierzu gibt Billman, 2011). Das Zusammenwirken der sympathischen und parasympathischen Bestandteile des autonomen Nervensystems, welches sich unter anderem in der Herzratenvariabilität (HRV) widerspiegelt, erlaubt uns nicht nur Einblicke in die physiologische Adaptionsfähigkeit, sondern auch in die psychische Flexibilität und Regulationsfähigkeit des Menschen, um so auf sich ständig ändernde Umweltanforderungen angemessen reagieren zu können (z.B. Appelhans & Luecken, 2006; Beauchaine, 2001; ChuDuc, NguyenPhan, & NguyenViet, 2013; Porges, 1995b; Quintana & Heathers, 2014; Riganello, Garbarino, & Sannita, 2012; Shaffer et al., 2014; Stein & Kleiger, 1999; Thayer & Lane, 2000). Mit ganz einfachen Worten: Die Variabilität unseres Herzschlages stellt eine Art Interface dar, welches Auskunft über das Zusammenspiel physiologischer und psychologischer Prozesse gibt. In der vorliegenden Monografie beschäftige ich mich intensiv mit dem Thema HRV, insbesondere mit der Anwendung und Durchführung von HRV-Kurzzeitmessungen (meistens fünf Minuten) im Kontext (bio-) psychologischer Forschung. Während ich im Rahmen des ersten Kapitels eine komprimierte Einführung in die Thematik und einen Überblick über die nachfolgenden Kapitel gebe, beschäftigt sich Kapitel II mit der Frage, welche methodischen Standards für HRV-Kurzzeitmessungen derzeit vorliegen. Ausgangspunkt hierfür sind vereinzelte Hinweise (z.B. im Rahmen meta-analytischer Bestrebungen) darauf, dass die Erfassung, Darstellung und Interpretation von HRV-Messungen durch ein nicht unerhebliches Maß an Diversität gekennzeichnet ist (z.B. de Vries, 2013; Ellis, Zhu, Koenig, Thayer, & Wang, 2015; Quintana & Heathers, 2014; Tak et al., 2009; Zahn et al., 2016). Ferner fehlen bis heute belastbare Normwerte für die gängigsten HRV-Parameter, die typischerweise in Kurzzeitmessungen berechnet werden können (vgl. Nunan, Sandercock, & Brodie, 2010). Ausgehend von diesen Beobachtungen stellen wir ein systematisches Literaturreview vor. In einem ersten Schritt haben wir aktuelle Standards zur Erhebung und Auswertung von HRV-Messungen identifiziert, auf deren Basis wir ein Klassifikationssystem zur Beurteilung von HRV-Studien erstellt haben. Nachfolgend wurden zwischen 2000 und 2013 publizierte Artikel (N = 457), im Hinblick auf die extrahierten methodischen Standards, überprüft. Unsere Ergebnisse legen das Vorhandensein einer beträchtlichen methodischen Heterogenität und einen Mangel an wichtigen Informationen nahe (z.B. in Bezug auf die Erhebung essentieller Kontrollvariablen oder das Berichten von HRV-Parametern), einhergehend mit der Tatsache, dass sich gängige Empfehlungen und Richtlinien (z.B. Task Force, 1996) nur partiell in der empirischen Praxis wiederfinden. Auf der Grundlage unserer Ergebnisse leiten wir Empfehlungen für weitere Forschung in diesem Bereich ab, wobei sich unsere „Checkliste“ besonders an forschende Psychologen richtet. Abschließend diskutieren wir die Einschränkungen unseres Reviews und unterbreiten Vorschläge, wie sich diese - bisweilen unbefriedigende - Situation verbessern lässt. Während unserer umfangreichen Literaturrecherche ist uns sehr schnell aufgefallen, dass HRV-Kurzzeitmessungen auf ein breites wissenschaftliches Interesse stoßen, wobei verschiedenste Konzepte und Forschungsfragen mit spezifischen HRV-Mustern in Verbindung gebracht werden (vgl. Beauchaine, 2001; Dong, 2016; Francesco et al., 2012; Makivić, Nikić, & Willis, 2013; Nunan et al., 2010; Pinna et al., 2007; Quintana & Heathers, 2014; Sammito et al., 2015; Sandercock, 2007). Darunter befinden sich sowohl eher eigenschaftsähnliche (z.B. Trait-Angst; Miu, Heilman, & Miclea, 2009; Watkins, Grossman, Krishnan, & Sherwood, 1998) als auch stark situationsabhängige Konstrukte (z.B. akute emotionale Erregung; Lackner, Weiss, Hinghofer-Szalkay, & Papousek, 2013; Papousek, Schulter, & Premsberger, 2002). Während die beiden einflussreichsten Theorien zur HRV, die Polyvagal-Theorie (Porges, 1995b, 2001, 2007) und das Modell der neuroviszeralen Integration (Thayer & Lane, 2000, 2009), einen dispositionellen Charakter der HRV nahelegen, sind zahlreiche Einflussfaktoren bekannt, die unmittelbare Auswirkungen