Predicting 6-minute walking test outcomes in patients with chronic obstructive pulmonary disease without physical performance measures

Background and Objective: 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. Methods: 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. Results: 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. Conclusions: 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

Object detection and image annotation on the iPhone Detección de objetos y anotación de imágenes en el iPhone Detecció d'objectes i anotació d'imatges a l'iPhone

[ANGLÈS] This project is about object detection on a mobile device like iPhone. The iPhone tool allows users to take pictures, annotate them with bounding boxes and object tags and uploading the data to the server. With this annotated images the users can train object detectors and use them on the iPhone.[CASTELLÀ] El proyecto trata sobre como llevar la detección de objetos a un dispositivo móvil como iPhone. Empezando con la recolección y anotación de imágenes para generar una base de datos con el fin de poder entrenar detectores de objetos y poderlos utilizar en el mismo dispositivo.[CATALÀ] El projecte tracta com portar la detecció d'objectes a un dispositiu mòbil com iPhone. Comença amb la recol·lecció i anotació d'imatges per generar una base de dades per així a partir d'aquesta base de dades poder entrenar detectors d'objectes i poder-los utilitzar al propi dispositiu

Object detection and image annotation on the iPhone Detección de objetos y anotación de imágenes en el iPhone Detecció d'objectes i anotació d'imatges a l'iPhone

[ANGLÈS] This project is about object detection on a mobile device like iPhone. The iPhone tool allows users to take pictures, annotate them with bounding boxes and object tags and uploading the data to the server. With this annotated images the users can train object detectors and use them on the iPhone.[CASTELLÀ] El proyecto trata sobre como llevar la detección de objetos a un dispositivo móvil como iPhone. Empezando con la recolección y anotación de imágenes para generar una base de datos con el fin de poder entrenar detectores de objetos y poderlos utilizar en el mismo dispositivo.[CATALÀ] El projecte tracta com portar la detecció d'objectes a un dispositiu mòbil com iPhone. Comença amb la recol·lecció i anotació d'imatges per generar una base de dades per així a partir d'aquesta base de dades poder entrenar detectors d'objectes i poder-los utilitzar al propi dispositiu

Object detection and image annotation on the iPhone Detección de objetos y anotación de imágenes en el iPhone Detecció d'objectes i anotació d'imatges a l'iPhone

[ANGLÈS] This project is about object detection on a mobile device like iPhone. The iPhone tool allows users to take pictures, annotate them with bounding boxes and object tags and uploading the data to the server. With this annotated images the users can train object detectors and use them on the iPhone.[CASTELLÀ] El proyecto trata sobre como llevar la detección de objetos a un dispositivo móvil como iPhone. Empezando con la recolección y anotación de imágenes para generar una base de datos con el fin de poder entrenar detectores de objetos y poderlos utilizar en el mismo dispositivo.[CATALÀ] El projecte tracta com portar la detecció d'objectes a un dispositiu mòbil com iPhone. Comença amb la recol·lecció i anotació d'imatges per generar una base de dades per així a partir d'aquesta base de dades poder entrenar detectors d'objectes i poder-los utilitzar al propi dispositiu

Wearable Bioimpedance Measurement for Respiratory Monitoring During Inspiratory Loading

status: publishe

Chest Movement and Respiratory Volume both Contribute to Thoracic Bioimpedance during Loaded Breathing

Bioimpedance has been widely studied as alternative to respiratory monitoring methods because of its linear relationship with respiratory volume during normal breathing. However, other body tissues and fluids contribute to the bioimpedance measurement. The objective of this study is to investigate the relevance of chest movement in thoracic bioimpedance contributions to evaluate the applicability of bioimpedance for respiratory monitoring. We measured airflow, bioimpedance at four electrode configurations and thoracic accelerometer data in 10 healthy subjects during inspiratory loading. This protocol permitted us to study the contributions during different levels of inspiratory muscle activity. We used chest movement and volume signals to characterize the bioimpedance signal using linear mixed-effect models and neural networks for each subject and level of muscle activity. The performance was evaluated using the Mean Average Percentage Errors for each respiratory cycle. The lowest errors corresponded to the combination of chest movement and volume for both linear models and neural networks. Particularly, neural networks presented lower errors (median below 4.29%). At high levels of muscle activity, the differences in model performance indicated an increased contribution of chest movement to the bioimpedance signal. Accordingly, chest movement contributed substantially to bioimpedance measurement and more notably at high muscle activity levels.status: publishe

A 36 mu W 1.1 mm(2) Reconfigurable Analog Front-End for Cardiovascular and Respiratory Signals Recording

This paper presents a 1.2 V 36 μW reconfigurable analog front-end (R-AFE) as a general-purpose low-cost IC for multiple-mode biomedical signals acquisition. The R-AFE efficiently reuses a reconfigurable preamplifier, a current generator (CG), and a mixed signal processing unit, having an area of 1.1 mm2 per R-AFE while supporting five acquisition modes to record different forms of cardiovascular and respiratory signals. The R-AFE can interface with voltage-, current-, impedance-, and light-sensors and hence can measure electrocardiography (ECG), bio-impedance (BioZ), photoplethysmogram (PPG), galvanic skin response (GSR), and general-purpose analog signals. Thanks to the chopper preamplifier and the low-noise CG utilizing dynamic element matching, the R-AFE mitigates ${\text{1}}\text{/}f$ noise from both the preamplifier and the CG for improved measurement sensitivity. The IC achieves competitive performance compared to the state-of-the-art dedicated readout ICs of ECG, BioZ, GSR, and PPG, but with approximately 1.4×-5.3× smaller chip area per channel.status: publishe

Noninvasive Assessment of Neuromechanical and Neuroventilatory Coupling in COPD

This study explored the use of parasternal second intercostal space and lower intercostal space surface electromyogram (sEMG) and surface mechanomyogram (sMMG) recordings (sEMGpara and sMMGpara, and sEMGlic and sMMGlic, respectively) to assess neural respiratory drive (NRD), neuromechanical (NMC) and neuroventilatory (NVC) coupling, and mechanical efficiency (MEff) noninvasively in healthy subjects and chronic obstructive pulmonary disease (COPD) patients. sEMGpara, sMMGpara, sEMGlic, sMMGlic, mouth pressure (Pmo), and volume (Vi) were measured at rest, and during an inspiratory loading protocol, in 16 COPD patients (8 moderate and 8 severe) and 9 healthy subjects. Myographic signals were analyzed using fixed sample entropy and normalized to their largest values (fSEsEMGpara%max, fSEsMMGpara%max, fSEsEMGlic%max, and fSEsMMGlic%max). fSEsMMGpara%max, fSEsEMGpara%max, and fSEsEMGlic%max were significantly higher in COPD than in healthy participants at rest. Parasternal intercostal muscle NMC was significantly higher in healthy than in COPD participants at rest, but not during threshold loading. Pmo-derived NMC and MEff ratios were lower in severe patients than in mild patients or healthy subjects during threshold loading, but differences were not consistently significant. During resting breathing and threshold loading, Vi-derived NVC and MEff ratios were significantly lower in severe patients than in mild patients or healthy subjects. sMMG is a potential noninvasive alternative to sEMG for assessing NRD in COPD. The ratios of Pmo and Vi to sMMG and sEMG measurements provide wholly noninvasive NMC, NVC, and MEff indices that are sensitive to impaired respiratory mechanics in COPD and are therefore of potential value to assess disease severity in clinical practice. Autho