Using cardiovascular measures for adaptive automation

Adaptieve automatisering betekent zoveel als een technologisch ingerichte werkomgeving die zich aanpast aan de gebruiker. Binnen het onderzoeksgebied adaptieve automatisering worden technologische systemen ontwikkeld die flexibel zijn en zich kunnen aanpassen aan de specifieke behoeften en eisen van de individuele mens. Het idee is dat het hele systeem (mens en machine samen) het best functioneert als de werklast van de mens op een optimaal niveau wordt gehouden. Enerzijds moet voorkomen worden dat de hele taak geautomatiseerd wordt, zodat de menselijke bestuurder niet meer snel en adequaat kan ingrijpen als er iets mis gaat met het geautomatiseerde systeem. Anderzijds moet voorkomen worden dat oververmoeidheid en concentratieverlies optreden door een voortdurende (te) hoge werklast . Meten is weten. Een belangrijke eerste voorwaarde om de werklast op een adequaat niveau te houden is deze te kunnen meten. Een geschikte manier om mentale belasting te bepalen is met behulp van fysiologische methoden, in het bijzonder met behulp van hartslag, bloeddruk en ademhalingsmaten. In de literatuur is er nog enige onduidelijkheid over de relatie tussen mentale inspanning en de psychofysiologische reacties daarop. In dit proefschrift worden een aantal onduidelijkheden over deze relatie verklaard aan de hand van een onderscheid dat gemaakt wordt tussen toestand-gerelateerde (of compensatoire) effecten en korte termijn effecten die naar verwachting meer rechtstreeks verband houden met veranderingen in de taakeisen. De toestand gerelateerde effecten zijn pogingen van het lichaam om te herstellen van langdurige inspanning en terug te keren naar een evenwichtssituatie. De korte termijn effecten ontstaan doordat het lichaam reageert op momentane verschillen in werklast waar het lichaam energie voor moet leveren. In het beschreven onderzoek is een aantal experimenten in een gesimuleerde ambulancemeldkamer en in een rijsimulator uitgevoerd op basis waarvan een nieuwe methode is ontwikkeld die meer inzicht geeft in de momentane werklast van de mens. Deze methode leent zich ook uitstekend voor gebruik bij adaptieve automatisering. In het kort kan worden gezegd dat de toestandseffecten, met andere woorden het herstellen van inspanning, de effecten die door de huidige werklast worden veroorzaakt, in veel werksituaties overschaduwen. Hierdoor zijn de directe effecten van werklastveranderingen vaak niet zichtbaar. De oplossing die in dit proefschrift wordt beschreven is een keuze voor korte termijn cardiovasculaire maten. Daarmee zijn verschillen in hartslag, hartslagvariabiliteit en andere cardiovasculaire maten nog steeds zichtbaar in de korte termijn respons-patronen, ondanks de aanwezigheid van de toestandseffecten. De korte termijn analyse berust op een tijd-frequentie methode waarbij de variabiliteit van de cardiovasculaire maten wordt berekend in tijdsegmenten van 30 seconden. Deze methode is getoetst in experimenten die opnieuw in de ambulance meldkamer simulatie en rijsimulator zijn uitgevoerd. De conclusie is dat de verschillen in gevonden effecten in eerder beschreven onderzoek m.b.t. werkbelastingmaten in belangrijke mate verklaard kunnen worden uit de compenserende werking van het bloeddrukregulatie systeem. De compenserende werking van het bloeddrukregulatiesysteem nivelleert de directe effecten van mentale inspanning en maakt daarmee de interpretatie van de werklast-effecten op de cardiovasculaire maten moeilijker. Dit geldt met name voor de hartslag en hartslagvariabiliteit. Door onderscheid te maken tussen effecten die direct gerelateerd zijn aan de taakeisen en effecten die veroorzaakt worden door het bloeddruk regulatiesysteem, krijgen we meer zicht op de echte taakgerelateerde mentale inspanning. Dit heeft geleid tot de ontwikkeling van betere mentale werklast-maten die bruikbaar zijn bij adaptieve automatisering