auf das autonome Nervensystem haben (Fatisson, Oswald, & Lalonde, 2016; Valentini & Parati, 2009). Demzufolge haben wir uns die Frage gestellt, wie zeitlich stabil individuelle HRV-Messungen sind (siehe Kapitel III). Da die existierende Literatur hierzu ambivalente Ergebnisse bereithält (Sandercock, 2007; Sandercock, Bromley, & Brodie, 2005) und die zeitliche Stabilität von HRV-Messungen bisher vornehmlich über sehr kurze Zeiträume mit wenigen Messzeitpunkten untersucht wurde (z.B. Cipryan & Litschmannova, 2013; Maestri et al., 2009; Pinna et al., 2007), haben wir eine längsschnittliche Studie mit fünf Messzeitpunkten, verteilt auf ein Jahr, konstruiert (N = 103 Studierende). In Abhängigkeit von der Körperhaltung der Probanden während der Messung (liegend, sitzend, stehend), haben wir nachfolgend die Retest-Reliabilität (absolute und relative Reliabilität; siehe Atkinson & Nevill, 1998; Baumgartner, 1989; Weir, 2005) der gängigsten HRV-Parameter ermittelt. Unsere Ergebnisse deuten auf ein beachtliches Ausmaß an Zufallsschwankungen der HRV-Parameter hin, welches weitgehend unabhängig von der Körperhaltung der Probanden und dem zeitlichen Abstand der Messzeitpunkte ist. Da diese Ergebnisse weitreichende Folgen suggerieren, diskutieren wir diese, unter Berücksichtigung vorhandener Einschränkungen, ausführlich. Während in Kapitel II und III vornehmlich methodische Fragen im Fokus stehen, stelle ich in Kapitel IV dieser Monografie eine Feldstudie vor. Im Rahmen dieser Studie haben wir die Zusammenhänge zwischen subjektivem Stress, Coping-Strategien, HRV und Schulleistung untersucht. Sowohl die bereits erwähnten Theorien (Porges, 1995b, 2001, 2007, Thayer & Lane, 2000, 2009), als auch eine beträchtliche Anzahl an Forschung, lassen Zusammenhänge zwischen HRV und Stress (z.B. Berntson & Cacioppo, 2004; Chandola, Heraclides, & Kumari, 2010; Krohne, 2017; Michels, Sioen, et al., 2013; Oken, Chamine, & Wakeland, 2015; Porges, 1995a; Pumprla, Howorka, Groves, Chester, & Nolan, 2002) sowie HRV und kognitiver Leistung vermuten (z.B. Duschek, Muckenthaler, Werner, & Reyes del Paso, 2009; Hansen, Johnsen, & Thayer, 2003; Luque-Casado, Perales, Cárdenas, & Sanabria, 2016; Shah et al., 2011). Allerdings fehlt es bislang an Studien, welche die komplexeren Zusammenhänge zwischen all den genannten Konstrukten untersuchen. Dies gilt insbesondere für die Untersuchung von Kindern und Jugendlichen. Um zur Schließung dieser Wissenslücke beizutragen, haben wir Gymnasiasten (N = 72, zwischen zehn und 15 Jahren alt) im Rahmen eine Querschnittstudie zu deren Stresserleben und Bewältigungsstrategien (mittels SSKJ 3-8; Lohaus, Eschenbeck, Kohlmann, & Klein-Heßling, 2006) befragt. Außerdem wurden bei all diesen Schülern HRV und Zeugnisdurchschnittsnoten erhoben. Unsere Ergebnisse unterstreichen die Bedeutung konstruktiver Coping-Strategien zur Vermeidung von physischen und psychischen Stresssymptomen, welche ihrerseits negative Auswirkungen auf die Schulleistung haben. Demgegenüber lassen sich die erwarteten Zusammenhänge zwischen HRV und Stress/Coping (Berntson & Cacioppo, 2004; Dishman et al., 2000; Fabes & Eisenberg, 1997; Lucini, Di Fede, Parati, & Pagani, 2005; Michels, Sioen, et al., 2013; O’Connor, Allen, & Kaszniak, 2002; Porges, 1995a) sowie HRV und kognitiver Leistung (Hansen et al., 2003; Suess, Porges, & Plude, 1994; Thayer, Hansen, Saus-Rose, & Johnsen, 2009) anhand unserer Daten nicht bestätigen. Mögliche Gründe für dieses Befundmuster sowie Anforderungen an zukünftige Studien dieser Art werden abschließend diskutiert. Schlussendlich (a) fasse ich alle gesammelten Erkenntnisse prägnant zusammen, (b) diskutiere deren Implikationen, (c) stelle deren Beitrag zum wissenschaftlichen Forschungsstand heraus, und (d) gebe einen kurzen Einblick in die jüngsten Entwicklungen der HRV-Forschung (Kapitel V). Außerdem, und damit schließe ich den inhaltlichen Part dieser Monografie ab, möchte ich den Leser an meinen zehn wichtigsten Lernerfahrungen teilhaben lassen.
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Sattar, Nedal Abdul. "Heart rate variability in man." Thesis, University of Edinburgh, 1989. http://hdl.handle.net/1842/30723.

