Impact of QRS misclassifications on heart-rate-variability parameters (results from the CARLA cohort study)

Background Heart rate variability (HRV), an important marker of autonomic nervous system activity, is usually determined from electrocardiogram (ECG) recordings corrected for extrasystoles and artifacts. Especially in large population-based studies, computer-based algorithms are used to determine RR intervals. The Modular ECG Analysis System MEANS is a widely used tool, especially in large studies. The aim of this study was therefore to evaluate MEANS for its ability to detect non-sinus ECG beats and artifacts and to compare HRV parameters in relation to ECG processing. Additionally, we analyzed how ECG processing affects the statistical association of HRV with cardiovascular disease (CVD) risk factors. Methods 20-min ECGs from 1,674 subjects of the population-based CARLA study were available for HRV analysis. All ECGs were processed with the ECG computer program MEANS. A reference standard was established by experienced clinicians who visually inspected the MEANS-processed ECGs and reclassified beats if necessary. HRV parameters were calculated for 5-minute segments selected from the original 20-minute ECG. The effects of misclassified typified normal beats on i) HRV calculation and ii) the associations of CVD risk factors (sex, age, diabetes, myocardial infarction) with HRV were modeled using linear regression. Results Compared to the reference standard, MEANS correctly classified 99% of all beats. The averaged sensitivity of MEANS across all ECGs to detect non-sinus beats was 76% [95% CI: 74.1;78.5], but for supraventricular extrasystoles detection sensitivity dropped to 38% [95% CI: 36.8;38.5]. Time-domain parameters were less affected by false sinus beats than frequency parameters. Compared to the reference standard, MEANS resulted in a higher SDNN on average (mean absolute difference 1.4ms [95% CI: 1.0;1.7], relative 4.9%). Other HRV parameters were also overestimated as well (between 6.5 and 29%). The effect estimates for the association of CVD risk factors with HRV did not differ between the editing methods.</p

Application of AdaptIVe Evaluation Methodology.

Since the last decade, development efforts by academia and industry for automated driving functions have increased significantly. Also, the European research project AdaptIVe is looking into this topic. Within AdaptIVe, 21 different automated driving functions for different speed ranges and target areas have been developed. They have been developed in three sub projects (SPs), addressing different automation scenarios:Sub project 4: Automation in close-distance scenarios: Addresses manoeuvres at low speed (below 30 km/h) that are characterised by the presence of close obstacles, such as in parking manoeuvres.Sub project 5: Automation in urban scenarios: Deals with driving scenarios in urban environments that are characterised by an average speed range of 0 to 70 km/h.Sub project 6: Automation in highway scenarios: Addresses motorway scenarios (or motorway similar roads) considering velocities up to 130 km/h. Due to the large operation spaces and various complex situations that are covered by these functions, efforts for evaluation are expected to increase significantly. In order to enable an efficient assessment of automated driving functions, within the subproject 7 a comprehensive evaluation methodology addressing this challenge has been developed. Technical Assessment: Evaluates the performance of the developed automated driving functions with respect to a defined baseline.User-related Assessment: Analyses the interaction between the function and the user, trust, usability as well as acceptance of the developed functions.In-Traffic Assessment: Focuses on the effects of the surrounding traffic on the automated driving function as well as the effects of the automated driving function on the surrounding non-users.Impact Assessment: Determines the potential effects of the function with respect to safety and environmental aspects (e.g. fuel consumption, traffic efficiency).The evaluation methodologies developed in previous research projects dealt mainly with active safety functions, for which the assessment focused mainly on testing of functions’ use cases. For automated driving the assessment approach needs to be extended in order to ensure that the whole situation space which is addressed by the functions is covered. Therefore, in the developed evaluation approach the test resources are allocated based on the functions’ classification in order to enable a holistic and efficient assessment. Hence, the automated driving functions are classified based on their automation level and their operation time in two different function types: •Functions that operate only for a short period of time (seconds up to few minutes). Typical examples are automated parking functions and the minimum risk manoeuvre function. These functions are called "event based”.•Functions that, once they are active, can operate over a longer period of time (minutes up to hours). A typical example of this type of function is a “highway pilot”. They are called "continuously operating" functions

