Similarity based hierarchical clustering of physiological parameters for the identification of health states - a feasibility study

This paper introduces a new unsupervised method for the clustering of physiological data into health states based on their similarity. We propose an iterative hierarchical clustering approach that combines health states according to a similarity constraint to new arbitrary health states. We applied method to experimental data in which the physical strain of subjects was systematically varied. We derived health states based on parameters extracted from ECG data. The occurrence of health states shows a high temporal correlation to the experimental phases of the physical exercise. We compared our method to other clustering algorithms and found a significantly higher accuracy with respect to the identification of health states.Comment: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC

Konzeption eines Verfahrens zur ähnlichkeitsbasierten Cluster-Analyse von Vitaldaten und dessen Validierung am Beispiel von Schmerzmittelgaben nach herzchirurgischen Eingriffen

In der Arbeit wird ein Verfahren zur Identifikation von Gesundheitszuständen des Menschen entwickelt. Mit Hilfe einer hierarchischen Cluster-Analyse werden anhand von Biosignalen Zustände mit charakteristischen Vitalparametersätzen identifiziert. Das Verfahren wird mit Hilfe realer und synthetischer Daten validiert. Mit der Detektion von Schmerzmittelgaben nach herzchirurgischen Eingriffen im Rahmen einer klinischen Messstudie wird eine Evaluierung vorgenommen. Die Ergebnisse der Methode werden mit Referenzverfahren verglichen. Es zeigen sich vergleichbare oder bessere Ergebnisse

Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning

Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera based approaches allow to derive remote PPG (rPPG) signals similar to PPG and therefore provide the opportunity for non-invasive measurements of BP. Various methods relying on machine learning techniques have recently been published. Performances are often reported as the mean average error (MAE) on the data which is problematic. This work aims to analyze the PPG- and rPPG based BP prediction error with respect to the underlying data distribution. First, we train established neural network (NN) architectures and derive an appropriate parameterization of input segments drawn from continuous PPG signals. Second, we use this parameterization to train NNs with a larger PPG dataset and carry out a systematic evaluation of the predicted blood pressure. The analysis revealed a strong systematic increase of the prediction error towards less frequent BP values across NN architectures. Moreover, we tested different train/test set split configurations which underpin the importance of a careful subject-aware dataset assignment to prevent overly optimistic results. Third, we use transfer learning to train the NNs for rPPG based BP prediction. The resulting performances are similar to the PPG-only case. Finally, we apply different personalization techniques and retrain our NNs with subject-specific data for both the PPG-only and rPPG case. Whilst the particular technique is less important, personalization reduces the prediction errors significantly

Above- and belowground biodiversity jointly tighten the P cycle in agricultural grasslands

Experiments showed that biodiversity increases grassland productivity and nutrient exploitation, potentially reducing fertiliser needs. Enhancing biodiversity could improve P-use efficiency of grasslands, which is beneficial given that rock-derived P fertilisers are expected to become scarce in the future. Here, we show in a biodiversity experiment that more diverse plant communities were able to exploit P resources more completely than less diverse ones. In the agricultural grasslands that we studied, management effects either overruled or modified the driving role of plant diversity observed in the biodiversity experiment. Nevertheless, we show that greater above- (plants) and belowground (mycorrhizal fungi) biodiversity contributed to tightening the P cycle in agricultural grasslands, as reduced management intensity and the associated increased biodiversity fostered the exploitation of P resources. Our results demonstrate that promoting a high above- and belowground biodiversity has ecological (biodiversity protection) and economical (fertiliser savings) benefits. Such win-win situations for farmers and biodiversity are crucial to convince farmers of the benefits of biodiversity and thus counteract global biodiversity loss

Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning

Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera based approaches allow to derive remote PPG (rPPG) signals similar to PPG and therefore provide the opportunity for non-invasive measurements of BP. Various methods relying on machine learning techniques have recently been published. Performances are often reported as the mean average error (MAE) on the data which is problematic. This work aims to analyze the PPG- and rPPG based BP prediction error with respect to the underlying data distribution. First, we train established neural network (NN) architectures and derive an appropriate parameterization of input segments drawn from continuous PPG signals. Second, we use this parameterization to train NNs with a larger PPG dataset and carry out a systematic evaluation of the predicted blood pressure. The analysis revealed a strong systematic increase of the prediction error towards less frequent BP values across NN architectures. Moreover, we tested different train/test set split configurations which underpin the importance of a careful subject-aware dataset assignment to prevent overly optimistic results. Third, we use transfer learning to train the NNs for rPPG based BP prediction. The resulting performances are similar to the PPG-only case. Finally, we apply different personalization techniques and retrain our NNs with subject-specific data for both the PPG-only and rPPG case. Whilst the particular technique is less important, personalization reduces the prediction errors significantly