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Kurths, Jürgen, A. Voss, Annette Witt, P. Saparin, H. J. Kleiner, and N. Wessel. "Quantitative analysis of heart rate variability." Universität Potsdam, 1994. http://opus.kobv.de/ubp/volltexte/2007/1347/.

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In the modern industrialized countries every year several hundred thousands of people die due to the sudden cardiac death. The individual risk for this sudden cardiac death cannot be defined precisely by common available, non-invasive diagnostic tools like Holter-monitoring, highly amplified ECG and traditional linear analysis of heart rate variability (HRV). Therefore, we apply some rather unconventional methods of nonlinear dynamics to analyse the HRV. Especially, some complexity measures that are basing on symbolic dynamics as well as a new measure, the renormalized entropy, detect some abnormalities in the HRV of several patients who have been classified in the low risk group by traditional methods. A combination of these complexity measures with the parameters in the frequency domain seems to be a promising way to get a more precise definition of the individual risk. These findings have to be validated by a representative number of patients.
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Migeotte, Pierre-François. "Heart rate variability :applications in microgravity." Doctoral thesis, Universite Libre de Bruxelles, 2003. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/211257.

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Berndtsson, Andreas. "Heart Rate Variability Biofeedback for Android." Thesis, KTH, Medicinsk teknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-136687.

