As the world population ages, the healthcare system is facing new challenges in treating more patients at a lower cost than today. One trend in addressing this problem is to increase the opportunities of in-home care. To achieve this there is a need for safe and cost-effective monitoring systems. Biosignal PI is an ongoing open-source project created to develop a flexible and affordable platform for development of stand-alone devices able to measure and process physiological signals. This master thesis project, performed at the department of Medical Sensors, Signals and System at the School of Technology and Health, aimed at further develop the Biosignal PI software by constructing a new flexible software framework architecture that could be used for measurement and processing of different types of biosignals. The project also aimed at implementing features for Heart Rate Variability(HRV) Analysis in the Biosignal PI software as well as developing a graphical user interface(GUI) for the Raspberry PI hardware module PiFace Control and Display. The project developed a new flexible abstract software framework for the Biosignal PI. The new framework was constructed to abstract all hardware specifics into smaller interchangeable modules, with the idea of the modules being independent in handling their specific task making it possible to make changes in the Biosignal PI software without having to rewrite all of the core. The new developed Biosignal PI software framework was implemented into the existing hardware setup consisting of an Raspberry PI, a small and affordable single-board computer, connected to ADAS1000, a low power analog front end capable of recording an Electrocardiography(ECG). To control the Biosignal PI software two different GUIs were implemented. One GUI extending the original software GUI with the added feature of making it able to perform HRV-Analysis on the Raspberry PI. This GUI requires a mouse and computer screen to function. To be able to control the Biosignal PI without mouse the project also created a GUI for the PiFace Control and Display. The PiFace GUI enables the user to collect and store ECG signals without the need of an big computer screen, increasing the mobility of the Biosignal PI device.   To help with the development process and also to make the project more compliant with the Medical Device Directive a couple of development tools were implemented such as a CMake build system, integrating the project with the Googletest testing framework for automated testing and the implementation of the document generator software Doxygen to be able to create an Software Documentation.    The Biosignal PI software developed in this thesis is available through Github at https://github.com/biosignalpi/Version-A1-Rapsberry-PI / Allt eftersom världens befolkning åldras, ställs sjukvården inför nya utmaningar i att behandla fler patienter till en lägre kostnad än idag. En trend för att lösa detta problem är att utöka möjligheterna till vård i hemmet.För att kunna göra detta finns det ett ökande behov av säkra och kostnadseffektiva patientövervakningssystem. Biosignal PI är ett pågående projekt med öppen källkod som skapats för att utveckla en flexibel och prisvärd plattform för utveckling av fristående enheter som kan mäta och bearbeta olika fysiologiska signaler. Detta examensarbete genomfördes vid institutionen för medicinska sensorer, signaler och system vid Skolan för Teknik och Hälsa. Projektet syftade till att vidareutveckla den befintliga mjukvaran för Biosignal PI genom att skapa ett nytt flexibelt mjukvaruramverk som kan användas för mätning och bearbetning av olika typer av biosignaler.Projektet syftade också till att utvidga mjukvaran och lägga till funktioner för att kunna genomföra hjärtfrekvensvariabilitets(HRV) analys i Biosignal PIs mjukvara, samt att utveckla ett grafiskt användargränssnitt(GUI) för hårdvarumodulen PiFace Control and Display. Projektet har utvecklat ett nytt flexibelt mjukvaruramverk för Biosignal PI. Det nya ramverket konstruerades för att abstrahera alla hårdvaruspecifika delar in i mindre utbytbara moduler, med tanken att modulerna ska vara oberoende i hur de hanterar sin specifika uppgift. På så sätt ska det vara möjligt att göra ändringar i Biosignal PIs programvara utan att behöva skriva om hela mjukvaran.Det nyutvecklade Biosignal PI ramverket implementerades i det befintliga hårdvaru systemet, som består av en Raspberry PI, liten och prisvärd enkortsdator, ansluten till ADAS1000, en analog hårdvarumodul med möjlighet att registrera ett elektrokardiografi(EKG/ECG). För att kontrollera Biosignal PI programmet har två olika grafiska användargränssnitt skapats.Det ena gränssnitt är en utvidgning av original programvaran med tillagd funktionalitet för att kunna göra HRV-Analys på Raspberry PI, detta gränssnitt kräver dock mus och dataskärm för att kunna användas.För att kunna styra Biosignal PI utan mus och skärm skapades det även ett gränssnitt för PiFace Control and Display. PiFace gränssnittet gör det möjligt för användaren att samla in och lagra EKG-signaler utan att behöva en stor datorskärm, på så sätt kan man öka Biosignal PI systemets mobilitet. För att underlätta utvecklingsprocessen, samt göra projektet mer förenligt med det medicintekniska regelverket, har ett par utvecklingsverktyg integrerats till Biosignal PI projektet såsom CMake för kontroll av kompileringsprocessen, test ramverket Googletest för automatiserad testning samt integrering med dokumentations generatorn Doxygen för att kunna skapa en dokumentation av mjukvaran.