Potential societal benefits by increasing vehicle automation

Im vorliegenden Forschungsvorhaben wird der potenzielle gesellschaftliche Nutzen durch die zunehmende Fahrzeugautomatisierung untersucht. Dazu werden primär die Potenziale hinsichtlich der Verkehrssicherheit und in einem weiteren Schritt die Potenziale zur Steigerung der Verkehrseffizienz und zur Änderung des Energiebedarfs analysiert. Dabei werden vom Stau-Chauffeur bis zum Urbanen Roboter-Taxi insgesamt fünf verschiedene Fahrfunktionen bei vier verschiedenen Marktdurchdringungsszenarien (5 %, 25 %, 50 %, 100 %) analysiert. Um die Potenziale der Fahrzeugautomatisierung hinsichtlich der Verkehrssicherheit zu ermitteln, wird nach der Bestimmung der Wirkfelder der jeweiligen Fahrfunktionen eine zweiteilige Methode angewendet. Diese berücksichtigt neben der Bestimmung der Änderung der Unfallschwere durch Unfallresimulationen auch die Änderung der Auftretenshäufigkeit der Szenarien. Da automatisierte Fahrzeuge im Gegensatz zu Systemen der aktiven Sicherheit kontinuierlich arbeiten, ist es wahrscheinlich, dass bestimmte Unfallszenarien (z. B. Auffahrszenarien) durch automatisierte Fahrfunktionen nicht mehr so häufig hervorgerufen werden. Die durch automatisierte Fahrzeuge induzierte Änderung der Auftretenshäufigkeiten verschiedener Szenarien wird mit einer Verkehrssimulation ermittelt. Mittels einer Hochrechnungsmethodik werden die Simulationsergebnisse auf das gesamte Bundesgebiet skaliert. Dabei zeigt sich, dass z. B. durch den Autobahn-Chauffeur bei einer Durchdringungsrate von 50 % rund 30 % aller Unfälle mit Personenschaden auf deutschen Autobahnen verhindert werden können. Dies entspricht ca. 2 % aller Unfälle mit Personenschaden auf deutschen Straßen. Zur Abschätzung der Potenziale hinsichtlich des Energiebedarfs wird die Änderung des streckenbezogenen Energiebedarfs der Fahrzeuge induziert durch automatisiertes Fahren untersucht. Die Betrachtung findet ebenfalls unter Nutzung von Verkehrssimulationen statt.In this research report the potenzial societal benefits induced by advancing vehicle automation are investigated. On the one hand the potenzial for safety benefits is considered, on the other hand the potenzials for reducing fuel consumption and pollutant emissions and increasing traffic efficien-cy are investigated. The main objective of this research is to assess the potenzial of increasing road safety. Within this investigation, five different automated driving functions ranging from a Traffic Jam-Chauffeur to an Urban Robot-Taxi are examined for four different market penetrations (5%, 25%, 50%, 100%). For identifying the benefits with respect to road safety, methods incorporating the characteristics of automated driving functions are used. Hence, besides investigating the change of severity of the accident by using accident resimulations, the change of frequency of occurrence induced by automated driving is considered as well. This is in particularly necessary since automated driving functions in contrast to active safety systems continuously control the behavior of the vehicle. Thus, it is possible that certain accident scenarios (e.g. rear-end scenarios) will occur less frequently with the introduction of automated driving functions. These changes in frequency of occurrence of accident scenarios are analysed by using traffic simulations. After determining the effectiveness of the automated driving functions, they are projected and depicted over the whole territory of the Federal Republic of Germany. The results indicate that, e.g. a Motorway- Chauffeur at a market penetration of 50% has a potenzial for reducing about 30% of all accidents on German motorways resulting in personal injury. This equals 2% of all accidents with personal injuries on German roads. In order to estimate the potenzials of automated driving functions concerning the energy demand the distance related energy demand will be investigated. For this purpose, the effects of automated driving functions in larger traffic scenarios will be assessed and carried out by using traffic simulations