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Heart rate variability (HRV) is the variations in time between consecutive heart beats, and reflects the functioning of the autonomic nervous system. Not only is HRV a good marker for many physiological disorders, but it is well known that HRV can be altered consciously by different approaches even though it is controlled by the autonomic nervous system. Respiration is an important factor in modulating HRV and this property is utilized in HRV biofeedback, which is a method that aims at increasing heart rate variability. HRV biofeedback systems typically measures heart rate variability and display the parameters on a screen, enabling the user to gain control and increase heart rate variations. In this thesis a software for biofeedback of heart rate variability is presented. The software was implemented for Android and runs on a tablet computer to make the biofeedback system portable and more accessible than most other biofeedback systems. The developed software has proven to be fully functional in real-time providing the user with reliable information. A small pilot study on healthy volunteers has also been made to evaluate the effects of the biofeedback training. These measurements give a preliminary indication that biofeedback session with the proposed solution increases HRV. However, a more comprehensive study with a larger population needs to be carried out in order to confidently confirm the positive effects of biofeedback sessions with the software.
Heart rate variability (HRV) är variationerna i tid mellan två efterföljande hjärtslag, och återspeglar autonomiska nervsystemets funktion. HRV är en tydlig markör for många sjukdomar, men det är också välkänt att HRV kan påverkas medvetet trots att det styrs av autonomiska nervsystemet. Andning är en viktig påverkande faktor av HRV och denna egenskap utnyttjas i HRV biofeedback, som är en teknik som syftar till att öka HRV. Typiska system för HRV biofeedback mäter variationerna i hjärtfrekvens och visar upp informationen på en display, vilket låter användaren ta kontroll över denna parameter och öka HRV. I denna uppsats presenteras ett program för biofeedback av HRV. Mjukvaran har implementerats för Android och körs på en surfplatta för att skapa ett biofeedbacksystem som är portabelt och där tillgängligheten är hög, till skillnad från de flesta andra biofeedback system som är beroende av en dator. Programmet som utvecklats har visat sig vara fullt funktionellt i realtid och visar upp pålitliga parametrar för användaren. En förstudie har även utförts för att utvärdera effekterna vid användning av programmet. Dessa mätningar indikerar att biofeedbackträning med den föreslagna lösningen ökar HRV efter användning. En mer omfattande studie med fler personer bör dock genomföras för att ge en tydligare bild av effekterna av träning med detta program.
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Heathers, James. "Methodological improvements in heart rate variability." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/13106.

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Heart rate variability (HRV) refers to the amplitude and period of fluctuations in the heart rate over time. HRV is an accessible, low-cost and straightforward technique for measuring autonomic outflow, but also a complicated epiphenomena of interacting autonomic, circulatory and respiratory factors. Confusion about the meaning of HRV is reflected in the literature establishing basic HRV theory, and in the applied literature which uses HRV as a dependent variable or predictor of psychological outcomes. Here, 2 straightforward issues present themselves: 1) best-case practice for methodological implementation is not being followed, and 2) sample sizes for between-subjects investigations of phenomena where HRV is a dependent variable are underpowered. Specifically addressing the above; 1) an attempt is made to a) understand and codify a best-case practice for methodological control in biobehavioural research, and b) investigate profound but common sources of error in HRV recording; 2) rationale for the development and field testing of a device which allows mass collection of HRV records from experimental participants is outlined. Best-case practice for experimental implementation is recommended: the use of within-subjects data, the consideration of the nature of ‘baseline’ periods against which experimental conditions are compared, and respiratory monitoring within participants to control for occasional or whole-sample artifacts. Current research is not well controlled – theoretical, statistical and practical errors are widely observed. For addressing experimental power, pulse monitoring shows acceptable reliability over time, and the device developed (a smartphone-based pulse rate monitor) shows excellent accuracy in comparison to conventional measurement. A novel solution for correcting pulse to pulse intervals is offered which improves measurement accuracy and performs well in field trials of mass-collected data.
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Litster, Caroline Elizabeth. "Heart rate, heart rate variability, electrodermal activity and the differentiation-of-deception /." Title page, table of contents and abstract only, 2002. http://web4.library.adelaide.edu.au/theses/09SSPS/09sspsl7769.pdf.

