Similarity based hierarchical clustering of physiological parameters for the identification of health states - a feasibility study
This work addresses the need for better health state identification from physiological data, but it is incremental as it builds on existing clustering techniques.
The paper tackled the problem of clustering physiological data into health states using a new unsupervised hierarchical method, achieving significantly higher accuracy compared to other clustering algorithms in identifying health states from ECG data during physical exercise.
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.