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

TL;DR

This study presents a novel unsupervised hierarchical clustering method for physiological data that accurately identifies health states and correlates well with experimental phases, outperforming existing algorithms.

Contribution

Introduces a new similarity-based hierarchical clustering approach for physiological data, improving health state identification accuracy over existing methods.

Findings

High temporal correlation between health states and exercise phases

Significantly higher accuracy than other clustering algorithms

Effective clustering based on ECG-derived parameters

Abstract

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.