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Zapanta, Laurence (Laurence F. ). "Heart rate variability in mice with coronary heart disease." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/34118.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
Includes bibliographical references (leaves 69-71).
Heart rate variability (HRV), the beat-to-beat fluctuation of the heart rate, is a non-invasive test that measures the autonomic regulation of the heart. Assessment of HRV has been shown to predict the risk of mortality in patients after an acute myocardial infarction. Recently, the Krieger lab at MIT developed genetically engineered double knockout (dKO) mice that develop coronary artery disease accompanied by spontaneous myocardial infarctions and die at a very young age. This thesis investigated whether HRV could function as a prognostic indicator in the dKO mouse. A novel method for estimating physiological state of the mouse from the electrocardiogram using an innovative activity index was developed in order to compare HRV variables at different times while controlling for physiologic state. Traditional time and frequency domain variables were used to assess the prognostic power of HRV. Results have shown that none of the HRV variables were helpful in predicting mortality in the dKO mice. Mean heart rate showed some prognostic power, but it was not consistent in all the dKO mice. Finally, the activity index developed in this thesis provided a reliable metric for activity in mice as validated by a camera with motion detection.
by Laurence Zapanta.
S.M.
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Dodds, Kirsty Lyn. "Heart to Heart: Exploring Heart Rate Variability in Insomnia Patient Subtypes." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/17262.

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Insomnia is one of the most common complaints in medical practice and the sleep disorder of highest prevalence. At least 10% of the worldwide population has chronic insomnia, which has been associated with a range of negative health outcomes. Within the clinical setting, patient subtypes have been defined according to symptomology. More recently insomnia researchers have proposed phenotypes based on total sleep time during overnight polysomnography (PSG). Short-sleeping insomnia patients are purported to be a biologically severe phenotype at higher risk of cardiovascular morbidity, poor mental health, and obesity (compared to healthy controls). Heart rate variability (HRV) is an objective marker that provides insight into autonomic nervous system dynamics. The overarching aim of my research was to explore a large clinical sample of patients with Insomnia Disorder to determine whether differences in HRV exist during sleep in empirically-derived insomnia patient subtypes. The aim of the work presented within Chapter 2 was to identify all previous insomnia-HRV research to determine if HRV was impaired in adult patients with insomnia, and whether treatments altered HRV. A systematic review of five web databases located 22 relevant articles; 17 case-control studies and 5 interventions studies. Results were difficult to synthesise due to incomparable methodology and reporting. There was a high risk of bias in the majority of studies. It was concluded that although HRV impairment in insomnia may be a widely-accepted concept, it is not supported by research nor has it been determined if it varies after treatment or according to patient subtype. The aim of the first empirical study of the thesis (Chapter 3) was to objectively-derive insomnia patient subtypes and evaluate their physiological signals (HRV and electroencephalography [EEG]) during sleep onset. Patients (n = 96) with clinically-diagnosed Insomnia Disorder underwent overnight PSG to determine sleep metrics for cluster analysis using Ward’s method: Total Sleep Time (TST), Wake After Sleep Onset (WASO) and Sleep Onset Latency (SOL). Electrocardiogram (ECG) from the PSG was extracted in the 10 minutes before and after sleep onset. After R-wave detection, the ECG was visually checked and manually corrected as required. Six time and frequency-domain HRV measures were analyzed; heart rate (HR), standard deviation of all N-N intervals (SDNN), root mean square of successive R-R intervals (RMSSD), percentage of successive R-R intervals that differ by > 50 ms (PNN50), high frequency (HF), and low frequency (LF)/HF ratio. Cluster analysis derived two solutions; one comprising two subtypes and another with three subtypes. The two cluster solution consisted of insomnia with short-sleep duration (I-SSD: n = 43) and insomnia with normal objective sleep duration (I-NSD: n = 53). At sleep onset, between-group HRV analysis revealed reduced parasympathetic activity (PNN50 and RMSSD) in the short-sleeping subtype. This was not mirrored by significant increases in HR and/or the LF/HF ratio. These findings suggested that reduced parasympathetic activity during sleep onset might contribute to poor cardiometabolic health outcomes previously reported in short-sleeping insomnia patients. The final component of this thesis was a case-control study (Chapter 4) which examined whether HRV measures differed between insomnia subtypes across the nocturnal period. It was hypothesized that short-sleeping insomnia patients would have impaired HRV compared to normal-sleep duration insomnia patients, consistent with differences observed at sleep onset (Chapter 3). Insomnia patients underwent overnight PSG, which provided sleep metrics for cluster analysis and ECG for HRV analysis. ECG was visually checked for accurate R-wave detection, and manually corrected as required. HRV analysis was performed from lights-off to lights-on (and separately by sleep/wake stage) using time and frequency-domain measures. Differences in HRV measures (HR, SDNN, RMSSD, LF, HF, LF/HF) were tested between the subtypes using General Linear Models controlling for age as a core confounder. Short-sleeping insomnia patients (I-SSD: n = 34; 45.5 ± 10.5 years) and normal-sleep duration insomnia patients (I-NSD: n = 41; 37.6 ± 10.9 years) were included in the HRV analysis. There were no statistically significant nocturnal HRV differences between subtypes after controlling for age. As such, short-sleeping insomnia patients did not have statistically significant reductions in HRV measures representative of parasympathetic activity.«br /» In summary, there was a lack of persistent nocturnal HRV disparities (between empirically-derived insomnia patient subtypes) that extended beyond sleep onset in this large clinical sample of patients with Insomnia Disorder. The central tenet of 24-hour hyperarousal amongst short-sleep duration insomnia patients cannot be supported by the combined findings of these two empirical studies. Post-hoc calculations revealed larger sample sizes would be required to determine a small to medium effect size difference in nocturnal HRV between insomnia patient subtypes. Until this time, the directional relationship between insomnia, heart rate variability, hyperarousal and cardiovascular disease remains unclear in the heterogeneous insomnia population.
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Zemánek, Ladislav. "Analýza variability srdečního rytmu pomocí entropie." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-220014.

