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.

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.

Detecting COPD exacerbations early using daily telemonitoring of symptoms and k-means clustering: a pilot study.

COPD places an enormous burden on the healthcare systems and causes diminished health-related quality of life. The highest proportion of human and economic cost is associated with admissions for acute exacerbation of respiratory symptoms (AECOPD). Since prompt detection and treatment of exacerbations may improve outcomes, early detection of AECOPD is a critical issue. This pilot study was aimed to determine whether a mobile health system could enable early detection of AECOPD on a day-to-day basis. A novel electronic questionnaire for the early detection of COPD exacerbations was evaluated during a 6-months field trial in a group of 16 patients. Pattern recognition techniques were applied. A k-means clustering algorithm was trained and validated, and its accuracy in detecting AECOPD was assessed. Sensitivity and specificity were 74.6 and 89.7 %, respectively, and area under the receiver operating characteristic curve was 0.84. 31 out of 33 AECOPD were early identified with an average of 4.5 ± 2.1 days prior to the onset of the exacerbation that was considered the day of medical attendance. Based on the findings of this preliminary pilot study, the proposed electronic questionnaire and the applied methodology could help to early detect COPD exacerbations on a day-to-day basis and therefore could provide support to patients and physicians.

Computerized analysis of respiratory sounds during COPD exacerbations.

Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a major event in the natural course of the disease, and is associated with significant mortality and socioeconomic impact. Abnormal respiratory sounds are commonly present in patients with AECOPD. Computerized analysis of these sounds can assist in diagnosis and in evaluation during follow-up. Exploratory data analysis methods were applied to respiratory sounds in these patients when they were hospitalized because of exacerbation. Two different patterns of presentation and evolution of respiratory sounds in AECOPD were found and described from the method of computerized respiratory sound analysis and unsupervised clustering that was devised. Based on the findings of the study, remote monitoring of respiratory sounds may be useful for the detection and/or follow-up of COPD exacerbation.