Breathing patterns recognition: A functional data analysis approach

•Assessing subject's respiratory function and his/her breathing pattern is fundamental.•State-of-art analysis of respiratory data requires operator-dependent choices.•Semi-automatic procedure proposed to pre-process and analyse respiratory tracks.•Functional Data Analysis techniques used to identify representative breath curve.•The identified breathing patterns proved to be coherent with the physiopathology. The ongoing pandemic proved fundamental is to assess a subject's respiratory functionality and breathing pattern measurement during quiet breathing is feasible in almost all patients, even those uncooperative. Breathing pattern consists of tidal volume and respiratory rate in an individual assessed by data tracks of lung or chest wall volume over time. State-of-art analysis of these data requires operator-dependent choices such as individuation of local minima in the track, elimination of anomalous breaths and individuation of breath clusters corresponding to different breathing patterns. A semi-automatic, robust and reproducible procedure was proposed to pre-process and analyse respiratory tracks, based on Functional Data Analysis (FDA) techniques, to identify representative breath curve and the corresponding breathing patterns. This was achieved through three steps: 1) breath separation through precise localization of the minima of the volume trace; 2) functional outlier breaths detection according to time-duration, magnitude and shape; 3) breath clustering to identify different pattern of interest, through K-medoids with Alignment. The method was firstly validated on simulated tracks and then applied to real data in conditions of clinical interest: operational volume change, exercise, mechanical ventilation, paradoxical breathing and age. The total error in the accuracy of minima detection and in was less than 5%; with the artificial outliers being almost completely removed with an accuracy of 99%. During incremental exercise and independently on the bike resistance level, five clusters were identified (quiet breathing; recovery phase; onset of exercise; maximal and intermediate levels of exercise). During mechanical ventilation, the procedure was able to separate the non-ventilated from the ventilatory-supported breathing and to identify the worsening of paradoxical breathing due to the disease progression and the breathing pattern changes in healthy subjects due to age. We proposed a robust validated automatic breathing patterns identification alg