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The analysis of HRV is an advanced and noninvasive method which is used to investigate the involuntary nervous system. It is also one of the important parameters of its proper function. Heart rate variability can also be analyzed by entropy, which studies the discrepancy of the RR intervals of the HRV signal and thus can be used to diagnose cardiac diseases
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Books on the topic "Heart Rate Variability"

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Ernst, Gernot. Heart Rate Variability. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-4309-3.

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Marek, Malik, and Camm A. John, eds. Heart rate variability. Armonk, NY: Futura Pub. Co., 1995.

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Mahon, Cameron. Power spectral analysis of heart rate variability. Ottawa: National Library of Canada, 1990.

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Khandoker, Ahsan Habib, Chandan Karmakar, Michael Brennan, Marimuthu Palaniswami, and Andreas Voss. Poincaré Plot Methods for Heart Rate Variability Analysis. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-7375-6.

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García Martínez, Constantino Antonio, Abraham Otero Quintana, Xosé A. Vila, María José Lado Touriño, Leandro Rodríguez-Liñares, Jesús María Rodríguez Presedo, and Arturo José Méndez Penín. Heart Rate Variability Analysis with the R package RHRV. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65355-6.

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Bernstein, Laura, Aradhita Yadava, Sarah Collett, and Julie Patrick. Challenges in Collecting Heart Rate Variability Data Over Videoconferencing Platforms. 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2022. http://dx.doi.org/10.4135/9781529603446.

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D, Jindal G., and Bhabha Atomic Research Centre, eds. Medical analyzer for the study of physiological variability and disease characterization. Mumbai: Bhabha Atomic Research Centre, 2003.

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Sharif, Bayan Salim. The monitoring and analysis of fetal and neonatal heart rate variability. (s.l: The Author), 1988.

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Thompson, Catherine Ruth. Heart rate variability in neonates: The contribution of respiratory sinus arrhythmia. Birmingham: University of Birmingham, 1993.

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Cotie, Lisa. The validation of heart rate variability in individuals with spinal cord injury. St. Catharines, Ont: Brock University, Faculty of Applied Health Sciences, 2009.

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Book chapters on the topic "Heart Rate Variability"

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Tavernier, Benoît, and Mathieu Jeanne. "Heart Rate Variability." In Monitoring Technologies in Acute Care Environments, 109–15. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8557-5_13.

