Miguel Angel Fernandez-Granero

Computerised Analysis of Telemonitored Respiratory Sounds for Predicting Acute Exacerbations of COPD

Chronic obstructive pulmonary disease (COPD) is one of the commonest causes of death in the world and poses a substantial burden on healthcare systems and patients’ quality of life. The largest component of the related healthcare costs is attributable to admissions due to acute exacerbation (AECOPD). The evidence that might support the effectiveness of the telemonitoring interventions in COPD is limited partially due to the lack of useful predictors for the early detection of AECOPD. Electronic stethoscopes and computerised analyses of respiratory sounds (CARS) techniques provide an opportunity for substantial improvement in the management of respiratory diseases. This exploratory study aimed to evaluate the feasibility of using: (a) a respiratory sensor embedded in a self-tailored housing for ageing users; (b) a telehealth framework; (c) CARS and (d) machine learning techniques for the remote early detection of the AECOPD. In a 6-month pilot study, 16 patients with COPD were equipped with a home base-station and a sensor to daily record their respiratory sounds. Principal component analysis (PCA) and a support vector machine (SVM) classifier was designed to predict AECOPD. 75.8% exacerbations were early detected with an average of 5 ± 1.9 days in advance at medical attention. The proposed method could provide support to patients, physicians and healthcare systems.

Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: A systematic review

Major reported factors associated with the limited effectiveness of home telemonitoring interventions in chronic respiratory conditions include the lack of useful early predictors, poor patient compliance and the poor performance of conventional algorithms for detecting deteriorations. This article provides a systematic review of existing algorithms and the factors associated with their performance in detecting exacerbations and supporting clinical decisions in patients with chronic obstructive pulmonary disease (COPD) or asthma. An electronic literature search in Medline, Scopus, Web of Science and Cochrane library was conducted to identify relevant articles published between 2005 and July 2015. A total of 20 studies (16 COPD, 4 asthma) that included research about the use of algorithms in telemonitoring interventions in asthma and COPD were selected. Differences on the applied definition of exacerbation, telemonitoring duration, acquired physiological signals and symptoms, type of technology deployed and algorithms used were found. Predictive models with good clinically reliability have yet to be defined, and are an important goal for the future development of telehealth in chronic respiratory conditions. New predictive models incorporating both symptoms and physiological signals are being tested in telemonitoring interventions with positive outcomes. However, the underpinning algorithms behind these models need be validated in larger samples of patients, for longer periods of time and with well-established protocols. In addition, further research is needed to identify novel predictors that enable the early detection of deteriorations, especially in COPD. Only then will telemonitoring achieve the aim of preventing hospital admissions, contributing to the reduction of health resource utilization and improving the quality of life of patients.

An artificial intelligence approach to early predict symptom-based exacerbations of COPD

ABSTRACT Acute exacerbations are one of the main causes that reduce health-related quality of life and lead to hospitalisations of patients of chronic obstructive pulmonary disease (COPD). Prediction of exacerbations could diminish those negative effects and reduce the high costs associated with COPD patients. In this study, 16 patients were telemonitored at home during six months. Respiratory sounds were recorded daily with an electronic sensor ad-hoc designed. In order to enable an automatic prediction of symptom-based exacerbations, recorded data were used to train and validate a decision tree forest classifier. The developed model was capable of predicting early acute exacerbations of COPD, as average, with a 4.4 days margin prior to onset. Thirty-two out of 41 exacerbations were detected early. A percentage of 75.8% (25 out of 33) of detected episodes were reported exacerbation and 87.5% (7 out of 8) were unreported events. The achieved results demonstrated that machine-learning techniques have significant potential to support the early detection of COPD exacerbations.

Automatic Tool for Optic Disc and Cup Detection on Retinal Fundus Images

The aging of the population is a matter of concern due to its association with various diseases in humans that limit their quality of life. Among them, glaucoma is one of the leading causes of blindness in the world. To its early diagnose, retinal fundus images are visually inspected by experts. In recent years, image-based computer aided diagnosis systems have been proposed. Automatic segmentation of Optic Disc (OD) and cup areas are their first and most difficult tasks. In this paper, a computerized technique aimed to their extraction from the original images is presented. The tool is related to human perception due to the use of an advanced color metric, CIE94 within a uniform color space, CIE L*a*b* to compute pixels’ color gradients [1]. Based on this information, a classifier assigns a probability value to each of the pixels, meaning its suitability for being part of the Optic Disc and Cup border. The tool has been tested on 200 images from different public databases achieving an accuracy value of 96.63%. This quality level makes the proposed color-based image processing system capable to assist the physicians in glaucoma screening programs.

Automatic Recognition of Daily Physical Activities for an Intelligent-Portable Oxygen Concentrator (iPOC).

In recent years, new autonomous physiological close-loop controlled (PCLC) medical devices for oxygen delivery are being researched. Most of this PCLC devices are based on the feedback of arterial oxygen saturation, measured using a pulse oximeter. However, pulse oximeters may provide spuriously low or high SpO2 values. In this work, a different approach to adjust automatically oxygen dosing in portable oxygen concentrators (POC) according to the physical activity performed by patients with COPD is presented. To that purpose, the ability of various machine-learning algorithms to recognize four human daily activities from sensor signals collected from a single waist-worn tri-axial accelerometer is evaluated. A set of 56 features was considered and recognition accuracy of up to 91.15% on the four activities of daily living was obtained using a SVM classifier. The associated activity recognition error rate was lower than 5%, ensuring a low percentage of time wrongly assigned to a certain activity. The underlying idea is the hardware implementation of the SVM classifier to control the oxygen flow in intelligent portable oxygen concentrators.