Cardiovascular disease has been a leading cause of death worldwide over the last decades (Roth et al., 2015; WHO, 2021a). In countries with middle or elevated gross domestic product indices, stroke and myocardial infarction represent the prevalent causes of death. Over the years, the scientific community has identified significant cognitive and emotional impacts on survivors of coronary heart disease and cardiovascular disease. We know that ageing populations and high-stress levels associated with contemporary lifestyles play a crucial role in the prognosis and recovery of individuals with myocardial infarction. These factors are associated with an increased societal burden related to survivors’ care. As they age, a higher proportion of women than men are affected by coronary heart disease, including myocardial infarction. Nonetheless, women remain under-represented in studies addressing trajectories of recovery associated with myocardial infarction. The arching goal of this thesis is to expand the knowledge on the association of various environmental and physical factors with a history of myocardial infarction in a sample of Canadian women. The accomplished research is presented in the form of two empirical studies carried out on samples of Canadian women with and without a history of myocardial infarction, as well as two systematic reviews of the literature. The first study established the state of knowledge on the Trier Social Stress Test paradigm, a tool that we later used in our laboratory study. Through an in-depth examination of the protocols used by different research groups, this systematic review identified essential elements for valid conclusions and proposed a set of recommendations for standardizing the use of the Trier Social Stress Test in research. The second systematic review updated the current scientific knowledge concerning the cognitive consequences of women with a history of coronary heart disease. Despite cardiovascular disease, including coronary heart disease, remainsunderstudied in women, the last decade has seen an emergence of research supporting cognition to be affected. Our findings support subtle cognitive impairments in women with a history of coronary heart disease. Our literature review was conducted to facilitate interpreting the results obtained in a sample of women with a history of MI in this thesis’ fourth study. Regarding data collection, an online questionnaire validated the presence of specific risk factors and symptoms associated with myocardial infarction in a sample of middle-aged Canadian women (N = 366). Finally, a laboratory study measured alterations in the physiological responses (i.e., heart rate variability and salivary cortisol secretion) associated with exposure to a social stressor (i.e., Trier Social Stress Test) in women with a history of myocardial infarction and age-matched controls (N = 29). This body of data and analytic reviews contribute to expanding the knowledge of physiological and cognitive impairments in women with a MI history. Our research also helps improve testing paradigms to examine deficits and identify areas where further research is needed. Our findings support women experiencing different symptoms than those described in men, and it pleads for these to be no longer described as "atypical." Our work highlights a similar prevalence of certain factors (e.g., hypertension) in Canadian women and women from other parts of the world. In terms of the laboratory study, our results indicate subjective/perceived levels of stress intensity to be comparable between the myocardial infarction and non-myocardial infarction women groups. However, we only found tendencies in changes related to measured physiological variables.

Zymosan-induced peritonitis is a model commonly used to study systemic inflammatory response syndrome and multiple organ dysfunction syndrome. However, effects of zymosan on cardiac function have not been reported. We evaluated cardiac responses to zymosan in mice and the role of β-Glucan and dectin-1 in mediating these responses. Temperature and cardiac function were evaluated before and after intraperitoneal (i.p.) injection of zymosan (100 or 500 mg/kg) or saline. Chronotropic and dromotropic functions were measured using electrocardiograms (ECGs) collected from conscious mice. Cardiac inotropic function was determined by echocardiography. High-dose zymosan caused a rapid and maintained hypothermia along with visual signs of illness. Baseline heart rate (HR) was unaffected but HR variability (HRV) increased, and there was a modest slowing of ventricular conduction. High-dose zymosan also caused prominent decreases in cardiac contractility at 4 and 24 h. Because zymosan is known to cause gastrointestinal tract pathology, peritoneal wash and blood samples were evaluated for bacteria at 24 h after zymosan or saline injection. Translocation of bacterial occurred in all zymosan-treated mice (n=3), and two had bacteremia. Purified β-Glucan (50 and 125 mg/kg, i.p.) had no effect on temperature or ECG parameters. However, deletion of dectin-1 modified the ECG responses to high-dose zymosan; slowing of ventricular conduction and the increase in HRV were eliminated but a marked bradycardia appeared at 24 h after zymosan treatment. Zymosan-treated dectin-1 knockout mice also showed hypothermia and visual signs of illness. Fecal samples from dectin-1 knockout mice contained more bacteria than wild types, but zymosan caused less translocation of bacteria. Collectively, these findings demonstrate that zymosan-induced systemic inflammation causes cardiac dysfunction in mice. The data suggest that dectin-1-dependent and -independent mechanisms are involved. Although zymosan treatment causes translocation of bacteria, this effect does not have a major role in the overall systemic response to zymosan.

