Publications

Chronic obstructive pulmonary disease (COPD) requires a multifactorial assessment, evaluating the airflow limitation and symptoms of the patients. The 6-min walk test (6MWT) is commonly used to evaluate the functional exercise capacity in these patients. This study aims to propose a novel predictive model of the major 6MWT outcomes for COPD assessment, without physical performance measurements.Cardiopulmonary and clinical parameters were obtained from fifty COPD patients. These parameters were used as inputs of a Bayesian network (BN), which integrated three multivariate models including the 6-min walking distance (6MWD), the maximum HR (HRmax) after the walking, and the HR decay 3 min after (HRR3). The use of BN allows the assessment of the patients' status by predicting the 6MWT outcomes, but also inferring disease severity parameters based on actual patient's 6MWT outcomes.Firstly, the correlation obtained between the estimated and actual 6MWT measures was strong (R = 0.84, MAPE = 8.10% for HRmax) and moderate (R = 0.58, MAPE = 15.43% for 6MWD and R = 0.58, MAPE = 32.49% for HRR3), improving the classical methods to estimate 6MWD. Secondly, the classification of disease severity showed an accuracy of 78.3% using three severity groups, which increased up to 84.4% for two defined severity groups.We propose a powerful two-way assessment tool for COPD patients, capable of predicting 6MWT outcomes without the need for an actual walking exercise. This model-based tool opens the way to implement a continuous monitoring system for COPD patients at home and to provide more personalized care.Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.

JTD Keywords: 6mwt, bayesian networks, copd, distance, exercise capacity, physical capacity, reference equations, severity, survival, wearables, 6mwt, Heart-rate recovery, Wearables

JTD Keywords: Biomarkers, Copd

Many studies have focused on novel noninvasive techniques to monitor respiratory rate such as bioimpedance. We propose an algorithm to detect respiratory phases using wearable bioimpedance to compute time parameters like respiratory rate, inspiratory and expiratory times, and duty cycle. The proposed algorithm was compared with two other algorithms from literature designed to estimate the respiratory rate using physiological signals like bioimpedance. We acquired bioimpedance and airflow from 50 chronic obstructive pulmonary disease (COPD) patients during an inspiratory loading protocol. We compared performance of the algorithms by computing accuracy and mean average percentage error (MAPE) between the bioimpedance parameters and the reference parameters from airflow. We found similar performance for the three algorithms in terms of accuracy (>0.96) and respiratory time and rate errors (<3.42 %). However, the proposed algorithm showed lower MAPE in duty cycle (10.18 %), inspiratory time (10.65 %) and expiratory time (8.61 %). Furthermore, only the proposed algorithm kept the statistical differences in duty cycle between COPD severity levels that were observed using airflow. Accordingly, we suggest bioimpedance to monitor breathing pattern parameters in home situations.Clinical relevance - This study exhibits the suitability of wearable thoracic bioimpedance to detect respiratory phases and to compute accurate breathing pattern parameters. © 2021 IEEE.

JTD Keywords: algorithms, copd, signals, Algorithm, Algorithms, Bioimpedance, Breathing rate, Chronic obstructive lung disease, Electronic device, Human, Humans, Lung, Pulmonary disease, chronic obstructive, Respiratory rate, Wearable electronic devices

In this work, the flow signal Expiratory Pause (EP) temporal analysis is used in 18 patients with obstructive lung diseases going through spontaneous breathing trial at weaning process. The main objective was to identify the patients who were successfully disconnected (success group: 9 patients), and those who were not (failure and reintubated group: 9 patients). A variable selection stage was done by mean group comparison and step wise variable inclusion, leading to a 3 parameters set: EP time median; cycle time mean; and median absolute deviation of the EP maxima local number. Next, this set was used in a classifier based on linear discriminant analysis, which results in 17 patients (94.4%) correctly classified, with 88.9% of specificity (Sp) and 100% of sensitivity (Se). Finally, applying the leave-one-out cross validation method, results were 88.9% of correctly classified patients (Sp=77.8% and Se=100%). In conclusion, the proposed parameters showed a good performance and could be used to help therapists to wean patients with obstructive diseases.

JTD Keywords: Chronic Obstructive Pulmonary Disease (COPD), Weaning, Mechanical ventilation, Expiratory pause