24 results
Now showing1 - 10 of 24
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Radarbasierte Erfassung von Herzratenveränderungen bei Schmerz- und DyspnoereizHintergrund: Die berührungslose und belastungsfreie radarbasierte Erfassung der Herzrate (HR) mit EKG-äquivalenter Genauigkeit konnte auf einer Palliativstation gezeigt werden ([1]). Hierbei zeigten sich Hinweise auf HR-Effekte durch Symptome. Im klinischen Setting unterliegt die HR verschiedenen Einflüssen. Standardisierte Modelle bei gesunden Probanden können helfen die radarbasierten Messverfahren bezüglich der Symptomerkennung zu validieren. Fragestellung: Kann radarbasierte HR-Erfassung Effekte von Schmerz- und Dyspnoemodellen unter standardisierten Laborbedingungen bei gesunden Probanden im Vergleich zu Selbstreport (iPOS) und EKG reliabel erfassen? Methodik: 37 gesunde Proband:innen durchliefen eine Ruhephase (RP), gefolgt von Intervention (IV) 1, einer RP, einer zweiten IV und einer abschließenden RP. Schmerz wurde durch den Cold-Pressor-Test (CPT; Hand in Eiswasser) induziert, Dyspnoe durch laminar erhöhten Atemwiderstand mittels ausschließlichen Atmens durch einen Strohhalm. Die Abfolge der Interventionen war cross-over randomisiert. Die subjektive Symptomstärke wurde mittels iPOS erfasst und Gruppen gebildet (0–2 vs. 3–4). Die HR wurde kontinuierlich über ein sechs-Kanal-EKG sowie ein 61GHz-Radarsystem erfasst ([2]). Ergebnisse: EKG- und Radar-Messungen korrelierten stark (Bias: -0,26 bpm, SD: 4,44 bpm, Limits of Agreement: -8,96 bis 8,44 bpm). Im Median wurden Schmerz als 3 und Dyspnoe als 2 (iPOS) empfunden. Die HR zeigte Unterschiede zwischen Baseline und CPT (p<0,001) sowie zwischen niedriger und hoher Symptomstärke (iPOS 0–2 vs. 3–4, p=0,038). Für Dyspnoe waren Baseline vs. Intervention signifikant (p<0,0001), die subjektive Ausprägung nach iPOS zeigte keinen Unterschied (p=0,278). Schlussfolgerung: Es konnte bei gesunden Probanden ein Dyspnoemodell etabliert werden, das sowohl in psychometrischer Selbsteinschätzung als auch in physiologischen Parametern Atemnot darstellt. Schmerz- und Dyspnoereize erzeugen messbare Änderungen der radarbasierten Herzrate. Dies unterstützt die Interpretation von Daten, die in klinischem Setting an Patient:innen erfasst werden. Nächste Schritte umfassen die Untersuchung von Quereffekten und komplexen Interventionen.Publicationtype: Journal Article2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , CARE-HOUSE: developing a framework for conceptualizing social implications of digital health technologies in palliative care(Springer, 2026-04-27); ; ; ; Background: The use of digital health technologies holds potential benefits for palliative care. Their implementation is fundamentally shifting routines and care practices. These shifts entail social implications that are complex, may emerge gradually and are challenging to identify. However, the social implications of digital health technologies are not conceptualized yet. This study addresses this gap by developing a framework on the social implications of digital technologies in palliative care, where the importance of social inclusion and interpersonal connection is particularly evident. Methods: We assessed the potential social implications of digital health technologies’ use in palliative care through a sequential research design using qualitative empirical methods of social research. Alongside conducting an iterative narrative literature review, we held multidisciplinary expert consultations using a conceptual mapping method and focus groups involving researchers, clinicians, and a patient and public involvement group, analyzed with qualitative structured content analysis. Results: Participants as well as the literature review identified key areas to the understanding and analysis of the social implications of digital health technologies in palliative care: principles and objectives of palliative care (patient-centered care, Total care, multiprofessional collaboration, relieving suffering, improving quality of life); the various actors involved in a specific care practice; the different roles they might have; the interactions among different actors; the tasks individual actors might carry out; the processes they are involved in; and the contexts they are embedded in. These factors were summarized and set out in the CARE-HOUSE Framework to conceptualize the social implications of digital health technologies’ use in palliative care and to support researchers in assessing the impacts of these technologies. Conclusions: The CARE-HOUSE Framework provides guidance for analyzing the social implications of digital health technologies in palliative care. It will be for future studies to evaluate the framework’s adaptability and scalability to diverse technologies and settings.Publicationtype: Journal Article56 13 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Radar-based inspiratory-to-expiratory time ratio estimation: a validation study(Springer, 2026-03-04); ; ; Respiration is a key indicator of health and wellbeing, with metrics such as respiratory rate (RR), inspiratory time (TI), expiratory time (TE), and the inspiratory-to-expiratory time (I:E) ratio offering insights into conditions ranging from acute life-threatening and chronic diseases to symptom management. While traditional methods already measure these parameters with high accuracy, they still require contact-based sensors, limiting their practicality for continuous monitoring. This study evaluates radar as a non-contact alternative by validating multiple radar-derived respiratory metrics against impedance pneumography measurements in 30 healthy volunteers at rest. Synchronous recordings from both modalities were analysed to assess agreement across methods using descriptive statistics, scatter plots, modified Bland-Altman plots, and equivalence testing (TI: ±0.3 s, TE: ±0.3 s, RR: ±2 brpm, I:E ratio: ±0.2). Equivalence testing indicated high correlation (p ≤ 0.001***) across all metrics, with 81.8% (TI), 77.6% (TE), 97.2% (RR), and 85.7% (I:E ratio) of values within predefined bounds. These findings highlight radar's potential for continuous respiratory monitoring, particularly in medical fields where minimizing patient burden is essential as in palliative, post anaesthesia, and intensive care settings.Publicationtype: Journal Article29 12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , PEPbench - Open, reproducible, and systematic benchmarking of automated pre-ejection period extraction algorithmsThe pre-ejection period (PEP) is a widely used cardiac parameter in psychophysiology that reflects the duration between the onset of ventricular depolarization and the opening of the aortic valve. PEP is often used as a marker of cardiac sympathetic nervous system (SNS) activity, particularly in within-subject comparisons under similar hemodynamic conditions. While many algorithms for automated PEP extraction from electrocardiography (ECG) and impedance cardiography (ICG) signals (more precisely, its first derivative, dZ/dt) have been proposed in literature, they have not been systematically benchmarked. This lack of standardized algorithm comparisons originates from the absence of open-source algorithms and annotated datasets for evaluating PEP extraction algorithms. To address this issue, we introduce PEPbench, an open-source Python package with different Q-peak and B-point detection algorithms from literature that can be combined to create comprehensive PEP extraction pipelines, and a standardized framework for evaluating PEP extraction algorithms. We use PEPbench to systematically compare 108 different algorithm combinations. All combinations are evaluated on two datasets with manually annotated Q-peaks and B-points, which we make publicly available as the first datasets with reference PEP annotations. Our results show that the algorithms can differ vastly in their performance and that B-point detection algorithms introduce a considerable amount of error. Thus, we suggest that automated PEP extraction algorithms should be used with caution on a beat-to-beat level as their error rates are relatively high. This highlights the need for open and reproducible benchmarking frameworks for PEP extraction algorithms to improve the quality of research findings in the field of psychophysiology. With PEPbench, we aim to take a first step toward this goal and encourage other researchers to engage in the evaluation of PEP extraction algorithms by contributing algorithms, data, and annotations. We hope to establish a community-driven platform, fostering innovation and collaboration in the field of psychophysiology and beyond.Publicationtype: Journal Article40 23 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Radiometric measurement of the effect of nonpharmacological interventions on vital signs of patients with palliative care using the example of music therapy(Liebert, 2025-08-20); ; ; ; Background: Nonpharmacological, psychosocial interventions such as music therapy (MT) are common in palliative care. However, measuring the effects of these interventions is challenging. Contactless and therefore burden-free vital sign monitoring may provide a feasible solution. Aim: The aim is to investigate the use of a radar system for measuring the effect of nonpharmacological interventions on heart and respiratory rate in patients with palliative care utilizing MT as an example. Design: Radar devices were installed under patients’ mattresses of the palliative care ward to record heart and respiratory rate data. The pre-, peri-, and post-intervention heart rates were compared for 10-minute intervals, respectively. Heart rate changes were assessed for all interventions and two subgroups (receiving MT and MT with physiotherapy). Data from 77 patients were recorded as part of the GUARDIAN project performed at the palliative care unit of the University