Estimación robusta de la diferencia del tiempo de tránsito del pulso sanguíneo a partir de señales fotopletismográficas

En el presente trabajo se va a estudiar la posibilidad de detectar estrés mental utilizando técnicas no invasivas basadas en la señal fotopletismográfica de pulso (PPG). Para ello se pretende detectar cambios en la velocidad de pulso arterial (PWV), utilizando señales de PPG tomadas en dos puntos distintos del árbol arterial con las que poder medir el tiempo de llegada de pulso arterial a la periferia (PAT) y la diferencia de ese tiempo de llegada entre dos puntos de la periferia distintos (PTTD). Tanto el PAT como el PTTD han sido propuestas en la bibliografía como medidas influenciados por el Tiempo de Tránsito de Pulso (PTT), este último capaz de medir cambios en la dinámica cardiovascular. Sin embargo, el PTTD, al contrario que el PAT, no necesita del electrocardiograma (ECG) para ser obtenido y no está influenciado por el periodo de pre-eyección (PEP) -un intervalo de tiempo en la sístole ventricular que cambia pulso a pulso- el cual genera que el PAT pierda la relación con el PTT, dos factores importantes que aventajan al PTTD frente al PAT. Primero, se estudia de fiabilidad de los puntos fiduciales para la detección de los pulsos de la señal PPG y con ésto comprobar cuál es el método con la mayor precisión. Se demuestra mediante diversos análisis que el mejor punto para detectar los pulsos corresponde al valor de la PPG en el instante de máxima pendiente (valor máximo en la primera derivada). Resulta necesario implementar un detector de artefactos ya que el método de adquisición de la PPG es muy sensible a ellos pudiendo llegar a haber segmentos en los que la señal registrada es absolutamente inutilizable. Posteriormente, se analizan 14 voluntarios sanos sometidos a un protocolo de estrés y se realiza un test estadístico para comprobar la validez del método propuesto. Los resultados muestran que la desviación estándar de la PTTD tiene la capacidad estadística suficiente como para discernir entre estados de estrés y de relajación, para cada uno de los sujetos por separado. Además, se puede ver una tendencia descendente generalizada del descenso de la PTTD en situación de estrés con respecto a relajación. %Sin embargo, resultará necesario repetir el análisis con una muestra de señales mayor ya que se dispone de pocos sujetos en la base de datos utilizada, ya que la calidad de la señal de PPG que se registró en la frente es muy mala y hay muy pocos sujetos con los que se puede computar la PTTD. A modo de conclusión, se ha visto que la PTTD contiene información fisiológica que puede ser interesante para la detección de estrés. A su vez, también es una técnica potencialmente interesante para otros tipos de aplicaciones clínicas tales como la estimación no invasiva de la presión arterial o la evaluación de la rigidez arterial, pero se necesita estudiar la adecuación de ésta en cada escenario en particular. Además, como la PTTD se puede medir a partir de únicamente dos señales PPG, la técnica es idónea para dispositivos wearable y smartphones

Searching for improvement

Engineering design can be thought of as a search for the best solutions to engineering problems. To perform an effective search, one must distinguish between competing designs and establish a measure of design quality, or fitness. To compare different designs, their features must be adequately described in a well-defined framework, which can mean separating the creative and analytical parts of the design process. By this we mean that a distinction is drawn between coming up with novel design concepts, or architectures, and the process of detailing or refining existing design architecture. In the case of a given design architecture, one can consider the set of all possible designs that could be created by varying its features. If it were possible to measure the fitness of all designs in this set, then one could identify a fitness landscape and search for the best possible solution for this design architecture. In this Chapter, the significance of the interactions between design features in defining the metaphorical fitness landscape is described. This highlights that the efficiency of a search algorithm is inextricably linked to the problem structure (and hence the landscape). Two approaches, namely, Genetic Algorithms (GA) and Robust Engineering Design (RED) are considered in some detail with reference to a case study on improving the design of cardiovascular stents