Photoplethysmography (PPG) is a method to detect blood volume changes in every heartbeat. The peaks in the PPG signal corresponds to the electrical impulses sent by the heart. The duration between each heartbeat varies, and these variances are better known as heart rate variability (HRV). Thus, finding peaks correctly from PPG signals provides the opportunity to measure an accurate HRV. Additional research indicates that deep learning approaches can extract HRV from a PPG signal with significantly greater accuracy compared to other traditional methods. In this study, deep learning classifiers were built to detect peaks in a noise-contaminated PPG signal and to recognize the performed activity during the data recording. The dataset used in this study is provided by the PhysioBank database consisting of synchronized PPG-, acceleration- and gyro data. The models investigated in this study were limited toa one-layer LSTM network with six varying numbers of neurons and four different window sizes. The most accurate model for the peak classification was the model consisting of 256 neurons and a window size of 15 time steps, with a Matthews correlation coefficient (MCC) of 0.74. The model consisted of64 neurons and a window duration of 1.25 seconds resulted in the most accurate activity classification, with an MCC score of 0.63. Concludingly, more optimization of a deep learning approach could lead to promising accuracy on peak detection and thus an accurate measurement of HRV. The probable cause for the low accuracy of the activity classification problem is the limited data used in this study.

The project describes the methods useful for observe changes of heart rate in ECG signal. Heart rate variability become (HRV) the conventionally accepted term to describe variations of NN intervals between consecutive heart beats and generally it is function of instantaneous heart rate or NN interval on time. HRV may be evaluated by time domain or frequency domain measures. In Matlab was developed algorithm, realized like function, which counts HRV parameters from ECG signal series. Analysis in time domain adverts to high correlation between statistic and geometric parameters and similarly with signal HRV. Results of frequency domain analysis shows similarity of power spectral density, which was calculated by two different ways (from interpolated and no interpolated signal HRV). Functionality of developed algorithm was verified on each signal. Project results have signification in progress of analysis ECG signal methods with a view to observe pathological changes in heart rate.

Hintergrund: Das Auftreten einer kardialen autonomen Dysfunktion nach einem akutem ischämischen Schlaganfall (AIS) geht mit einer ungünstigen Prognose und einer erhöhten Mortalität einher. In der vorliegenden Arbeit wurde die Hypothese untersucht, dass Herzfrequenzvariabilitäts (HRV)-Biofeedback die autonome Herzfunktion nach Schlaganfall verbessern kann. Methodik/Design: 48 AIS-Patienten erhielten unter randomisierten Bedingungen entweder HRV- oder Sham-Biofeedback (1:1) zusätzlich zur standardisierten Stroke Unit Versorgung. Bei sämtlichen Studienteilnehmern wurde vor Beginn der ersten und nach Abschluss der letzten Biofeedbacksitzung eine autonome Funktionsmessung durchgeführt, die neben der Messung der HRV auch eine Erfassung der autonomen vasomotorischen (die neurovaskuläre Regulation der arteriellen Blutgefäßweite erfassenden) und sudomotorischen (die neuronale Regulation der Schweißdrüsenfunktion quantifizierenden) Funktion beinhaltete. Die HRV wurde mittels Standardabweichung der NN-Intervalle (SDNN), der Standardabweichung der Differenzen benachbarter NN-Intervalle (SD of ΔNN), der Quadratwurzel des Mittelwerts aus der Summe der Quadrate der Differenzen zwischen benachbarten NN-Intervallen (RMSSD), sowie mittels des Variationskoeffizienten der R-R-Intervalle (CVNN) untersucht. Während die Parameter SDNN und RMSSD vorwiegend parasympathisch