Unsupervised study of plethysmography signals through DTW clustering

The study of plethysmography time series is crucial to better understand the breathing behavior of mice, in particular the influence of neurotoxins on the respiratory system. Current approaches rely on a few respiratory descriptors computed on individual breathing cycles that fail to account for the variety of breathing habits and their evolution with time. In this paper we introduce a new procedure for the automatic analysis of plethysmography signals. Our method relies on a new and robust segmentation of respiratory cycles and a DTW-based clustering algorithm to extract the most typical respiratory cycles (called reference sequences). We can then create a symbolic representation of any new recording by matching respiratory cycles to their closest reference sequence. This new representation is a visual and quantitative tool to assess the breathing behavior of mice and its evolution with time. Our method is applied to plethysmography signals collected on mice with two different genotypes and exposed to a neurotoxin. Clinical relevance This article proposes a novel approach to study plethysmography data. Our algorithm is able to accurately extract clinically meaningful respiratory cycles and the associated ventilation patterns descriptors such as tidal volume and inhalation/exhalation duration. In addition, thanks to the associated symbolic representation of signals, the temporal evolution of respiration is easily quantified. This opens a new research path to study the often slowly evolving and subtle influence of neurotoxins on the respiratory system.