Co-ordination of brain and heart oscillations during non-rapid eye movement sleep

Oscillatory activities of the brain and heart show a strong variation across wakefulness and sleep. Separate lines of research indicate that non‐rapid eye movement (NREM) sleep is characterised by electroencephalographic slow oscillations (SO), sleep spindles, and phase–amplitude coupling of these oscillations (SO–spindle coupling), as well as an increase in high‐frequency heart rate variability (HF‐HRV), reflecting enhanced parasympathetic activity. The present study aimed to investigate further the potential coordination between brain and heart oscillations during NREM sleep. Data were derived from one sleep laboratory night with polysomnographic monitoring in 45 healthy participants (22 male, 23 female; mean age 37 years). The associations between the strength (modulation index [MI]) and phase direction of SO–spindle coupling (circular measure) and HF‐HRV during NREM sleep were investigated using linear modelling. First, a significant SO–spindle coupling (MI) was observed for all participants during NREM sleep, with spindle peaks preferentially occurring during the SO upstate (phase direction). Second, linear model analyses of NREM sleep showed a significant relationship between the MI and HF‐HRV (F = 20.1, r (2) = 0.30, p < 0.001) and a tentative circular‐linear correlation between phase direction and HF‐HRV (F = 3.07, r (2) = 0.12, p = 0.056). We demonstrated a co‐ordination between SO–spindle phase–amplitude coupling and HF‐HRV during NREM sleep, presumably related to parallel central nervous and peripheral vegetative arousal systems regulation. Further investigating the fine‐graded co‐ordination of brain and heart oscillations might improve our understanding of the links between sleep and cardiovascular health

Improved 3D MR Image Acquisition and Processing in Congenital Heart Disease

Congenital heart disease (CHD) is the most common type of birth defect, affecting about 1% of the population. MRI is an essential tool in the assessment of CHD, including diagnosis, intervention planning and follow-up. Three-dimensional MRI can provide particularly rich visualization and information. However, it is often complicated by long scan times, cardiorespiratory motion, injection of contrast agents, and complex and time-consuming postprocessing. This thesis comprises four pieces of work that attempt to respond to some of these challenges. The first piece of work aims to enable fast acquisition of 3D time-resolved cardiac imaging during free breathing. Rapid imaging was achieved using an efficient spiral sequence and a sparse parallel imaging reconstruction. The feasibility of this approach was demonstrated on a population of 10 patients with CHD, and areas of improvement were identified. The second piece of work is an integrated software tool designed to simplify and accelerate the development of machine learning (ML) applications in MRI research. It also exploits the strengths of recently developed ML libraries for efficient MR image reconstruction and processing. The third piece of work aims to reduce contrast dose in contrast-enhanced MR angiography (MRA). This would reduce risks and costs associated with contrast agents. A deep learning-based contrast enhancement technique was developed and shown to improve image quality in real low-dose MRA in a population of 40 children and adults with CHD. The fourth and final piece of work aims to simplify the creation of computational models for hemodynamic assessment of the great arteries. A deep learning technique for 3D segmentation of the aorta and the pulmonary arteries was developed and shown to enable accurate calculation of clinically relevant biomarkers in a population of 10 patients with CHD

Co-ordination of brain and heart oscillations during non rapid eye movement sleep