Derivation of the respiratory rate from directly and indirectly measured respiratory signals using autocorrelation

The estimation of respiratory rates from contineous respiratory signals is commonly done using either fourier transformation or the zero-crossing method. This paper introduces another method which is based on the autocorrelation function of the respiratory signal. The respiratory signals can be measured either directly using a flow sensor or chest strap or indirectly on the basis of the electrocardiogram (ECG). We compare our method against other established methods on the basis of real-world ECG signals and use a respiration-based breathing frequency as a reference. Our method achieved the best agreement between respiration rates derived from directly and indirectly measured respiratory signals

Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning

Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera based approaches allow to derive remote PPG (rPPG) signals similar to PPG and therefore provide the opportunity for non-invasive measurements of BP. Various methods relying on machine learning techniques have recently been published. Performances are often reported as the mean average error (MAE) on the data which is problematic. This work aims to analyze the PPG- and rPPG based BP prediction error with respect to the underlying data distribution. First, we train established neural network (NN) architectures and derive an appropriate parameterization of input segments drawn from continuous PPG signals. Second, we use this parameterization to train NNs with a larger PPG dataset and carry out a systematic evaluation of the predicted blood pressure. The analysis revealed a strong systematic increase of the prediction error towards less frequent BP values across NN architectures. Moreover, we tested different train/test set split configurations which underpin the importance of a careful subject-aware dataset assignment to prevent overly optimistic results. Third, we use transfer learning to train the NNs for rPPG based BP prediction. The resulting performances are similar to the PPG-only case. Finally, we apply different personalization techniques and retrain our NNs with subject-specific data for both the PPG-only and rPPG case. Whilst the particular technique is less important, personalization reduces the prediction errors significantly

Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning

Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera based approaches allow to derive remote PPG (rPPG) signals similar to PPG and therefore provide the opportunity for non-invasive measurements of BP. Various methods relying on machine learning techniques have recently been published. Performances are often reported as the mean average error (MAE) on the data which is problematic. This work aims to analyze the PPG- and rPPG based BP prediction error with respect to the underlying data distribution. First, we train established neural network (NN) architectures and derive an appropriate parameterization of input segments drawn from continuous PPG signals. Second, we use this parameterization to train NNs with a larger PPG dataset and carry out a systematic evaluation of the predicted blood pressure. The analysis revealed a strong systematic increase of the prediction error towards less frequent BP values across NN architectures. Moreover, we tested different train/test set split configurations which underpin the importance of a careful subject-aware dataset assignment to prevent overly optimistic results. Third, we use transfer learning to train the NNs for rPPG based BP prediction. The resulting performances are similar to the PPG-only case. Finally, we apply different personalization techniques and retrain our NNs with subject-specific data for both the PPG-only and rPPG case. Whilst the particular technique is less important, personalization reduces the prediction errors significantly

Soil property and management effects on grassland microbial communities across a latitudinal gradient in Germany

There is much interest in the identification of the main drivers controlling changes in the microbial community that may be related to sustainable land use. We examined the influence of soil properties and land-use intensity (N fertilization, mowing, grazing) on total phospholipid fatty acid (PLFA) biomass, microbial community composition (PLFA profiles) and activities of enzymes involved in the C, N, and P cycle. These relationships were examined in the topsoil of grasslands from three German regions (Schorfheide-Chorin (SCH), Hainich-Dun (HAI), Schwabische Alb (ALB)) with different parent material. Differences in soil properties explained 60% of variation in PLFA data and 81% of variation in enzyme activities across regions and land-use intensities. Degraded peat soils in the lowland areas of the SCH with high organic carbon (OC) concentrations and sand content contained lower PLFA biomass, lower concentrations of bacterial, fungal, and arbuscular mycorrhizal PLFAs, but greater enzyme activities, and specific enzyme activities (per unit microbial biomass) than mineral soils in the upland areas of the HAI and ALB, which are finer textured, drier, and have smaller OC concentrations. After extraction of variation that originated from large-scale differences among regions and differences in land-use intensities between plots, soil properties still explained a significant amount of variation in PLFA data (34%) and enzyme activities (60%). Total PLFA biomass and all enzyme activities were mainly related to OC concentration, while relative abundance of fungi and fungal to bacterial ratio were mainly related to soil moisture. Land-use intensity (LUI) significantly decreased the soil C:N ratio. There was no direct effect of LUI on total PLFA biomass, microbial community composition, N and P cycling enzyme activities independent of study region and soil properties. In contrast, the activities and specific activities of enzymes involved in the C cycle increased significantly with LUI independent of study region and soil properties, which can have impact on soil organic matter decomposition and nutrient cycling. Our findings demonstrate that microbial biomass and community composition as well as enzyme activities are more controlled by soil properties than by grassland management at the regional scale. (C) 2013 Elsevier B.V: All rights reserved