Trajectory-based and sound-based medical data clustering

Challenges in medicine are often faced as interdisciplinary endeavors. In such an interdisciplinary view, sonification of medical data provides an additional sensory dimension to highlight often hard-to-find information and details. Some examples of sonification of medical data include Covid genome mapping [5], auditory representations of tridimensional objects as the brain [4], enhancement of medical imagery through the use of sound [1]. Here, we focus on kidney filtering-efficiency time-evolution data. We consider the estimated glomerular filtration rate (eGFR), the main indicator of kidney efficiency in diabetic kidney disease patients.1 We propose a technique to sonify the eGFR trajectories with time, frequency, and timbre to distinguish amongst patients (Figure 1). Multiple pitch trajectories can be formally investigated with the tools of counterpoint (Figure 2), and computationally analyzed with sound-processing techniques. Patients who present similar patterns of eGFR behavior can be more easily spotted through musical similarities. We use the Fréchet distance, which evaluates the shape similarity between curves [2], to cluster patients with similar eGFR behavior. We thus compare the information gathered through sonification and shape-based analysis. We find the mean curves in each trajectory cluster and we compare them with the characteristics of sonified curves. Clustering methods have also been applied to sound analysis: it is the case of k-means to cluster sound data [3]. The Fréchet-based clustering technique is a development of k-means taking shape into account. Thus, we sketch a sound-based clustering approach for medical data, as an additional tool to find patterns of behavior. This study can foster new research between computer science, medicine, and sound processing.