Clustering Algorithms As Classifiers Of Blood Pressure Recordings

Abstract

Pattern recognition techniques, such as clustering algorithms, are applied to recordings of arterial distension waveforms to detect emergent properties of data. The feature extraction stage is based on the Fast Fourier Transform components analysis. Statistical K-means clustering helps in the feature selection step.To generalize the method uses both neural network self-organizing feature mapping and neural network supervised learning to classify waves according to patient age. This process shows encouraging results for a set of blood pressure recordings belonging to three differents decades.