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Sosnowski, Maciej. "Heart Rate Variability." In Specialized Aspects of ECG, 97–258. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-880-5_3.

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Casamian-Sorrosal, Domingo. "Heart Rate Variability." In Guide to Canine and Feline Electrocardiography, 231–40. Chichester, UK: John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781119254355.ch16.

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Orbell, Sheina, Havah Schneider, Sabrina Esbitt, Jeffrey S. Gonzalez, Jeffrey S. Gonzalez, Erica Shreck, Abigail Batchelder, et al. "Heart Rate Variability." In Encyclopedia of Behavioral Medicine, 952–53. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_805.

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Fel, Lü, and Marek Malik. "Heart rate variability." In Cardiac Pacing and Electrophysiology, 49–62. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-0872-0_6.

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Thayer, Julian F. "Heart Rate Variability." In Encyclopedia of Behavioral Medicine, 1048–49. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39903-0_805.

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Sainburg, Robert L., Andrew L. Clark, George E. Billman, Zachary J. Schlader, Toby Mündel, Kevin Milne, Earl G. Noble, et al. "Heart Rate Variability." In Encyclopedia of Exercise Medicine in Health and Disease, 387–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-540-29807-6_94.

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Ernst, Gernot. "History of Heart Rate Variability." In Heart Rate Variability, 3–8. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4309-3_1.

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Ernst, Gernot. "Intensive Care and Trauma." In Heart Rate Variability, 217–31. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4309-3_10.

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Ernst, Gernot. "Neurologic Disorders." In Heart Rate Variability, 233–44. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4309-3_11.

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Conference papers on the topic "Heart Rate Variability"

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Rowe, Dennis W., John Sibert, and Don Irwin. "Heart rate variability." In the SIGCHI conference. New York, New York, USA: ACM Press, 1998. http://dx.doi.org/10.1145/274644.274709.

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Lin, Der Chyan B., and Richard L. Hughson. "Cascade heart rate variability." In SPIE's First International Symposium on Fluctuations and Noise, edited by Sergey M. Bezrukov, Hans Frauenfelder, and Frank Moss. SPIE, 2003. http://dx.doi.org/10.1117/12.500599.

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Cheng, Jen-Liang, Jin-Ren Jeng, Zhu-Xuan Lin, and Jiunn-Horng Lee. "Approximating Heart Rate Variability." In 2nd International ICST Conference on Pervasive Computing Technologies for Healthcare. ICST, 2008. http://dx.doi.org/10.4108/icst.pervasivehealth2008.2552.

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Jen-Liang Cheng, Jin-Ren Jeng, Zhu-Xuan Lin, and Jiunn-Horng Lee. "Approximating Heart Rate Variability." In 2008 Second International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth). IEEE, 2008. http://dx.doi.org/10.1109/pcthealth.2008.4571103.

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Kalinkov, Kalin, Valentina Markova, and Todor Ganchev. "Heart Rate Variability calculation methods." In 2020 International Conference on Biomedical Innovations and Applications (BIA). IEEE, 2020. http://dx.doi.org/10.1109/bia50171.2020.9244285.

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Shrivastava, A., and P. P. Bansod. "Heart Rate Variability Monitor IC." In Confluence 2013: The Next Generation Information Technology Summit (4th International Conference). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/cp.2013.2355.

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"Stress and Heart Rate Variability." In The First International Workshop on Biosignal Processing and Classification. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0001194000680077.

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Gusev, Marjan, Stojancho Tudjarski, and Ana Anagelevska. "Sampling Rate Impact on Heart Rate Variability." In 2022 30th Telecommunications Forum (TELFOR). IEEE, 2022. http://dx.doi.org/10.1109/telfor56187.2022.9983696.

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Paliwal, Sukriti, C. Vasantha Lakshmi, and C. Patvardhan. "Real time heart rate detection and heart rate variability calculation." In 2016 IEEE Region 10 Humanitarian Technology Conference (R10-HTC). IEEE, 2016. http://dx.doi.org/10.1109/r10-htc.2016.7906818.