determinierten Indikatoren der HRV entsprechen, stellt der CVNN einen kompositen Parameter der sympathischen und der parasympathischen Aktivität dar. Darüber hinaus wurde eine Frequenzanalyse der HRV durchgeführt, um die Frequenzbänder der HRV differenziert zu erfassen und den Wirkmechanismus des HRV-Biofeedbacks auf die kardiale autonome Funktion zu charakterisieren. Die beiden sympathisch regulierten Funktionen der Vaso- und Sudomotorik wurden nach sympathischer Aktivierung gemessen, wobei die vasomotorische Funktion mittels Photoplethysmographie (PPG) der vasokonstriktorischen Reaktion (VCR) und die sudomotorische Hautleitwertänderung (SSR) durch Ableiteelektroden erfasst wurde. Die Bewertung des Schweregrades der autonomen Symptome durch den Survey of Autonomic Symptoms (SAS; TIS: Gesamtschwere autonomer Symptome) und des funktionellen Defizites durch die modifizierte Rankin-Skala (mRS) erfolgten vor Beginn der Intervention und drei Monate nach Interventionsbeendigung. Das Studienprotokoll wurde vor Beginn der Untersuchung in der Datenbank clinicaltrials.gov hinterlegt [clinicaltrials.gov identifier: NCT03865225]. Ergebnisse: 48 AIS-Patienten (19 Frauen; Alter im Median 69 [Interquartilsbereich 18.0] Jahre) wurden in die Untersuchung eingeschlossen. Angesichts einer hohen Adhärenz und Verträglichkeit der HRV-Biofeedbackanwendung (<0.1% fehlende Daten, keine Studienabbrühe während der Hospitalisierungsphase, unerwünschte Wirkungen: leichtgradig n=1/48) ließ sich das HRV-Biofeedbackverfahren unproblematisch in das das Setting einer multidisziplinären Stroke Unit integrieren. Die Anwendung von HRV-Biofeedback führte zu einer Erhöhung der HRV unter metronomischer Atmung (SDNN: 34,1 [45.0] ms Baseline vs. 43,5 [79.0] ms post-Intervention, p=0,015; SD of ΔNN: 29.3 [52.7] ms baseline vs. 46.4 [142.1] ms post-intervention, p=0.013; RMSSD: 29,1 [52.2] ms Baseline vs. 46,0 [140.6] ms post-Intervention, p=0.015; nicht-signifikanter Trend einer Erhöhung des CVNN: 4.1 [5.1] % Baseline vs. 5.4 [7.2] % post-Intervention, p=0.052), die nach dem Sham-Biofeedback nicht zu verzeichnen war (p=nicht signifikant (ns)). Zudem ergab die Frequenzanalyse der HRV unter metronomischer Atmung nach HRV-Biofeedback einen Anstieg im Niederfrequenzband (LF) (484.8 [1941.4] ms2 Baseline vs. 1471.3 [3329.9] ms2 post-Intervention, p=0.019) und der Total Power (1273.9 [3299.2] ms2 Baseline vs. 1771.5 [13038.8] ms2 post-Intervention, p=0.022), der in der Sham-Biofeedbackgruppe nicht beobachtet wurde (p=ns). In beiden Studiengruppen zeigte sich keine Veränderung der sympathischen Funktionen der Sudo- und Vasomotorik (p=ns). HRV-Biofeedback führte zu einer Linderung des Schweregrades autonomer Symptome drei Monate nach der Intervention (TIS: 7.5 [7.0] Baseline vs. 3.5 [8.0] Follow-Up, p=0.029), welche in der Sham-Biofeedbackgruppe ausblieb (p=ns). Erwartungsgemäß zeigten beide Studiengruppen nach drei Monaten eine Besserung der funktionellen Defizite (HRV-Biofeedbackgruppe, mRS: 2.0 [1.0] Baseline vs. 0.0 [2.0] Follow-Up, p=0.023; Sham-Biofeedbackgruppe, mRS: 2.2 [2.0] Baseline vs. 1.0 [2.0] Follow-Up, p=0.0005). Schlussfolgerungen: Die Integration von HRV-Biofeedback in die multidisziplinäre Standardversorgung einer Schlaganfallstation führte bei Patienten mit AIS zu einer Verbesserung der kardialen autonomen Funktion. Diese funktionelle Verbesserung wurde wahrscheinlich durch einen vorwiegend parasympathischen Mechanismus vermittelt und ging mit einer anhaltenden Linderung autonomer Symptome einher.:1.EINLEITUNG 1 2. HINTERGRUND 4 2.1 Schlaganfall: Pathophysiologie und klinische Bedeutung 4 2.1.1 Definition und Klassifikation 4 2.1.2 Epidemiologie 7 2.1.3 Lokalisationsbezogene klinische Präsentation 9 2.1.4 Therapie 13 2.1.5 Risikofaktoren 16 2.2 Autonomes Nervensystem (ANS): Grundlagen und Beeinträchtigungen bei Schlaganfallpatienten16 2.2.1 Anatomische und physiologische Grundlagen 17 2.2.1.1 Sympathisches Nervensystem (SNS) 20 2.2.1.2 