Chronic Obstructive Pulmonary Disease (COPD) is a progressive chronic disease, predicted to become the third leading cause of death by 2030. COPD patients are at risk of sudden and acute worsening of symptoms, reducing the patient's quality of life and leading to hospitalization. We present the results of a pilot study with 18 COPD patients using an m-Health system, based on a tablet computer and pulse oximeter, for a period of six months. For prioritizing patients for clinical review, a data-driven approach has been developed which generates personalized alerts using the electronic symptom diary, pulse rate, blood oxygen saturation, and respiratory rate derived from oximetry data. This work examines the advantages of multivariate novelty detection over univariate approaches and shows the benefit of including respiratory rate as a predictor.

Original publication

DOI

10.1109/EMBC.2014.6944294

Type

Conference paper

Publication Date

2014

Volume

2014

Pages

3164 - 3167

Keywords

Aged, Algorithms, Area Under Curve, Chronic Disease, Clinical Alarms, Computers, Handheld, Female, Heart Rate, Hospitalization, Humans, Male, Middle Aged, Multivariate Analysis, Oximetry, Oxygen, Pilot Projects, Pulmonary Disease, Chronic Obstructive, Quality of Life, Respiratory Rate, Retrospective Studies, Signal Processing, Computer-Assisted, Software, User-Computer Interface