Neuronal Texture Analysis in Murine Model of Down’s Syndrome

An alteration of neuronal morphology is present in cognitive neurological diseases where learning or memory abilities are affected. The quantification of this alteration and its evolution by the study of microscopic images is essential. However, the use of advanced and automatic image processing techniques is currently very limited, focusing on the analysis of the morphology of isolated neurons. On this article we present a new methodology, based on texture analysis, to characterize the global distribution of different neural patterns in immunofluorescence images of brain tissue sections, where the neurons can be visualized as they are really distributed. We apply the technique to mice brain tissue section dividing them into two classes: Ts1Cje Down’s syndrome model and wild type, free of this neurodegenerative disease. Taking into account CA1 region of the hippocampus, we calculate and compare several state of the art texture descriptors that are subsequently classified using machine learning techniques. Achieving a 95% of accuracy, the assumption that texture characterization is relevant to quantify globally morphological alterations in the neurons, seems to be demonstrated.

Automatic CDR Estimation for Early Glaucoma Diagnosis

Glaucoma is a degenerative disease that constitutes the second cause of blindness in developed countries. Although it cannot be cured, its progression can be prevented through early diagnosis. In this paper, we propose a new algorithm for automatic glaucoma diagnosis based on retinal colour images. We focus on capturing the inherent colour changes of optic disc (OD) and cup borders by computing several colour derivatives in CIE L∗a∗b∗ colour space with CIE94 colour distance. In addition, we consider spatial information retaining these colour derivatives and the original CIE L∗a∗b∗ values of the pixel and adding other characteristics such as its distance to the OD centre. The proposed strategy is robust due to a simple structure that does not need neither initial segmentation nor removal of the vascular tree or detection of vessel bends. The method has been extensively validated with two datasets (one public and one private), each one comprising 60 images of high variability of appearances. Achieved class-wise-averaged accuracy of 95.02% and 81.19% demonstrates that this automated approach could support physicians in the diagnosis of glaucoma in its early stage, and therefore, it could be seen as an opportunity for developing low-cost solutions for mass screening programs.

Physiological closed-loop control in intelligent oxygen therapy: A review

BACKGROUND AND OBJECTIVE Oxygen therapy has become a standard care for the treatment of patients with chronic obstructive pulmonary disease and other hypoxemic chronic lung diseases. In current systems, manually continuous adjustment of O2 flow rate is a time-consuming task, often unsuccessful, that requires experienced staff. The primary aim of this systematic review is to collate and report on the principles, algorithms and accuracy of autonomous physiological close-loop controlled oxygen devices as well to present recommendations for future research and studies in this area. METHODS A literature search was performed on medical database MEDLINE, engineering database IEEE-Xplore and wide-raging scientific databases Scopus and Web of Science. A narrative synthesis of the results was carried out. RESULTS A summary of the findings of this review suggests that when compared to the conventional manual practice, the closed-loop controllers maintain higher saturation levels, spend less time below the target saturation, and save oxygen resources. Nonetheless, despite of their potential, autonomous oxygen therapy devices are scarce in real clinical applications. CONCLUSIONS Robustness of control algorithms, fail-safe mechanisms, limited reliability of sensors, usability issues and the need for standardized evaluating methods of assessing risks can be among the reasons for this lack of matureness and need to be addressed before the wide spreading of a new generation of automatic oxygen devices.

A Machine Learning Approach to Prediction of Exacerbations of Chronic Obstructive Pulmonary Disease

Chronic Obstructive Pulmonary Disease (COPD) places an enormous burden on the health care systems and causes diminished health related quality of life. The highest proportion of human and economic cost is associated to admissions for acute exacerbation of respiratory symptoms. The remote monitoring of COPD patients with the view of early detection of acute exacerbation of COPD (AECOPD) is one of the goals of the respiratory community. In this study, machine learning was used to develop predictive models. Models robustness to exacerbation definition was analyzed. A non-knowled-ge based approach was followed on data self-reported by patients using a multimodal tool during a remote monitoring 6 months trial. Comparison of different classifier algorithms operating with different AECOPD definitions was performed. Significant results were obtained for AECOPD prediction, regardless of the definition of exacerbation used. Best accuracy was achieved using a PNN classifier independently of the selected AECOPD definition. Our study suggests that the proposed data-driven methodology could help to design reliable predictive algorithms aimed to predict COPD exacerbations and therefore could provide support both to physicians and patients.

Development and Evaluation of an Automated, Home-Based, Electronic Questionnaire for Detecting COPD Exacerbations.

Collaboration between patients and their medical and technical experts enabled the development of an automated questionnaire for the early detection of COPD exacerbations (AQCE). The questionnaire consisted of fourteen questions and was implemented on a computer system for use by patients at home in an un-supervised environment. Psychometric evaluation was conducted after a 6-month field trial. Fifty-two patients were involved in the development of the questionnaire. Reproducibility was studied using 19 patients (ICC = 0.94). Sixteen out of the 19 subjects started the 6 month-field trial with the computer application. Cronbachs alpha of 0.81 was achieved. In the concurrent validity analysis, a correlation of 0.80 (p = 0.002) with the CCQ was reported. The results suggest that AQCE is a valid and reliable questionnaire, showing that an automated home-based electronic questionnaire may enable early detection of exacerbations of COPD.