Parasympathisches Nervensystem (PaNS) 22 2.2.1.3 Enterisches Nervensystem (ENS) 23 2.2.2 Autonome Dysfunktion beim Schlaganfall 24 2.3 Herzratenvariabilität (HRV): Ein diagnostisches Target der kardialen autonomen Funktion 25 2.3.1 Definition 25 2.3.2 Relevanz 27 2.3.3 Anwendungsbereiche 28 2.4 Biofeedback: Allgemeine Therapieprinzipien und HRV-spezifische Anwendung 29 2.4.1 Definition 29 2.4.2 Anwendungsbereiche 29 2.4.3 Herzratenvariabilitäts-gestütztes Biofeedback 31 3. FORSCHUNGSLÜCKE („RESEARCH GAP“) 32 4. ZIELSETZUNG UND HYPOTHESEN 32 5. METHODIK 33 5.1 Ethik 33 5.2 Studiendesign und Messprotokoll 34 5.3 Patienten 36 5.3.1 Patientenrekrutierung 36 5.3.2 Einschlusskriterien 36 5.3.3 Ausschlusskriterien 36 5.3.4 Patienteninformation und -einverständniserklärung 37 5.3.5 Randomisierung 37 5.4 Funktionsmessungen 37 5.4.1 Funktionen des autonomen Nervensystems 37 5.4.1.1 Kardiale autonome Funktion: Herzratenvariabilität (HRV) 40 5.4.1.2 Sudomotorische autonome Funktion: Sympathetic Skin Response (SSR) 44 5.4.1.3 Vasomotorische autonome Funktion: Photoplethysmographie (PPG) 46 5.4.2 Symptomschwere und funktionelle Beeinträchtigung 48 5.4.2.1 Autonomes Outcome: Survey of Autonomic Symptoms (SAS) 48 5.4.2.2 Funktionelles Outcome: modified Rankin Scale (mRS) 49 5.4.2.3 Neurologisches Outcome: National Institutes of Health Stroke Scale (NIHSS) 49 5.5 Studienintervention: Herzratenvariabilitätsgestütztes Biofeedback 50 5.6 Statistische Analyse 51 6. ERGEBNISSE 52 6.1 Demographische Daten und Baseline-Charakteristika 52 6.2 Rekrutierung und fehlende Daten 54 6.3 Autonome Funktionsmessungen 56 6.3.1 Kardiale autonome Funktion: Herzratenvariabilität (HRV) 56 6.3.2 Sudomotorische autonome Funktion: Sympathetic Skin Response (SSR) 61 6.3.3 Vasomotorische autonome Funktion: Photoplethysmographie (PPG) 61 6.4 Symptomschwere und funktionelle Beeinträchtigung 62 6.4.1 Autonomes Outcome: Survey of Autonomic Symptoms (SAS) 62 6.4.2 Funktionelle Beeinträchtigung: modified Rankin Scale (mRS) 63 7. DISKUSSION 63 7.1. Zentrale Erkenntnisse 63 7.2 Autonome Funktionen 64 7.2.1 Kardiale autonome Funktion: Herzratenvariabilität (HRV) 64 7.2.2 Sudomotorische Funktionsmessung: Sympathetic Skin Response (SSR) 71 7.2.3 Vasomotorische Flussmessung 72 7.3 Symptomschwere und funktionelle Beeinträchtigung 73 7.3.1 Symptome des autonomen Nervensystems: Survey of Autonomic Symptoms (SAS) 73 7.3.2 Funktionelle Beeinträchtigung: modified Rankin Scale (mRS) 75 7.4 Limitationen und Ausblick 76 8. ZUSAMMENFASSUNG 78 8.1 Zusammenfassung 78 8.2. Summary 80 9. LITERATURVERZEICHNIS 82 10. ANHANG 109 10.1 Anhang I Fragebögen Klinischer Outcomes 109 10.2 Anhang II Demographische Daten 112 10.3. Anhang III Autonome Funktionsmessungen 116 10.4 Anhang IV Symptomschwere und funktionelle Beeinträchtigung 119 10.5 Erklärung zur Eröffnung des Promotionsverfahrens 122 10.6 Erklärung zur Einhaltung gesetzlicher Vorgaben 123 / Background: The occurrence of cardiac autonomic dysfunction following acute ischaemic stroke (AIS) worsens clinical outcome and is associated with an increased mortality. Therefore, we tested the hypothesis that heart rate variability (HRV) biofeedback can improve autonomic cardiac function post stroke. Methods/Design: We allocated (1:1) 48 AIS patients in a randomized fashion to undergo nine sessions of either HRV- or sham-biofeedback over three days in addition to standard stroke unit care. Autonomic function measurements, consisting of measurements of HRV, vasomotor (neurovascular control of arterial blood flow) and sudomotor (neural sweat gland control) function, were performed in all study participants before the start of the first biofeedback session and after completion of the last session. HRV was assessed using standard deviation of NN intervals (SDNN), a marker for primarily parasympathetically mediated cardiac modulation, Standard deviation of differences between adjacent NN intervals (SD of ΔNN) and root mean square of