Oscillatory activities of the brain and heart show a strong variation across wakefulness and sleep. Separate lines of research indicate that non-rapid eye movement (NREM) sleep is characterised by electroencephalographic slow oscillations (SO), sleep spindles, and phase–amplitude coupling of these oscillations (SO–spindle coupling), as well as an increase in high-frequency heart rate variability (HF-HRV), reflecting enhanced parasympathetic activity. The present study aimed to investigate further the potential coordination between brain and heart oscillations during NREM sleep. Data were derived from one sleep laboratory night with polysomnographic monitoring in 45 healthy participants (22 male, 23 female; mean age 37 years). The associations between the strength (modulation index [MI]) and phase direction of SO–spindle coupling (circular measure) and HF-HRV during NREM sleep were investigated using linear modelling. First, a significant SO–spindle coupling (MI) was observed for all participants during NREM sleep, with spindle peaks preferentially occurring during the SO upstate (phase direction). Second, linear model analyses of NREM sleep showed a significant relationship between the MI and HF-HRV (F = 20.1, r2 = 0.30, p < 0.001) and a tentative circular-linear correlation between phase direction and HF-HRV (F = 3.07, r2 = 0.12, p = 0.056). We demonstrated a co-ordination between SO–spindle phase–amplitude coupling and HF-HRV during NREM sleep, presumably related to parallel central nervous and peripheral vegetative arousal systems regulation. Further investigating the fine-graded co-ordination of brain and heart oscillations might improve our understanding of the links between sleep and cardiovascular health

Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest

The automatic detection of pulse during out-of-hospital cardiac arrest (OHCA) is necessary for the early recognition of the arrest and the detection of return of spontaneous circulation (end of the arrest). The only signal available in every single defibrillator and valid for the detection of pulse is the electrocardiogram (ECG). In this study we propose two deep neural network (DNN) architectures to detect pulse using short ECG segments (5 s), i.e., to classify the rhythm into pulseless electrical activity (PEA) or pulse-generating rhythm (PR). A total of 3914 5-s ECG segments, 2372 PR and 1542 PEA, were extracted from 279 OHCA episodes. Data were partitioned patient-wise into training (80%) and test (20%) sets. The first DNN architecture was a fully convolutional neural network, and the second architecture added a recurrent layer to learn temporal dependencies. Both DNN architectures were tuned using Bayesian optimization, and the results for the test set were compared to state-of-the art PR/PEA discrimination algorithms based on machine learning and hand crafted features. The PR/PEA classifiers were evaluated in terms of sensitivity (Se) for PR, specificity (Sp) for PEA, and the balanced accuracy (BAC), the average of Se and Sp. The Se/Sp/BAC of the DNN architectures were 94.1%/92.9%/93.5% for the first one, and 95.5%/91.6%/93.5% for the second one. Both architectures improved the performance of state of the art methods by more than 1.5 points in BAC.This work was supported by: The Spanish Ministerio de Economía y Competitividad, TEC2015-64678-R, jointly with the Fondo Europeo de Desarrollo Regional (FEDER), UPV/EHU via GIU17/031 and the Basque Government through the grant PRE_2018_2_0260

Understanding safety-critical interactions with a home medical device through Distributed Cognition

As healthcare shifts from the hospital to the home, it is becoming increasingly important to understand how patients interact with home medical devices, to inform the safe and patient-friendly design of these devices. Distributed Cognition (DCog) has been a useful theoretical framework for understanding situated interactions in the healthcare domain. However, it has not previously been applied to study interactions with home medical devices. In this study, DCog was applied to understand renal patients’ interactions with Home Hemodialysis Technology (HHT), as an example of a home medical device. Data was gathered through ethnographic observations and interviews with 19 renal patients and interviews with seven professionals. Data was analyzed through the principles summarized in the Distributed Cognition for Teamwork methodology. In this paper we focus on the analysis of system activities, information flows, social structures, physical layouts, and artefacts. By explicitly considering different ways in which cognitive processes are distributed, the DCog approach helped to understand patients’ interaction strategies, and pointed to design opportunities that could improve patients’ experiences of using HHT. The findings highlight the need to design HHT taking into consideration likely scenarios of use in the home and of the broader home context. A setting such as home hemodialysis has the characteristics of a complex and safety-critical socio-technical system, and a DCog approach effectively helps to understand how safety is achieved or compromised in such a system