Hospital Erlangen-Nürnberg. Results: The heart rate of patients with palliative care was monitored continuously, prior to and following a complex intervention. Significant changes in heart rate were recorded depending on the intervention: A reduction of heart rate by −3.342 bpm (−3.913%, ±6.011 bpm, p = 0.0229) was found in the first 10 minutes after the MT intervention. The monitoring of the respiratory rate was only possible on an intermittent basis. Conclusions: Our study shows that radiometric heart rate monitoring is feasible during MT, highlighting the radar system’s potential for assessing complex interventions in palliative care. However, reliability issues in respiratory rate measurement call for further research.Publicationtype: Journal Article24 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Continuous-wave radar and motion-derived biomarkers for non-contact vital status classification in end-of-life care: A clinically validated machine learning approach(IEEE, 2025-08-18); ; ; ; In palliative care, effective communication about anticipated death is critical for aligning therapeutic goals, managing family expectations, and ensuring dignified care. However, prognostic uncertainty - particularly regarding the time of death - remains a challenge due to the limited reliability of current methods. This study explores the potential of radar-derived motion biomarkers as a novel approach to distinguish between living and deceased patients, addressing the need for objective decision-support tools in palliative care. Using continuous-wave radar, we recorded the torso displacement (distance signal) of 16 palliative care patients during their dying phase and derived ground-truth annotations from electronic health records (EHR). Machine learning (ML) algorithms processed 5-minute segments of radar-derived motion signals for binary vital status classification. We evaluated the results with balanced accuracy, Gini gain, and SHAP values. Palliative care specialists provided qualitative feedback to ensure clinical relevance. The ML models achieved balanced accuracy of 0.92-0.98 in distinguishing vital states, demonstrating radar technology's potential as an objective monitoring tool. This study is the first to investigate continuous motion biomarkers in end-of-life patients under real-world clinical conditions, capturing physiological changes during this critical phase. Limitations include the challenges in EHR-derived annotation accuracy, as well as the inherent complexity of physiological variability near death. Our findings highlight radar technology's viability for complementary vital status monitoring in palliative care settings. By providing objective data, this approach could reduce prognostic uncertainty while maintaining patient dignity. This work bridges technological innovation with palliative care's humanistic ethos, offering new possibilities for evidence-based end-of-life management.Publicationtype: Journal Article53 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Radarbasierte Atemerfassung auf einer Palliativstation(Thieme, 2025-04-28); ; ; Zusammenfassung: In der Palliativversorgung werden technische Hilfsmittel wie EKG oder Pulsoxymetrie nur sparsam eingesetzt, um Zuwendung und Beziehungsarbeit nicht zu stören. Gleichzeitig ist Symptomerkennung essenziell und viele Patient*innen können diese nicht selbst äußern. Diese Studie untersucht erstmals den Einsatz eines Radarsystems zum berührungslosen Atemfrequenzmonitoring bei Palliativpatient*innen. In einer prospektiven Studie wurde radarbasiertes Atemfrequenzmonitoring mit einer Goldstandardmethode bei stationär behandelten Palliativpatient*innen verglichen. Die Auswertung bei 13 Patient*innen zeigte eine Übereinstimmung der Atemfrequenzerfassungen durch Radar und Gold-Standardverfahren (n=13, Korrelation r=0,93; 88% der Atemfrequenzen innerhalb ±2 Atemzügen im Vergleich zu Goldstandard). Verlaufsanalysen ergaben Hinweise auf ein Potenzial zur Symptomerkennung. Radarbasiertes Atemfrequenzmonitoring unter klinischen Bedingungen ist zuverlässig und hat das Potenzial, Symptome zu erkennen.Publicationtype: Journal Article34 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Systematic investigation of heart sound propagation using continuous wave radar(IEEE, 2025-01-16); ; ; ; Monitoring the propagation of mechanical cardiac signals throughout the body is crucial for assessing cardiovascular health. A common drawback of current gold standard methods for vital sign monitoring is the necessity for continuous skin contact. Radar-based sensing offers a promising alternative by enabling contactless measurement of cardiac activity, including heart sound signals. As previous research has primarily focused on deriving signals from proximal body regions, insights into heart sound propagation to peripheral areas are lacking. To address this, we systematically investigated whether radar-based heart sound detection and propagation