successive differences between normal heartbeats (RMSSD), a predominantly parasympathetic measure of HRV as well as via coefficient of variation of R-R intervals (CVNN), a composite parameter of sympathetic and parasympathetic activity. Moreover, frequency analysis of HRV components was carried out to further explore the mechanism whereby HRV biofeedback alters cardiac autonomic function. Both sympathetically regulated vasomotor and sudomotor functions were measured after sympathetic activation with vasomotor function recorded by photoplethysmography (PPG) of vasoconstrictory response (VCR) and sudomotor skin conductance changes of the sympathetic skin response (SSR) by conduction electrodes. Assessment of severity of autonomic symptoms via Survey of Autonomic Symptoms (SAS; TIS: Total symptom score) and functional deficits via modified Rankin scale (mRS) was performed before the start of the intervention and three months post intervention. The study protocol was registered at clinicaltrials.gov prior to commencement of study [clinicaltrials.gov identifier: NCT03865225]. Results: We included 48 AIS patients (19 females; ages median 69 [interquartile range 18.0] years. Implementation of HRV biofeedback into the setting of a stroke unit was feasible with no dropouts and high adherence and tolerability. Adding HRV biofeedback to stroke unit care led to an increased HRV under metronomic breathing (SDNN: 34.1 [45.0] ms baseline vs. 43.5 [79.0] ms post-intervention, p=0.015; SD of ΔNN: 29.3 [52.7] ms baseline vs. 46.4 [142.1] ms post-intervention, p=0.013; RMSSD: 29.1 [52.2] ms baseline vs. 46.0 [140.6] ms post-intervention, p=0.015; non-significant trend towards increase in CVNN: 4.1 [5.1] % baseline vs. 5.4 [7.2] % post-intervention, p=0.052) which was not seen after sham biofeedback (p=non-significant (ns)). In addition, frequency analysis of HRV revealed an increase in the low frequency band (LF) under metronomic breathing (484.8 [1941.4] ms2 baseline vs. 1471.3 [3329.9] ms2 post-intervention, p=0.019 and in total power (Total Power: 1273.9 [3299.2] ms2 baseline vs. 1771.5 [13038.8] ms2 post-intervention, p=0.022) after HRV biofeedback, which was not seen in the sham biofeedback group (p=ns). No changes in sympathetic sudomotor and vasomotor functions were detected in either study group (p=ns). HRV biofeedback led to a decrease of severity of autonomic symptoms (TIS: 7.5 [7.0] baseline vs. 3.5 [8.0] follow-up, p=0.029), which was absent in the sham biofeedback group. (p=ns). As expected both study groups showed an alleviation of functional deficits after three months (HRV biofeedback group, mRS: 2.0 [1.0] baseline vs. 0.0 [2.0] follow-up, p=0.023; Sham biofeedback group, mRS: 2.2 [2.0] baseline vs. 1.0 [2.0] follow-up, p=0.0005). Conclusions: Integrating HRV biofeedback into standard multidisciplinary stroke unit care for AIS led to improved cardiac autonomic function. This functional improvement was likely mediated by a predominantly parasympathetic mechanism and translated into sustained alleviation of autonomic symptoms.:1.EINLEITUNG 1 2. HINTERGRUND 4 2.1 Schlaganfall: Pathophysiologie und klinische Bedeutung 4 2.1.1 Definition und Klassifikation 4 2.1.2 Epidemiologie 7 2.1.3 Lokalisationsbezogene klinische Präsentation 9 2.1.4 Therapie 13 2.1.5 Risikofaktoren 16 2.2 Autonomes Nervensystem (ANS): Grundlagen und Beeinträchtigungen bei Schlaganfallpatienten16 2.2.1 Anatomische und physiologische Grundlagen 17 2.2.1.1 Sympathisches Nervensystem (SNS) 20 2.2.1.2 Parasympathisches Nervensystem (PaNS) 22 2.2.1.3 Enterisches Nervensystem (ENS) 23 2.2.2 Autonome Dysfunktion beim Schlaganfall 24 2.3 Herzratenvariabilität (HRV): Ein diagnostisches Target der kardialen autonomen Funktion 25 2.3.1 Definition 25 2.3.2 Relevanz 27 2.3.3 Anwendungsbereiche 28 2.4 Biofeedback: Allgemeine Therapieprinzipien und HRV-spezifische Anwendung 29 2.4.1 Definition 29 2.4.2 Anwendungsbereiche 29 2.4.3 Herzratenvariabilitäts-gestütztes