4 research outputs found
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
Untersuchung von Verarbeitungsalgorithmen zur automatischen Auswertung neuronaler Signale aus Multielektroden-Arrays
Mit Hilfe von Multielektroden-Arrays (MEAs) können viele Zellen gleichzeitig kontaktiert und deren elektrische Aktivität abgeleitet werden. Für die weitere Analyse müssen die abgeleiteten Signale in ihre Einzelbestandteile zerlegt werden. Dieser Vorgang wird als Spike Sorting bezeichnet. In der vorliegenden Arbeit werden Ansätze für ein vollständig automatisiertes Spike Sorting vorgestellt und untersucht. Dabei werden Verfahren aufgezeigt, die mit Hilfe von adaptiven Verfahren die abgeleiteten Zellsignale optimal filtern und automatisch in deren Einzelkomponenten zerlegen
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