measurement is feasible across the whole body. We recorded heart sounds in N=22 participants sequentially at eleven locations using a custom-built continuous-wave radar system and phonocardiogram as heart sound gold standard. Additionally, an electrocardiogram was acquired as reference for overall heart activity. After synchronization and preprocessing, we manually segmented the heart sounds and extracted temporal characteristics from ensemble-averaged signals. Our findings show that heart sounds can be detected across the entire body with the radar-based as well as the gold standard system. Furthermore, the heart sounds’ temporal characteristics vary between measurement locations. As the distance to the heart increases, we observed significantly increased propagation time intervals. This finding is consistent across both systems, exhibiting a strong agreement for the first heart sound (r = 0.73, p < 0.001) and a moderate agreement for the second heart sound (r = 0.56, p < 0.001). In conclusion, our work is the first to demonstrate that radar-based systems are feasible for contactless evaluation of heart sound propagation, offering new possibilities for research and health monitoring.Publicationtype: Journal Article60 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Contactless radar-based heart rate estimation in palliative care – a feasibility study and possible use in symptom management(BioMed Central, 2024-11-30); ; ; ; Leuschner, JulieBackground: Heart rate (HR) monitoring is a medical standard to provide information about a patient’s health status. In palliative care, relationship and social engagement are crucial therapeutic concepts. For fear of disrupting communication, social contact, and care, continuous HR monitoring is underutilised despite its potential to inform on symptom burden and therapeutic effects. This study investigates radar-based HR monitoring as an innovative and burden-free approach for palliative care patients, compares its accuracy with conventional ECG methods, and shows potential for therapeutic guidance. Methods: A single-centre, comparative clinical trial was conducted with palliative care patients at the ward of the Department of Palliative Medicine of the University Hospital of Erlangen. The HR measurements obtained with radar were compared with Holter ECG (study arm I, overnight) and Task Force® Monitor (TFM)-based ECG validation recordings (study arm II, one hour). In addition, long-term radar measurements without validation were analysed in comparison with clinical health records (study arm III). Results: Both validation methods showed correlation by scatter plot, modified Bland-Altman plot, and equivalence testing. N = 34 patients participated in study arm I. HR of 4,079 five-minute intervals was analysed. Radar measurements and ECG showed high agreement: difference of HRs was within 5 bpm in 3780 of 4079 (92.67%) and within ±13.4 bpm (1.96 times the SD of the mean) in 3979 (97.55%) intervals, respectively. In study arm II, n = 19 patients participated. 57,048 heart beats were analysed. The HR difference was within 5 bpm for 53,583 out of 57,048 beats (93.93%) and within 8.2 bpm (± 1.96 times the SD of the mean) in 55,439 beats (97.25%), respectively. Arm III showed HR changes extracted from radar data in correlation with symptoms and treatment. Conclusion: Radar-based HR monitoring shows a high agreement in comparison with ECG-based HR monitoring and thus offers an option for continuous and above all burden-free HR assessment, with the potential for use in symptom management in palliative care, among others. Further research and technological advancements are still necessary to fully realize this innovative approach in enhancing palliative care practices.Publicationtype: Journal Article30 16 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , CW Radar-based Non-Contact Respiration Monitoring using Complex-Valued Neural Networks(2024-09); ; ; Machine learning-based respiration monitoring using continuous wave (CW) radar has been studied. However, most of the models are real-valued (RV), which only takes the magnitude of signals as input. Complex-valued (CV) signals can be formulated from radar baseband signals. In this paper, we compare CV-based and equivalent RV-based neural networks (NN) models to reconstruct the respiration signal with the 61 GHz CW radar. We demonstrate two model structures, a fully connected NN (FCNN) and a combination of the convolutional network and bidirectional gated recurrent units (ConVGRU), in CV-based and equivalent RV-based models, respectively. Ten hours of measurements from a clinical study are used to validate the performance. ConVGRU surpasses FCNN in both RV and CV. CV-based ConVGRU outperforms the RV-based ConVGRU with a smaller average breathing rate error of 0.55 breaths per minute (bpm), with more than 93% of the measurements achieving an error of less than 2 bpm.Publicationtype: Conference Paper20