Biofeedback 31 3. FORSCHUNGSLÜCKE („RESEARCH GAP“) 32 4. ZIELSETZUNG UND HYPOTHESEN 32 5. METHODIK 33 5.1 Ethik 33 5.2 Studiendesign und Messprotokoll 34 5.3 Patienten 36 5.3.1 Patientenrekrutierung 36 5.3.2 Einschlusskriterien 36 5.3.3 Ausschlusskriterien 36 5.3.4 Patienteninformation und -einverständniserklärung 37 5.3.5 Randomisierung 37 5.4 Funktionsmessungen 37 5.4.1 Funktionen des autonomen Nervensystems 37 5.4.1.1 Kardiale autonome Funktion: Herzratenvariabilität (HRV) 40 5.4.1.2 Sudomotorische autonome Funktion: Sympathetic Skin Response (SSR) 44 5.4.1.3 Vasomotorische autonome Funktion: Photoplethysmographie (PPG) 46 5.4.2 Symptomschwere und funktionelle Beeinträchtigung 48 5.4.2.1 Autonomes Outcome: Survey of Autonomic Symptoms (SAS) 48 5.4.2.2 Funktionelles Outcome: modified Rankin Scale (mRS) 49 5.4.2.3 Neurologisches Outcome: National Institutes of Health Stroke Scale (NIHSS) 49 5.5 Studienintervention: Herzratenvariabilitätsgestütztes Biofeedback 50 5.6 Statistische Analyse 51 6. ERGEBNISSE 52 6.1 Demographische Daten und Baseline-Charakteristika 52 6.2 Rekrutierung und fehlende Daten 54 6.3 Autonome Funktionsmessungen 56 6.3.1 Kardiale autonome Funktion: Herzratenvariabilität (HRV) 56 6.3.2 Sudomotorische autonome Funktion: Sympathetic Skin Response (SSR) 61 6.3.3 Vasomotorische autonome Funktion: Photoplethysmographie (PPG) 61 6.4 Symptomschwere und funktionelle Beeinträchtigung 62 6.4.1 Autonomes Outcome: Survey of Autonomic Symptoms (SAS) 62 6.4.2 Funktionelle Beeinträchtigung: modified Rankin Scale (mRS) 63 7. DISKUSSION 63 7.1. Zentrale Erkenntnisse 63 7.2 Autonome Funktionen 64 7.2.1 Kardiale autonome Funktion: Herzratenvariabilität (HRV) 64 7.2.2 Sudomotorische Funktionsmessung: Sympathetic Skin Response (SSR) 71 7.2.3 Vasomotorische Flussmessung 72 7.3 Symptomschwere und funktionelle Beeinträchtigung 73 7.3.1 Symptome des autonomen Nervensystems: Survey of Autonomic Symptoms (SAS) 73 7.3.2 Funktionelle Beeinträchtigung: modified Rankin Scale (mRS) 75 7.4 Limitationen und Ausblick 76 8. ZUSAMMENFASSUNG 78 8.1 Zusammenfassung 78 8.2. Summary 80 9. LITERATURVERZEICHNIS 82 10. ANHANG 109 10.1 Anhang I Fragebögen Klinischer Outcomes 109 10.2 Anhang II Demographische Daten 112 10.3. Anhang III Autonome Funktionsmessungen 116 10.4 Anhang IV Symptomschwere und funktionelle Beeinträchtigung 119 10.5 Erklärung zur Eröffnung des Promotionsverfahrens 122 10.6 Erklärung zur Einhaltung gesetzlicher Vorgaben 123

Introduction : L'apnée obstructive du sommeil (AOS) et ses comorbidités (tel le bruxisme du sommeil (BS)) sont associées à des fluctuations du système nerveux autonome (SNA) chez les adultes et les enfants. La variabilité de la fréquence cardiaque (VFC), qui concerne, entre autres, la fluctuation des intervalles de temps entre les battements cardiaques adjacents, est une méthode non invasive et reproductible d'évaluation de la modulation du SNA. Objectif : Évaluer l'effet individuel et potentiellement cumulatif de ces conditions sur la VFC de la population pédiatrique. Méthodes : Des questionnaires dentaires et de bruxisme ont été remplis par les parents. Les enfants ont subi une évaluation dentaire et un enregistrement polysomnographique. La VFC a été analysée dans une fenêtre de référence de 5 minutes ainsi que dans des fenêtres de 4x3 minutes avant et après les événements d’AOS ou de BS. Résultats : Un total de 41 enfants ont été classés en sous-groupes : BS, contrôle et AOS+BS. Le rapport de puissance spectrale Basse Fréquence/Haute Fréquence (BF/HF), qui est connu pour utiliser les transformations de Fourier rapide, était plus élevé dans le groupe AOS + BS que dans le groupe contrôle (p = 0,01) et le groupe BS (p = 0,04) pour toutes les fenêtres d’analyse combinées (B0-B8). Dans le domaine temporel de la VFC, l'écart type des intervalles RR (SDNN) des fenêtres était plus élevé après chaque événement (B5 à B8) que la ligne de base (B0) pour les 3 groupes (p < 0,05). Conclusion : La