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Qiao, Donghao, Farhana Zulkernine, Raihan Masroor, Roshaan Rasool, and Nauman Jaffar. "Measuring Heart Rate and Heart Rate Variability with Smartphone Camera." In 2021 22nd IEEE International Conference on Mobile Data Management (MDM). IEEE, 2021. http://dx.doi.org/10.1109/mdm52706.2021.00049.

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Reports on the topic "Heart Rate Variability"

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Sastre, Antonio. Practical Implementations of Real-Time Heart Rate Variability. Fort Belvoir, VA: Defense Technical Information Center, March 2004. http://dx.doi.org/10.21236/ada425939.

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Hagan, R. D., M. J. Buono, S. Singh, and C. G. Blood. Heart Rate Variability and Changes in Blood Volume. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada389810.

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De Jong, Marla J., and David C. Randall. Heart Rate Variability Analysis in the Assessment of Autonomic Function in Heart Failure. Fort Belvoir, VA: Defense Technical Information Center, July 2004. http://dx.doi.org/10.21236/ada425014.

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Clark, Robert D. Heart Rate Variability in Male Sexual Arousal and Erectile Dysfunction. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ad1013961.

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Chan DykN’an and Alexanyants. PECULIARITIES OF HEART RATE VARIABILITY OF QUALIFIED BADMINTON PLAYERS AT REST. Federal State Budgetary Educational Establishment of Higher Vocational Education "Povolzhskaya State Academy of Physical Culture, Sports and Tourism" Naberezhnye Chelny, December 2013. http://dx.doi.org/10.14526/34_2013_4.

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Brusseau, Valentin, I. Tauveron, R. Bagheri, U. Ugbolue, V. Magnon, J. B. Bouillon-Minois, V. Navel, and F. Dutheil. Effect of hyperthyroidism treatments on heart rate variability: A systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2022. http://dx.doi.org/10.37766/inplasy2022.8.0062.

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Abstract:
Review question / Objective: The reversibility of HRV abnormalities in hyperthyroidism remains contradictory. The purpose of the study is to conduct a systematic review and meta-analysis on the effect of antithyroid treatments on HRV in hyperthyroidism. Population: Untreated hyperthyroid patients Intervention: Antithyroid treatment Control: Controls without hyperthyroidism Outcomes: Reversibility of heart rate variability abnormalities in hyperthyroidism Study design: Systematic review. Information sources: All studies that addressed the effect of hyperthyroidism treatment on HRV were reviewed. Studies were searched electronically through the major article databases (PubMed, Cochrane Library, Embase, and Google Scholar) with the following keywords: ("hyperthyroidism" OR "hyperthyroid") AND ("heart rate variability" OR "HRV") until April 4, 2022.
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Zaglaniczny, K., W. Shoemaker, D. S. Gorguze, C. Woo, and J. Colombo. An Examination of Real-Time Heart Rate Variability During Laparoscopic Cholecystectomies and Radical Surgeries. Fort Belvoir, VA: Defense Technical Information Center, September 2000. http://dx.doi.org/10.21236/ada389114.

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Bucsea, Oana, Sara Jasim, Estreya Cohen, and Rebecca Pillai Riddell. Patterns of heart rate variability (HRV) responses to acute stress in neonates: A systematic review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2024. http://dx.doi.org/10.37766/inplasy2024.8.0007.

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wang, chen, and faming yang. Effect of exercise on heart rate variability in healthy adults - a systematic review and network meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, May 2023. http://dx.doi.org/10.37766/inplasy2023.5.0026.

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Fathima, Deena, Joss Lobo, Manuela Angioi, Wieslaw Blach, Lukasz Rydzik, Tadeusz Ambrozy, and Nikos Malliaropoulos. Sedentary lifestyle, heart rate variability and its Influence on Spine Posture in adults: A Systematic Review Study. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, June 2024. http://dx.doi.org/10.37766/inplasy2024.6.0055.

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