VFC est différente entre les trois groupes. / Introduction: Obstructive sleep apnea (OSA) and its comorbid conditions (such as sleep bruxism (SB)) are associated with an autonomic nervous system (ANS) fluctuation in adults and children. Heart rate variability (HRV), which is concerned with, among other things, the fluctuation of the time intervals between adjacent heartbeats, is a non-invasive and reproducible method of assessing the modulation of the ANS. Aim: To assess the individual and potentially cumulative effect of these conditions on HRV in the pediatric population. Methods: Dental and bruxism questionnaires were completed by the parents. Children underwent a dental assessment and polysomnographic recording. HRV was analyzed in a 5-minute baseline windows as well as in 4x3-minute windows before and after OSA or SB events. Results: A total of 41 children were classified into subgroups: SB, control and OSA+SB. The Low Frequency/High Frequency (LF/HF) power ratio, which is known to use Fast Fourier Transforms, was higher in the AOS + SB group than in the control group (p = 0.01) and the SB group (p=0.04) for all analysis windows combined (B0-B8). In the time domain of the HRV, the standard deviation of the RR intervals (SDNN) of the windows was higher after each event (B5 to B8) than the baseline (B0) for the 3 groups (p < 0.05). Conclusion: HRV is different between the three groups.

No description available.

Background The study aimed to investigate the link between burnout symptoms and prosocial behaviour, as well as the role of acute stress and vagally-mediated heart rate variability (vmHRV) on this association. Methods Seventy men were randomly assigned to either the stress or the control condition of the Trier Social Stress Test for Groups (TSST-G). Prosocial behaviour was assessed via a social decision-making paradigm during the respective TSST-G condition. Results Correlation analyses revealed negative correlations between prosocial behaviour and burnout symptoms. Acute stress was also associated with reduced prosocial behaviour, whereas no interaction effects with burnout symptoms could be revealed. Exploratory analyses showed that vmHRV was negatively correlated with burnout symptoms during the social decision-making paradigm but did not mediate the link between burnout and prosocial behaviour. Conclusion In conclusion, we report first experimental evidence that burnout symptoms are negatively associated with prosocial behaviour. Further studies are needed to explore the causal relations.

Objective: Restorative sleep is associated with increased autonomous parasympathetic nervous system activity that might be improved by heart rate variability-biofeedback (HRV-BF) training. Hence the aim of this study was to investigate the effect of a four-week mobile HRV-BF intervention on the sleep quality and HRV of healthy adults. - Methods: In a prospective study, 26 healthy participants (11 females; mean age: 26.04 ± 4.52 years; mean body mass index: 23.76 ± 3.91 kg/m²) performed mobile HRV-BF training with 0.1 Hz breathing over four weeks, while sleep quality, actigraphy and HRV were measured before and after the intervention. - Results: Mobile HRV-BF training with 0.1 Hz breathing improved the subjective sleep quality in healthy adults [t(24) = 4.9127, p ≤ 0.001, d = 0.99] as measured by the Pittsburgh Sleep Quality Index. In addition, mobile HRV-BF training with 0.1 Hz breathing was associated with an increase in the time and frequency domain parameters SDNN, Total Power and LF after four weeks of intervention. No effect was found on actigraphy metrics. - Conclusions: Mobile HRV-BF intervention with 0.1 Hz breathing increased the reported subjective sleep quality and may enhance the vagal activity in healthy young adults. HRV-BF training emerges as a promising tool for improving sleep quality and sleep-related symptom severity by means of normalizing an impaired autonomic imbalance during sleep.